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Weiye Loh

The Inequality That Matters - Tyler Cowen - The American Interest Magazine - 0 views

www.the-american-interest.com/article-bd.cfm

Income Inequality Social Divide Economy

shared by Weiye Loh on 16 Dec 10 - No Cached
  • most of the worries about income inequality are bogus, but some are probably better grounded and even more serious than even many of their heralds realize.
  • In terms of immediate political stability, there is less to the income inequality issue than meets the eye. Most analyses of income inequality neglect two major points. First, the inequality of personal well-being is sharply down over the past hundred years and perhaps over the past twenty years as well. Bill Gates is much, much richer than I am, yet it is not obvious that he is much happier if, indeed, he is happier at all. I have access to penicillin, air travel, good cheap food, the Internet and virtually all of the technical innovations that Gates does. Like the vast majority of Americans, I have access to some important new pharmaceuticals, such as statins to protect against heart disease. To be sure, Gates receives the very best care from the world’s top doctors, but our health outcomes are in the same ballpark. I don’t have a private jet or take luxury vacations, and—I think it is fair to say—my house is much smaller than his. I can’t meet with the world’s elite on demand. Still, by broad historical standards, what I share with Bill Gates is far more significant than what I don’t share with him.
  • when average people read about or see income inequality, they don’t feel the moral outrage that radiates from the more passionate egalitarian quarters of society. Instead, they think their lives are pretty good and that they either earned through hard work or lucked into a healthy share of the American dream.
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  • This is why, for example, large numbers of Americans oppose the idea of an estate tax even though the current form of the tax, slated to return in 2011, is very unlikely to affect them or their estates. In narrowly self-interested terms, that view may be irrational, but most Americans are unwilling to frame national issues in terms of rich versus poor. There’s a great deal of hostility toward various government bailouts, but the idea of “undeserving” recipients is the key factor in those feelings. Resentment against Wall Street gamesters hasn’t spilled over much into resentment against the wealthy more generally. The bailout for General Motors’ labor unions wasn’t so popular either—again, obviously not because of any bias against the wealthy but because a basic sense of fairness was violated. As of November 2010, congressional Democrats are of a mixed mind as to whether the Bush tax cuts should expire for those whose annual income exceeds $250,000; that is in large part because their constituents bear no animus toward rich people, only toward undeservedly rich people.
  • envy is usually local. At least in the United States, most economic resentment is not directed toward billionaires or high-roller financiers—not even corrupt ones. It’s directed at the guy down the hall who got a bigger raise. It’s directed at the husband of your wife’s sister, because the brand of beer he stocks costs $3 a case more than yours, and so on. That’s another reason why a lot of people aren’t so bothered by income or wealth inequality at the macro level. Most of us don’t compare ourselves to billionaires. Gore Vidal put it honestly: “Whenever a friend succeeds, a little something in me dies.”
  • Occasionally the cynic in me wonders why so many relatively well-off intellectuals lead the egalitarian charge against the privileges of the wealthy. One group has the status currency of money and the other has the status currency of intellect, so might they be competing for overall social regard? The high status of the wealthy in America, or for that matter the high status of celebrities, seems to bother our intellectual class most. That class composes a very small group, however, so the upshot is that growing income inequality won’t necessarily have major political implications at the macro level.
  • All that said, income inequality does matter—for both politics and the economy.
  • The numbers are clear: Income inequality has been rising in the United States, especially at the very top. The data show a big difference between two quite separate issues, namely income growth at the very top of the distribution and greater inequality throughout the distribution. The first trend is much more pronounced than the second, although the two are often confused.
  • When it comes to the first trend, the share of pre-tax income earned by the richest 1 percent of earners has increased from about 8 percent in 1974 to more than 18 percent in 2007. Furthermore, the richest 0.01 percent (the 15,000 or so richest families) had a share of less than 1 percent in 1974 but more than 6 percent of national income in 2007. As noted, those figures are from pre-tax income, so don’t look to the George W. Bush tax cuts to explain the pattern. Furthermore, these gains have been sustained and have evolved over many years, rather than coming in one or two small bursts between 1974 and today.1
  • At the same time, wage growth for the median earner has slowed since 1973. But that slower wage growth has afflicted large numbers of Americans, and it is conceptually distinct from the higher relative share of top income earners. For instance, if you take the 1979–2005 period, the average incomes of the bottom fifth of households increased only 6 percent while the incomes of the middle quintile rose by 21 percent. That’s a widening of the spread of incomes, but it’s not so drastic compared to the explosive gains at the very top.
  • The broader change in income distribution, the one occurring beneath the very top earners, can be deconstructed in a manner that makes nearly all of it look harmless. For instance, there is usually greater inequality of income among both older people and the more highly educated, if only because there is more time and more room for fortunes to vary. Since America is becoming both older and more highly educated, our measured income inequality will increase pretty much by demographic fiat. Economist Thomas Lemieux at the University of British Columbia estimates that these demographic effects explain three-quarters of the observed rise in income inequality for men, and even more for women.2
  • Attacking the problem from a different angle, other economists are challenging whether there is much growth in inequality at all below the super-rich. For instance, real incomes are measured using a common price index, yet poorer people are more likely to shop at discount outlets like Wal-Mart, which have seen big price drops over the past twenty years.3 Once we take this behavior into account, it is unclear whether the real income gaps between the poor and middle class have been widening much at all. Robert J. Gordon, an economist from Northwestern University who is hardly known as a right-wing apologist, wrote in a recent paper that “there was no increase of inequality after 1993 in the bottom 99 percent of the population”, and that whatever overall change there was “can be entirely explained by the behavior of income in the top 1 percent.”4
  • And so we come again to the gains of the top earners, clearly the big story told by the data. It’s worth noting that over this same period of time, inequality of work hours increased too. The top earners worked a lot more and most other Americans worked somewhat less. That’s another reason why high earners don’t occasion more resentment: Many people understand how hard they have to work to get there. It also seems that most of the income gains of the top earners were related to performance pay—bonuses, in other words—and not wildly out-of-whack yearly salaries.5
  • It is also the case that any society with a lot of “threshold earners” is likely to experience growing income inequality. A threshold earner is someone who seeks to earn a certain amount of money and no more. If wages go up, that person will respond by seeking less work or by working less hard or less often. That person simply wants to “get by” in terms of absolute earning power in order to experience other gains in the form of leisure—whether spending time with friends and family, walking in the woods and so on. Luck aside, that person’s income will never rise much above the threshold.
  • The funny thing is this: For years, many cultural critics in and of the United States have been telling us that Americans should behave more like threshold earners. We should be less harried, more interested in nurturing friendships, and more interested in the non-commercial sphere of life. That may well be good advice. Many studies suggest that above a certain level more money brings only marginal increments of happiness. What isn’t so widely advertised is that those same critics have basically been telling us, without realizing it, that we should be acting in such a manner as to increase measured income inequality. Not only is high inequality an inevitable concomitant of human diversity, but growing income inequality may be, too, if lots of us take the kind of advice that will make us happier.
  • Why is the top 1 percent doing so well?
  • Steven N. Kaplan and Joshua Rauh have recently provided a detailed estimation of particular American incomes.6 Their data do not comprise the entire U.S. population, but from partial financial records they find a very strong role for the financial sector in driving the trend toward income concentration at the top. For instance, for 2004, nonfinancial executives of publicly traded companies accounted for less than 6 percent of the top 0.01 percent income bracket. In that same year, the top 25 hedge fund managers combined appear to have earned more than all of the CEOs from the entire S&P 500. The number of Wall Street investors earning more than $100 million a year was nine times higher than the public company executives earning that amount. The authors also relate that they shared their estimates with a former U.S. Secretary of the Treasury, one who also has a Wall Street background. He thought their estimates of earnings in the financial sector were, if anything, understated.
  • Many of the other high earners are also connected to finance. After Wall Street, Kaplan and Rauh identify the legal sector as a contributor to the growing spread in earnings at the top. Yet many high-earning lawyers are doing financial deals, so a lot of the income generated through legal activity is rooted in finance. Other lawyers are defending corporations against lawsuits, filing lawsuits or helping corporations deal with complex regulations. The returns to these activities are an artifact of the growing complexity of the law and government growth rather than a tale of markets per se. Finance aside, there isn’t much of a story of market failure here, even if we don’t find the results aesthetically appealing.
  • When it comes to professional athletes and celebrities, there isn’t much of a mystery as to what has happened. Tiger Woods earns much more, even adjusting for inflation, than Arnold Palmer ever did. J.K. Rowling, the first billionaire author, earns much more than did Charles Dickens. These high incomes come, on balance, from the greater reach of modern communications and marketing. Kids all over the world read about Harry Potter. There is more purchasing power to spend on children’s books and, indeed, on culture and celebrities more generally. For high-earning celebrities, hardly anyone finds these earnings so morally objectionable as to suggest that they be politically actionable. Cultural critics can complain that good schoolteachers earn too little, and they may be right, but that does not make celebrities into political targets. They’re too popular. It’s also pretty clear that most of them work hard to earn their money, by persuading fans to buy or otherwise support their product. Most of these individuals do not come from elite or extremely privileged backgrounds, either. They worked their way to the top, and even if Rowling is not an author for the ages, her books tapped into the spirit of their time in a special way. We may or may not wish to tax the wealthy, including wealthy celebrities, at higher rates, but there is no need to “cure” the structural causes of higher celebrity incomes.
  • to be sure, the high incomes in finance should give us all pause.
  • The first factor driving high returns is sometimes called by practitioners “going short on volatility.” Sometimes it is called “negative skewness.” In plain English, this means that some investors opt for a strategy of betting against big, unexpected moves in market prices. Most of the time investors will do well by this strategy, since big, unexpected moves are outliers by definition. Traders will earn above-average returns in good times. In bad times they won’t suffer fully when catastrophic returns come in, as sooner or later is bound to happen, because the downside of these bets is partly socialized onto the Treasury, the Federal Reserve and, of course, the taxpayers and the unemployed.
  • if you bet against unlikely events, most of the time you will look smart and have the money to validate the appearance. Periodically, however, you will look very bad. Does that kind of pattern sound familiar? It happens in finance, too. Betting against a big decline in home prices is analogous to betting against the Wizards. Every now and then such a bet will blow up in your face, though in most years that trading activity will generate above-average profits and big bonuses for the traders and CEOs.
  • To this mix we can add the fact that many money managers are investing other people’s money. If you plan to stay with an investment bank for ten years or less, most of the people playing this investing strategy will make out very well most of the time. Everyone’s time horizon is a bit limited and you will bring in some nice years of extra returns and reap nice bonuses. And let’s say the whole thing does blow up in your face? What’s the worst that can happen? Your bosses fire you, but you will still have millions in the bank and that MBA from Harvard or Wharton. For the people actually investing the money, there’s barely any downside risk other than having to quit the party early. Furthermore, if everyone else made more or less the same mistake (very surprising major events, such as a busted housing market, affect virtually everybody), you’re hardly disgraced. You might even get rehired at another investment bank, or maybe a hedge fund, within months or even weeks.
  • Moreover, smart shareholders will acquiesce to or even encourage these gambles. They gain on the upside, while the downside, past the point of bankruptcy, is borne by the firm’s creditors. And will the bondholders object? Well, they might have a difficult time monitoring the internal trading operations of financial institutions. Of course, the firm’s trading book cannot be open to competitors, and that means it cannot be open to bondholders (or even most shareholders) either. So what, exactly, will they have in hand to object to?
  • Perhaps more important, government bailouts minimize the damage to creditors on the downside. Neither the Treasury nor the Fed allowed creditors to take any losses from the collapse of the major banks during the financial crisis. The U.S. government guaranteed these loans, either explicitly or implicitly. Guaranteeing the debt also encourages equity holders to take more risk. While current bailouts have not in general maintained equity values, and while share prices have often fallen to near zero following the bust of a major bank, the bailouts still give the bank a lifeline. Instead of the bank being destroyed, sometimes those equity prices do climb back out of the hole. This is true of the major surviving banks in the United States, and even AIG is paying back its bailout. For better or worse, we’re handing out free options on recovery, and that encourages banks to take more risk in the first place.
  • there is an unholy dynamic of short-term trading and investing, backed up by bailouts and risk reduction from the government and the Federal Reserve. This is not good. “Going short on volatility” is a dangerous strategy from a social point of view. For one thing, in so-called normal times, the finance sector attracts a big chunk of the smartest, most hard-working and most talented individuals. That represents a huge human capital opportunity cost to society and the economy at large. But more immediate and more important, it means that banks take far too many risks and go way out on a limb, often in correlated fashion. When their bets turn sour, as they did in 2007–09, everyone else pays the price.
  • And it’s not just the taxpayer cost of the bailout that stings. The financial disruption ends up throwing a lot of people out of work down the economic food chain, often for long periods. Furthermore, the Federal Reserve System has recapitalized major U.S. banks by paying interest on bank reserves and by keeping an unusually high interest rate spread, which allows banks to borrow short from Treasury at near-zero rates and invest in other higher-yielding assets and earn back lots of money rather quickly. In essence, we’re allowing banks to earn their way back by arbitraging interest rate spreads against the U.S. government. This is rarely called a bailout and it doesn’t count as a normal budget item, but it is a bailout nonetheless. This type of implicit bailout brings high social costs by slowing down economic recovery (the interest rate spreads require tight monetary policy) and by redistributing income from the Treasury to the major banks.
  • the “going short on volatility” strategy increases income inequality. In normal years the financial sector is flush with cash and high earnings. In implosion years a lot of the losses are borne by other sectors of society. In other words, financial crisis begets income inequality. Despite being conceptually distinct phenomena, the political economy of income inequality is, in part, the political economy of finance. Simon Johnson tabulates the numbers nicely: From 1973 to 1985, the financial sector never earned more than 16 percent of domestic corporate profits. In 1986, that figure reached 19 percent. In the 1990s, it oscillated between 21 percent and 30 percent, higher than it had ever been in the postwar period. This decade, it reached 41 percent. Pay rose just as dramatically. From 1948 to 1982, average compensation in the financial sector ranged between 99 percent and 108 percent of the average for all domestic private industries. From 1983, it shot upward, reaching 181 percent in 2007.7
  • There’s a second reason why the financial sector abets income inequality: the “moving first” issue. Let’s say that some news hits the market and that traders interpret this news at different speeds. One trader figures out what the news means in a second, while the other traders require five seconds. Still other traders require an entire day or maybe even a month to figure things out. The early traders earn the extra money. They buy the proper assets early, at the lower prices, and reap most of the gains when the other, later traders pile on. Similarly, if you buy into a successful tech company in the early stages, you are “moving first” in a very effective manner, and you will capture most of the gains if that company hits it big.
  • The moving-first phenomenon sums to a “winner-take-all” market. Only some relatively small number of traders, sometimes just one trader, can be first. Those who are first will make far more than those who are fourth or fifth. This difference will persist, even if those who are fourth come pretty close to competing with those who are first. In this context, first is first and it doesn’t matter much whether those who come in fourth pile on a month, a minute or a fraction of a second later. Those who bought (or sold, as the case may be) first have captured and locked in most of the available gains. Since gains are concentrated among the early winners, and the closeness of the runner-ups doesn’t so much matter for income distribution, asset-market trading thus encourages the ongoing concentration of wealth. Many investors make lots of mistakes and lose their money, but each year brings a new bunch of projects that can turn the early investors and traders into very wealthy individuals.
  • These two features of the problem—“going short on volatility” and “getting there first”—are related. Let’s say that Goldman Sachs regularly secures a lot of the best and quickest trades, whether because of its quality analysis, inside connections or high-frequency trading apparatus (it has all three). It builds up a treasure chest of profits and continues to hire very sharp traders and to receive valuable information. Those profits allow it to make “short on volatility” bets faster than anyone else, because if it messes up, it still has a large enough buffer to pad losses. This increases the odds that Goldman will repeatedly pull in spectacular profits.
  • Still, every now and then Goldman will go bust, or would go bust if not for government bailouts. But the odds are in any given year that it won’t because of the advantages it and other big banks have. It’s as if the major banks have tapped a hole in the social till and they are drinking from it with a straw. In any given year, this practice may seem tolerable—didn’t the bank earn the money fair and square by a series of fairly normal looking trades? Yet over time this situation will corrode productivity, because what the banks do bears almost no resemblance to a process of getting capital into the hands of those who can make most efficient use of it. And it leads to periodic financial explosions. That, in short, is the real problem of income inequality we face today. It’s what causes the inequality at the very top of the earning pyramid that has dangerous implications for the economy as a whole.
  • What about controlling bank risk-taking directly with tight government oversight? That is not practical. There are more ways for banks to take risks than even knowledgeable regulators can possibly control; it just isn’t that easy to oversee a balance sheet with hundreds of billions of dollars on it, especially when short-term positions are wound down before quarterly inspections. It’s also not clear how well regulators can identify risky assets. Some of the worst excesses of the financial crisis were grounded in mortgage-backed assets—a very traditional function of banks—not exotic derivatives trading strategies. Virtually any asset position can be used to bet long odds, one way or another. It is naive to think that underpaid, undertrained regulators can keep up with financial traders, especially when the latter stand to earn billions by circumventing the intent of regulations while remaining within the letter of the law.
  • For the time being, we need to accept the possibility that the financial sector has learned how to game the American (and UK-based) system of state capitalism. It’s no longer obvious that the system is stable at a macro level, and extreme income inequality at the top has been one result of that imbalance. Income inequality is a symptom, however, rather than a cause of the real problem. The root cause of income inequality, viewed in the most general terms, is extreme human ingenuity, albeit of a perverse kind. That is why it is so hard to control.
  • Another root cause of growing inequality is that the modern world, by so limiting our downside risk, makes extreme risk-taking all too comfortable and easy. More risk-taking will mean more inequality, sooner or later, because winners always emerge from risk-taking. Yet bankers who take bad risks (provided those risks are legal) simply do not end up with bad outcomes in any absolute sense. They still have millions in the bank, lots of human capital and plenty of social status. We’re not going to bring back torture, trial by ordeal or debtors’ prisons, nor should we. Yet the threat of impoverishment and disgrace no longer looms the way it once did, so we no longer can constrain excess financial risk-taking. It’s too soft and cushy a world.
  • Why don’t we simply eliminate the safety net for clueless or unlucky risk-takers so that losses equal gains overall? That’s a good idea in principle, but it is hard to put into practice. Once a financial crisis arrives, politicians will seek to limit the damage, and that means they will bail out major financial institutions. Had we not passed TARP and related policies, the United States probably would have faced unemployment rates of 25 percent of higher, as in the Great Depression. The political consequences would not have been pretty. Bank bailouts may sound quite interventionist, and indeed they are, but in relative terms they probably were the most libertarian policy we had on tap. It meant big one-time expenses, but, for the most part, it kept government out of the real economy (the General Motors bailout aside).
  • We probably don’t have any solution to the hazards created by our financial sector, not because plutocrats are preventing our political system from adopting appropriate remedies, but because we don’t know what those remedies are. Yet neither is another crisis immediately upon us. The underlying dynamic favors excess risk-taking, but banks at the current moment fear the scrutiny of regulators and the public and so are playing it fairly safe. They are sitting on money rather than lending it out. The biggest risk today is how few parties will take risks, and, in part, the caution of banks is driving our current protracted economic slowdown. According to this view, the long run will bring another financial crisis once moods pick up and external scrutiny weakens, but that day of reckoning is still some ways off.
  • Is the overall picture a shame? Yes. Is it distorting resource distribution and productivity in the meantime? Yes. Will it again bring our economy to its knees? Probably. Maybe that’s simply the price of modern society. Income inequality will likely continue to rise and we will search in vain for the appropriate political remedies for our underlying problems.
Weiye Loh

How We Know by Freeman Dyson | The New York Review of Books - 0 views

www.nybooks.com/...how-we-know

Information Language Data History Science

shared by Weiye Loh on 27 Feb 11 - No Cached
  • Another example illustrating the central dogma is the French optical telegraph.
  • The telegraph was an optical communication system with stations consisting of large movable pointers mounted on the tops of sixty-foot towers. Each station was manned by an operator who could read a message transmitted by a neighboring station and transmit the same message to the next station in the transmission line.
  • The distance between neighbors was about seven miles. Along the transmission lines, optical messages in France could travel faster than drum messages in Africa. When Napoleon took charge of the French Republic in 1799, he ordered the completion of the optical telegraph system to link all the major cities of France from Calais and Paris to Toulon and onward to Milan. The telegraph became, as Claude Chappe had intended, an important instrument of national power. Napoleon made sure that it was not available to private users.
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  • Unlike the drum language, which was based on spoken language, the optical telegraph was based on written French. Chappe invented an elaborate coding system to translate written messages into optical signals. Chappe had the opposite problem from the drummers. The drummers had a fast transmission system with ambiguous messages. They needed to slow down the transmission to make the messages unambiguous. Chappe had a painfully slow transmission system with redundant messages. The French language, like most alphabetic languages, is highly redundant, using many more letters than are needed to convey the meaning of a message. Chappe’s coding system allowed messages to be transmitted faster. Many common phrases and proper names were encoded by only two optical symbols, with a substantial gain in speed of transmission. The composer and the reader of the message had code books listing the message codes for eight thousand phrases and names. For Napoleon it was an advantage to have a code that was effectively cryptographic, keeping the content of the messages secret from citizens along the route.
  • After these two historical examples of rapid communication in Africa and France, the rest of Gleick’s book is about the modern development of information technolog
  • The modern history is dominated by two Americans, Samuel Morse and Claude Shannon. Samuel Morse was the inventor of Morse Code. He was also one of the pioneers who built a telegraph system using electricity conducted through wires instead of optical pointers deployed on towers. Morse launched his electric telegraph in 1838 and perfected the code in 1844. His code used short and long pulses of electric current to represent letters of the alphabet.
  • Morse was ideologically at the opposite pole from Chappe. He was not interested in secrecy or in creating an instrument of government power. The Morse system was designed to be a profit-making enterprise, fast and cheap and available to everybody. At the beginning the price of a message was a quarter of a cent per letter. The most important users of the system were newspaper correspondents spreading news of local events to readers all over the world. Morse Code was simple enough that anyone could learn it. The system provided no secrecy to the users. If users wanted secrecy, they could invent their own secret codes and encipher their messages themselves. The price of a message in cipher was higher than the price of a message in plain text, because the telegraph operators could transcribe plain text faster. It was much easier to correct errors in plain text than in cipher.
  • Claude Shannon was the founding father of information theory. For a hundred years after the electric telegraph, other communication systems such as the telephone, radio, and television were invented and developed by engineers without any need for higher mathematics. Then Shannon supplied the theory to understand all of these systems together, defining information as an abstract quantity inherent in a telephone message or a television picture. Shannon brought higher mathematics into the game.
  • When Shannon was a boy growing up on a farm in Michigan, he built a homemade telegraph system using Morse Code. Messages were transmitted to friends on neighboring farms, using the barbed wire of their fences to conduct electric signals. When World War II began, Shannon became one of the pioneers of scientific cryptography, working on the high-level cryptographic telephone system that allowed Roosevelt and Churchill to talk to each other over a secure channel. Shannon’s friend Alan Turing was also working as a cryptographer at the same time, in the famous British Enigma project that successfully deciphered German military codes. The two pioneers met frequently when Turing visited New York in 1943, but they belonged to separate secret worlds and could not exchange ideas about cryptography.
  • In 1945 Shannon wrote a paper, “A Mathematical Theory of Cryptography,” which was stamped SECRET and never saw the light of day. He published in 1948 an expurgated version of the 1945 paper with the title “A Mathematical Theory of Communication.” The 1948 version appeared in the Bell System Technical Journal, the house journal of the Bell Telephone Laboratories, and became an instant classic. It is the founding document for the modern science of information. After Shannon, the technology of information raced ahead, with electronic computers, digital cameras, the Internet, and the World Wide Web.
  • According to Gleick, the impact of information on human affairs came in three installments: first the history, the thousands of years during which people created and exchanged information without the concept of measuring it; second the theory, first formulated by Shannon; third the flood, in which we now live
  • The event that made the flood plainly visible occurred in 1965, when Gordon Moore stated Moore’s Law. Moore was an electrical engineer, founder of the Intel Corporation, a company that manufactured components for computers and other electronic gadgets. His law said that the price of electronic components would decrease and their numbers would increase by a factor of two every eighteen months. This implied that the price would decrease and the numbers would increase by a factor of a hundred every decade. Moore’s prediction of continued growth has turned out to be astonishingly accurate during the forty-five years since he announced it. In these four and a half decades, the price has decreased and the numbers have increased by a factor of a billion, nine powers of ten. Nine powers of ten are enough to turn a trickle into a flood.
  • Gordon Moore was in the hardware business, making hardware components for electronic machines, and he stated his law as a law of growth for hardware. But the law applies also to the information that the hardware is designed to embody. The purpose of the hardware is to store and process information. The storage of information is called memory, and the processing of information is called computing. The consequence of Moore’s Law for information is that the price of memory and computing decreases and the available amount of memory and computing increases by a factor of a hundred every decade. The flood of hardware becomes a flood of information.
  • In 1949, one year after Shannon published the rules of information theory, he drew up a table of the various stores of memory that then existed. The biggest memory in his table was the US Library of Congress, which he estimated to contain one hundred trillion bits of information. That was at the time a fair guess at the sum total of recorded human knowledge. Today a memory disc drive storing that amount of information weighs a few pounds and can be bought for about a thousand dollars. Information, otherwise known as data, pours into memories of that size or larger, in government and business offices and scientific laboratories all over the world. Gleick quotes the computer scientist Jaron Lanier describing the effect of the flood: “It’s as if you kneel to plant the seed of a tree and it grows so fast that it swallows your whole town before you can even rise to your feet.”
  • On December 8, 2010, Gleick published on the The New York Review’s blog an illuminating essay, “The Information Palace.” It was written too late to be included in his book. It describes the historical changes of meaning of the word “information,” as recorded in the latest quarterly online revision of the Oxford English Dictionary. The word first appears in 1386 a parliamentary report with the meaning “denunciation.” The history ends with the modern usage, “information fatigue,” defined as “apathy, indifference or mental exhaustion arising from exposure to too much information.”
  • The consequences of the information flood are not all bad. One of the creative enterprises made possible by the flood is Wikipedia, started ten years ago by Jimmy Wales. Among my friends and acquaintances, everybody distrusts Wikipedia and everybody uses it. Distrust and productive use are not incompatible. Wikipedia is the ultimate open source repository of information. Everyone is free to read it and everyone is free to write it. It contains articles in 262 languages written by several million authors. The information that it contains is totally unreliable and surprisingly accurate. It is often unreliable because many of the authors are ignorant or careless. It is often accurate because the articles are edited and corrected by readers who are better informed than the authors
  • Jimmy Wales hoped when he started Wikipedia that the combination of enthusiastic volunteer writers with open source information technology would cause a revolution in human access to knowledge. The rate of growth of Wikipedia exceeded his wildest dreams. Within ten years it has become the biggest storehouse of information on the planet and the noisiest battleground of conflicting opinions. It illustrates Shannon’s law of reliable communication. Shannon’s law says that accurate transmission of information is possible in a communication system with a high level of noise. Even in the noisiest system, errors can be reliably corrected and accurate information transmitted, provided that the transmission is sufficiently redundant. That is, in a nutshell, how Wikipedia works.
  • The information flood has also brought enormous benefits to science. The public has a distorted view of science, because children are taught in school that science is a collection of firmly established truths. In fact, science is not a collection of truths. It is a continuing exploration of mysteries. Wherever we go exploring in the world around us, we find mysteries. Our planet is covered by continents and oceans whose origin we cannot explain. Our atmosphere is constantly stirred by poorly understood disturbances that we call weather and climate. The visible matter in the universe is outweighed by a much larger quantity of dark invisible matter that we do not understand at all. The origin of life is a total mystery, and so is the existence of human consciousness. We have no clear idea how the electrical discharges occurring in nerve cells in our brains are connected with our feelings and desires and actions.
  • Even physics, the most exact and most firmly established branch of science, is still full of mysteries. We do not know how much of Shannon’s theory of information will remain valid when quantum devices replace classical electric circuits as the carriers of information. Quantum devices may be made of single atoms or microscopic magnetic circuits. All that we know for sure is that they can theoretically do certain jobs that are beyond the reach of classical devices. Quantum computing is still an unexplored mystery on the frontier of information theory. Science is the sum total of a great multitude of mysteries. It is an unending argument between a great multitude of voices. It resembles Wikipedia much more than it resembles the Encyclopaedia Britannica.
  • The rapid growth of the flood of information in the last ten years made Wikipedia possible, and the same flood made twenty-first-century science possible. Twenty-first-century science is dominated by huge stores of information that we call databases. The information flood has made it easy and cheap to build databases. One example of a twenty-first-century database is the collection of genome sequences of living creatures belonging to various species from microbes to humans. Each genome contains the complete genetic information that shaped the creature to which it belongs. The genome data-base is rapidly growing and is available for scientists all over the world to explore. Its origin can be traced to the year 1939, when Shannon wrote his Ph.D. thesis with the title “An Algebra for Theoretical Genetics.
  • Shannon was then a graduate student in the mathematics department at MIT. He was only dimly aware of the possible physical embodiment of genetic information. The true physical embodiment of the genome is the double helix structure of DNA molecules, discovered by Francis Crick and James Watson fourteen years later. In 1939 Shannon understood that the basis of genetics must be information, and that the information must be coded in some abstract algebra independent of its physical embodiment. Without any knowledge of the double helix, he could not hope to guess the detailed structure of the genetic code. He could only imagine that in some distant future the genetic information would be decoded and collected in a giant database that would define the total diversity of living creatures. It took only sixty years for his dream to come true.
  • In the twentieth century, genomes of humans and other species were laboriously decoded and translated into sequences of letters in computer memories. The decoding and translation became cheaper and faster as time went on, the price decreasing and the speed increasing according to Moore’s Law. The first human genome took fifteen years to decode and cost about a billion dollars. Now a human genome can be decoded in a few weeks and costs a few thousand dollars. Around the year 2000, a turning point was reached, when it became cheaper to produce genetic information than to understand it. Now we can pass a piece of human DNA through a machine and rapidly read out the genetic information, but we cannot read out the meaning of the information. We shall not fully understand the information until we understand in detail the processes of embryonic development that the DNA orchestrated to make us what we are.
  • The explosive growth of information in our human society is a part of the slower growth of ordered structures in the evolution of life as a whole. Life has for billions of years been evolving with organisms and ecosystems embodying increasing amounts of information. The evolution of life is a part of the evolution of the universe, which also evolves with increasing amounts of information embodied in ordered structures, galaxies and stars and planetary systems. In the living and in the nonliving world, we see a growth of order, starting from the featureless and uniform gas of the early universe and producing the magnificent diversity of weird objects that we see in the sky and in the rain forest. Everywhere around us, wherever we look, we see evidence of increasing order and increasing information. The technology arising from Shannon’s discoveries is only a local acceleration of the natural growth of information.
  • . Lord Kelvin, one of the leading physicists of that time, promoted the heat death dogma, predicting that the flow of heat from warmer to cooler objects will result in a decrease of temperature differences everywhere, until all temperatures ultimately become equal. Life needs temperature differences, to avoid being stifled by its waste heat. So life will disappear
  • Thanks to the discoveries of astronomers in the twentieth century, we now know that the heat death is a myth. The heat death can never happen, and there is no paradox. The best popular account of the disappearance of the paradox is a chapter, “How Order Was Born of Chaos,” in the book Creation of the Universe, by Fang Lizhi and his wife Li Shuxian.2 Fang Lizhi is doubly famous as a leading Chinese astronomer and a leading political dissident. He is now pursuing his double career at the University of Arizona.
  • The belief in a heat death was based on an idea that I call the cooking rule. The cooking rule says that a piece of steak gets warmer when we put it on a hot grill. More generally, the rule says that any object gets warmer when it gains energy, and gets cooler when it loses energy. Humans have been cooking steaks for thousands of years, and nobody ever saw a steak get colder while cooking on a fire. The cooking rule is true for objects small enough for us to handle. If the cooking rule is always true, then Lord Kelvin’s argument for the heat death is correct.
  • the cooking rule is not true for objects of astronomical size, for which gravitation is the dominant form of energy. The sun is a familiar example. As the sun loses energy by radiation, it becomes hotter and not cooler. Since the sun is made of compressible gas squeezed by its own gravitation, loss of energy causes it to become smaller and denser, and the compression causes it to become hotter. For almost all astronomical objects, gravitation dominates, and they have the same unexpected behavior. Gravitation reverses the usual relation between energy and temperature. In the domain of astronomy, when heat flows from hotter to cooler objects, the hot objects get hotter and the cool objects get cooler. As a result, temperature differences in the astronomical universe tend to increase rather than decrease as time goes on. There is no final state of uniform temperature, and there is no heat death. Gravitation gives us a universe hospitable to life. Information and order can continue to grow for billions of years in the future, as they have evidently grown in the past.
  • The vision of the future as an infinite playground, with an unending sequence of mysteries to be understood by an unending sequence of players exploring an unending supply of information, is a glorious vision for scientists. Scientists find the vision attractive, since it gives them a purpose for their existence and an unending supply of jobs. The vision is less attractive to artists and writers and ordinary people. Ordinary people are more interested in friends and family than in science. Ordinary people may not welcome a future spent swimming in an unending flood of information.
  • A darker view of the information-dominated universe was described in a famous story, “The Library of Babel,” by Jorge Luis Borges in 1941.3 Borges imagined his library, with an infinite array of books and shelves and mirrors, as a metaphor for the universe.
  • Gleick’s book has an epilogue entitled “The Return of Meaning,” expressing the concerns of people who feel alienated from the prevailing scientific culture. The enormous success of information theory came from Shannon’s decision to separate information from meaning. His central dogma, “Meaning is irrelevant,” declared that information could be handled with greater freedom if it was treated as a mathematical abstraction independent of meaning. The consequence of this freedom is the flood of information in which we are drowning. The immense size of modern databases gives us a feeling of meaninglessness. Information in such quantities reminds us of Borges’s library extending infinitely in all directions. It is our task as humans to bring meaning back into this wasteland. As finite creatures who think and feel, we can create islands of meaning in the sea of information. Gleick ends his book with Borges’s image of the human condition:We walk the corridors, searching the shelves and rearranging them, looking for lines of meaning amid leagues of cacophony and incoherence, reading the history of the past and of the future, collecting our thoughts and collecting the thoughts of others, and every so often glimpsing mirrors, in which we may recognize creatures of the information.
Weiye Loh

The Decline Effect and the Scientific Method : The New Yorker - 0 views

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shared by Weiye Loh on 05 Jan 11 - No Cached
  • On September 18, 2007, a few dozen neuroscientists, psychiatrists, and drug-company executives gathered in a hotel conference room in Brussels to hear some startling news. It had to do with a class of drugs known as atypical or second-generation antipsychotics, which came on the market in the early nineties.
  • the therapeutic power of the drugs appeared to be steadily waning. A recent study showed an effect that was less than half of that documented in the first trials, in the early nineteen-nineties. Many researchers began to argue that the expensive pharmaceuticals weren’t any better than first-generation antipsychotics, which have been in use since the fifties. “In fact, sometimes they now look even worse,” John Davis, a professor of psychiatry at the University of Illinois at Chicago, told me.
  • Before the effectiveness of a drug can be confirmed, it must be tested and tested again. Different scientists in different labs need to repeat the protocols and publish their results. The test of replicability, as it’s known, is the foundation of modern research. Replicability is how the community enforces itself. It’s a safeguard for the creep of subjectivity. Most of the time, scientists know what results they want, and that can influence the results they get. The premise of replicability is that the scientific community can correct for these flaws.
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  • But now all sorts of well-established, multiply confirmed findings have started to look increasingly uncertain. It’s as if our facts were losing their truth: claims that have been enshrined in textbooks are suddenly unprovable. This phenomenon doesn’t yet have an official name, but it’s occurring across a wide range of fields, from psychology to ecology. In the field of medicine, the phenomenon seems extremely widespread, affecting not only antipsychotics but also therapies ranging from cardiac stents to Vitamin E and antidepressants: Davis has a forthcoming analysis demonstrating that the efficacy of antidepressants has gone down as much as threefold in recent decades.
  • In private, Schooler began referring to the problem as “cosmic habituation,” by analogy to the decrease in response that occurs when individuals habituate to particular stimuli. “Habituation is why you don’t notice the stuff that’s always there,” Schooler says. “It’s an inevitable process of adjustment, a ratcheting down of excitement. I started joking that it was like the cosmos was habituating to my ideas. I took it very personally.”
  • At first, he assumed that he’d made an error in experimental design or a statistical miscalculation. But he couldn’t find anything wrong with his research. He then concluded that his initial batch of research subjects must have been unusually susceptible to verbal overshadowing. (John Davis, similarly, has speculated that part of the drop-off in the effectiveness of antipsychotics can be attributed to using subjects who suffer from milder forms of psychosis which are less likely to show dramatic improvement.) “It wasn’t a very satisfying explanation,” Schooler says. “One of my mentors told me that my real mistake was trying to replicate my work. He told me doing that was just setting myself up for disappointment.”
  • the effect is especially troubling because of what it exposes about the scientific process. If replication is what separates the rigor of science from the squishiness of pseudoscience, where do we put all these rigorously validated findings that can no longer be proved? Which results should we believe? Francis Bacon, the early-modern philosopher and pioneer of the scientific method, once declared that experiments were essential, because they allowed us to “put nature to the question.” But it appears that nature often gives us different answers.
  • The most likely explanation for the decline is an obvious one: regression to the mean. As the experiment is repeated, that is, an early statistical fluke gets cancelled out. The extrasensory powers of Schooler’s subjects didn’t decline—they were simply an illusion that vanished over time. And yet Schooler has noticed that many of the data sets that end up declining seem statistically solid—that is, they contain enough data that any regression to the mean shouldn’t be dramatic. “These are the results that pass all the tests,” he says. “The odds of them being random are typically quite remote, like one in a million. This means that the decline effect should almost never happen. But it happens all the time!
  • this is why Schooler believes that the decline effect deserves more attention: its ubiquity seems to violate the laws of statistics. “Whenever I start talking about this, scientists get very nervous,” he says. “But I still want to know what happened to my results. Like most scientists, I assumed that it would get easier to document my effect over time. I’d get better at doing the experiments, at zeroing in on the conditions that produce verbal overshadowing. So why did the opposite happen? I’m convinced that we can use the tools of science to figure this out. First, though, we have to admit that we’ve got a problem.”
  • In 2001, Michael Jennions, a biologist at the Australian National University, set out to analyze “temporal trends” across a wide range of subjects in ecology and evolutionary biology. He looked at hundreds of papers and forty-four meta-analyses (that is, statistical syntheses of related studies), and discovered a consistent decline effect over time, as many of the theories seemed to fade into irrelevance. In fact, even when numerous variables were controlled for—Jennions knew, for instance, that the same author might publish several critical papers, which could distort his analysis—there was still a significant decrease in the validity of the hypothesis, often within a year of publication. Jennions admits that his findings are troubling, but expresses a reluctance to talk about them publicly. “This is a very sensitive issue for scientists,” he says. “You know, we’re supposed to be dealing with hard facts, the stuff that’s supposed to stand the test of time. But when you see these trends you become a little more skeptical of things.”
  • the worst part was that when I submitted these null results I had difficulty getting them published. The journals only wanted confirming data. It was too exciting an idea to disprove, at least back then.
  • the steep rise and slow fall of fluctuating asymmetry is a clear example of a scientific paradigm, one of those intellectual fads that both guide and constrain research: after a new paradigm is proposed, the peer-review process is tilted toward positive results. But then, after a few years, the academic incentives shift—the paradigm has become entrenched—so that the most notable results are now those that disprove the theory.
  • Jennions, similarly, argues that the decline effect is largely a product of publication bias, or the tendency of scientists and scientific journals to prefer positive data over null results, which is what happens when no effect is found. The bias was first identified by the statistician Theodore Sterling, in 1959, after he noticed that ninety-seven per cent of all published psychological studies with statistically significant data found the effect they were looking for. A “significant” result is defined as any data point that would be produced by chance less than five per cent of the time. This ubiquitous test was invented in 1922 by the English mathematician Ronald Fisher, who picked five per cent as the boundary line, somewhat arbitrarily, because it made pencil and slide-rule calculations easier. Sterling saw that if ninety-seven per cent of psychology studies were proving their hypotheses, either psychologists were extraordinarily lucky or they published only the outcomes of successful experiments. In recent years, publication bias has mostly been seen as a problem for clinical trials, since pharmaceutical companies are less interested in publishing results that aren’t favorable. But it’s becoming increasingly clear that publication bias also produces major distortions in fields without large corporate incentives, such as psychology and ecology.
  • While publication bias almost certainly plays a role in the decline effect, it remains an incomplete explanation. For one thing, it fails to account for the initial prevalence of positive results among studies that never even get submitted to journals. It also fails to explain the experience of people like Schooler, who have been unable to replicate their initial data despite their best efforts
  • an equally significant issue is the selective reporting of results—the data that scientists choose to document in the first place. Palmer’s most convincing evidence relies on a statistical tool known as a funnel graph. When a large number of studies have been done on a single subject, the data should follow a pattern: studies with a large sample size should all cluster around a common value—the true result—whereas those with a smaller sample size should exhibit a random scattering, since they’re subject to greater sampling error. This pattern gives the graph its name, since the distribution resembles a funnel.
  • The funnel graph visually captures the distortions of selective reporting. For instance, after Palmer plotted every study of fluctuating asymmetry, he noticed that the distribution of results with smaller sample sizes wasn’t random at all but instead skewed heavily toward positive results.
  • Palmer has since documented a similar problem in several other contested subject areas. “Once I realized that selective reporting is everywhere in science, I got quite depressed,” Palmer told me. “As a researcher, you’re always aware that there might be some nonrandom patterns, but I had no idea how widespread it is.” In a recent review article, Palmer summarized the impact of selective reporting on his field: “We cannot escape the troubling conclusion that some—perhaps many—cherished generalities are at best exaggerated in their biological significance and at worst a collective illusion nurtured by strong a-priori beliefs often repeated.”
  • Palmer emphasizes that selective reporting is not the same as scientific fraud. Rather, the problem seems to be one of subtle omissions and unconscious misperceptions, as researchers struggle to make sense of their results. Stephen Jay Gould referred to this as the “shoehorning” process. “A lot of scientific measurement is really hard,” Simmons told me. “If you’re talking about fluctuating asymmetry, then it’s a matter of minuscule differences between the right and left sides of an animal. It’s millimetres of a tail feather. And so maybe a researcher knows that he’s measuring a good male”—an animal that has successfully mated—“and he knows that it’s supposed to be symmetrical. Well, that act of measurement is going to be vulnerable to all sorts of perception biases. That’s not a cynical statement. That’s just the way human beings work.”
  • One of the classic examples of selective reporting concerns the testing of acupuncture in different countries. While acupuncture is widely accepted as a medical treatment in various Asian countries, its use is much more contested in the West. These cultural differences have profoundly influenced the results of clinical trials. Between 1966 and 1995, there were forty-seven studies of acupuncture in China, Taiwan, and Japan, and every single trial concluded that acupuncture was an effective treatment. During the same period, there were ninety-four clinical trials of acupuncture in the United States, Sweden, and the U.K., and only fifty-six per cent of these studies found any therapeutic benefits. As Palmer notes, this wide discrepancy suggests that scientists find ways to confirm their preferred hypothesis, disregarding what they don’t want to see. Our beliefs are a form of blindness.
  • John Ioannidis, an epidemiologist at Stanford University, argues that such distortions are a serious issue in biomedical research. “These exaggerations are why the decline has become so common,” he says. “It’d be really great if the initial studies gave us an accurate summary of things. But they don’t. And so what happens is we waste a lot of money treating millions of patients and doing lots of follow-up studies on other themes based on results that are misleading.”
  • In 2005, Ioannidis published an article in the Journal of the American Medical Association that looked at the forty-nine most cited clinical-research studies in three major medical journals. Forty-five of these studies reported positive results, suggesting that the intervention being tested was effective. Because most of these studies were randomized controlled trials—the “gold standard” of medical evidence—they tended to have a significant impact on clinical practice, and led to the spread of treatments such as hormone replacement therapy for menopausal women and daily low-dose aspirin to prevent heart attacks and strokes. Nevertheless, the data Ioannidis found were disturbing: of the thirty-four claims that had been subject to replication, forty-one per cent had either been directly contradicted or had their effect sizes significantly downgraded.
  • The situation is even worse when a subject is fashionable. In recent years, for instance, there have been hundreds of studies on the various genes that control the differences in disease risk between men and women. These findings have included everything from the mutations responsible for the increased risk of schizophrenia to the genes underlying hypertension. Ioannidis and his colleagues looked at four hundred and thirty-two of these claims. They quickly discovered that the vast majority had serious flaws. But the most troubling fact emerged when he looked at the test of replication: out of four hundred and thirty-two claims, only a single one was consistently replicable. “This doesn’t mean that none of these claims will turn out to be true,” he says. “But, given that most of them were done badly, I wouldn’t hold my breath.”
  • the main problem is that too many researchers engage in what he calls “significance chasing,” or finding ways to interpret the data so that it passes the statistical test of significance—the ninety-five-per-cent boundary invented by Ronald Fisher. “The scientists are so eager to pass this magical test that they start playing around with the numbers, trying to find anything that seems worthy,” Ioannidis says. In recent years, Ioannidis has become increasingly blunt about the pervasiveness of the problem. One of his most cited papers has a deliberately provocative title: “Why Most Published Research Findings Are False.”
  • The problem of selective reporting is rooted in a fundamental cognitive flaw, which is that we like proving ourselves right and hate being wrong. “It feels good to validate a hypothesis,” Ioannidis said. “It feels even better when you’ve got a financial interest in the idea or your career depends upon it. And that’s why, even after a claim has been systematically disproven”—he cites, for instance, the early work on hormone replacement therapy, or claims involving various vitamins—“you still see some stubborn researchers citing the first few studies that show a strong effect. They really want to believe that it’s true.”
  • scientists need to become more rigorous about data collection before they publish. “We’re wasting too much time chasing after bad studies and underpowered experiments,” he says. The current “obsession” with replicability distracts from the real problem, which is faulty design. He notes that nobody even tries to replicate most science papers—there are simply too many. (According to Nature, a third of all studies never even get cited, let alone repeated.)
  • Schooler recommends the establishment of an open-source database, in which researchers are required to outline their planned investigations and document all their results. “I think this would provide a huge increase in access to scientific work and give us a much better way to judge the quality of an experiment,” Schooler says. “It would help us finally deal with all these issues that the decline effect is exposing.”
  • Although such reforms would mitigate the dangers of publication bias and selective reporting, they still wouldn’t erase the decline effect. This is largely because scientific research will always be shadowed by a force that can’t be curbed, only contained: sheer randomness. Although little research has been done on the experimental dangers of chance and happenstance, the research that exists isn’t encouraging
  • John Crabbe, a neuroscientist at the Oregon Health and Science University, conducted an experiment that showed how unknowable chance events can skew tests of replicability. He performed a series of experiments on mouse behavior in three different science labs: in Albany, New York; Edmonton, Alberta; and Portland, Oregon. Before he conducted the experiments, he tried to standardize every variable he could think of. The same strains of mice were used in each lab, shipped on the same day from the same supplier. The animals were raised in the same kind of enclosure, with the same brand of sawdust bedding. They had been exposed to the same amount of incandescent light, were living with the same number of littermates, and were fed the exact same type of chow pellets. When the mice were handled, it was with the same kind of surgical glove, and when they were tested it was on the same equipment, at the same time in the morning.
  • The premise of this test of replicability, of course, is that each of the labs should have generated the same pattern of results. “If any set of experiments should have passed the test, it should have been ours,” Crabbe says. “But that’s not the way it turned out.” In one experiment, Crabbe injected a particular strain of mouse with cocaine. In Portland the mice given the drug moved, on average, six hundred centimetres more than they normally did; in Albany they moved seven hundred and one additional centimetres. But in the Edmonton lab they moved more than five thousand additional centimetres. Similar deviations were observed in a test of anxiety. Furthermore, these inconsistencies didn’t follow any detectable pattern. In Portland one strain of mouse proved most anxious, while in Albany another strain won that distinction.
  • The disturbing implication of the Crabbe study is that a lot of extraordinary scientific data are nothing but noise. The hyperactivity of those coked-up Edmonton mice wasn’t an interesting new fact—it was a meaningless outlier, a by-product of invisible variables we don’t understand. The problem, of course, is that such dramatic findings are also the most likely to get published in prestigious journals, since the data are both statistically significant and entirely unexpected. Grants get written, follow-up studies are conducted. The end result is a scientific accident that can take years to unravel.
  • This suggests that the decline effect is actually a decline of illusion.
  • While Karl Popper imagined falsification occurring with a single, definitive experiment—Galileo refuted Aristotelian mechanics in an afternoon—the process turns out to be much messier than that. Many scientific theories continue to be considered true even after failing numerous experimental tests. Verbal overshadowing might exhibit the decline effect, but it remains extensively relied upon within the field. The same holds for any number of phenomena, from the disappearing benefits of second-generation antipsychotics to the weak coupling ratio exhibited by decaying neutrons, which appears to have fallen by more than ten standard deviations between 1969 and 2001. Even the law of gravity hasn’t always been perfect at predicting real-world phenomena. (In one test, physicists measuring gravity by means of deep boreholes in the Nevada desert found a two-and-a-half-per-cent discrepancy between the theoretical predictions and the actual data.) Despite these findings, second-generation antipsychotics are still widely prescribed, and our model of the neutron hasn’t changed. The law of gravity remains the same.
  • Such anomalies demonstrate the slipperiness of empiricism. Although many scientific ideas generate conflicting results and suffer from falling effect sizes, they continue to get cited in the textbooks and drive standard medical practice. Why? Because these ideas seem true. Because they make sense. Because we can’t bear to let them go. And this is why the decline effect is so troubling. Not because it reveals the human fallibility of science, in which data are tweaked and beliefs shape perceptions. (Such shortcomings aren’t surprising, at least for scientists.) And not because it reveals that many of our most exciting theories are fleeting fads and will soon be rejected. (That idea has been around since Thomas Kuhn.) The decline effect is troubling because it reminds us how difficult it is to prove anything. We like to pretend that our experiments define the truth for us. But that’s often not the case. Just because an idea is true doesn’t mean it can be proved. And just because an idea can be proved doesn’t mean it’s true. When the experiments are done, we still have to choose what to believe.
Weiye Loh

Science, Strong Inference -- Proper Scientific Method - 0 views

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  • Scientists these days tend to keep up a polite fiction that all science is equal. Except for the work of the misguided opponent whose arguments we happen to be refuting at the time, we speak as though every scientist's field and methods of study are as good as every other scientist's and perhaps a little better. This keeps us all cordial when it comes to recommending each other for government grants.
  • Why should there be such rapid advances in some fields and not in others? I think the usual explanations that we tend to think of - such as the tractability of the subject, or the quality or education of the men drawn into it, or the size of research contracts - are important but inadequate. I have begun to believe that the primary factor in scientific advance is an intellectual one. These rapidly moving fields are fields where a particular method of doing scientific research is systematically used and taught, an accumulative method of inductive inference that is so effective that I think it should be given the name of "strong inference." I believe it is important to examine this method, its use and history and rationale, and to see whether other groups and individuals might learn to adopt it profitably in their own scientific and intellectual work. In its separate elements, strong inference is just the simple and old-fashioned method of inductive inference that goes back to Francis Bacon. The steps are familiar to every college student and are practiced, off and on, by every scientist. The difference comes in their systematic application. Strong inference consists of applying the following steps to every problem in science, formally and explicitly and regularly: Devising alternative hypotheses; Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly is possible, exclude one or more of the hypotheses; Carrying out the experiment so as to get a clean result; Recycling the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain, and so on.
  • On any new problem, of course, inductive inference is not as simple and certain as deduction, because it involves reaching out into the unknown. Steps 1 and 2 require intellectual inventions, which must be cleverly chosen so that hypothesis, experiment, outcome, and exclusion will be related in a rigorous syllogism; and the question of how to generate such inventions is one which has been extensively discussed elsewhere (2, 3). What the formal schema reminds us to do is to try to make these inventions, to take the next step, to proceed to the next fork, without dawdling or getting tied up in irrelevancies.
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  • It is clear why this makes for rapid and powerful progress. For exploring the unknown, there is no faster method; this is the minimum sequence of steps. Any conclusion that is not an exclusion is insecure and must be rechecked. Any delay in recycling to the next set of hypotheses is only a delay. Strong inference, and the logical tree it generates, are to inductive reasoning what the syllogism is to deductive reasoning in that it offers a regular method for reaching firm inductive conclusions one after the other as rapidly as possible.
  • "But what is so novel about this?" someone will say. This is the method of science and always has been, why give it a special name? The reason is that many of us have almost forgotten it. Science is now an everyday business. Equipment, calculations, lectures become ends in themselves. How many of us write down our alternatives and crucial experiments every day, focusing on the exclusion of a hypothesis? We may write our scientific papers so that it looks as if we had steps 1, 2, and 3 in mind all along. But in between, we do busywork. We become "method- oriented" rather than "problem-oriented." We say we prefer to "feel our way" toward generalizations. We fail to teach our students how to sharpen up their inductive inferences. And we do not realize the added power that the regular and explicit use of alternative hypothesis and sharp exclusion could give us at every step of our research.
  • A distinguished cell biologist rose and said, "No two cells give the same properties. Biology is the science of heterogeneous systems." And he added privately. "You know there are scientists, and there are people in science who are just working with these over-simplified model systems - DNA chains and in vitro systems - who are not doing science at all. We need their auxiliary work: they build apparatus, they make minor studies, but they are not scientists." To which Cy Levinthal replied: "Well, there are two kinds of biologists, those who are looking to see if there is one thing that can be understood and those who keep saying it is very complicated and that nothing can be understood. . . . You must study the simplest system you think has the properties you are interested in."
  • At the 1958 Conference on Biophysics, at Boulder, there was a dramatic confrontation between the two points of view. Leo Szilard said: "The problems of how enzymes are induced, of how proteins are synthesized, of how antibodies are formed, are closer to solution than is generally believed. If you do stupid experiments, and finish one a year, it can take 50 years. But if you stop doing experiments for a little while and think how proteins can possibly be synthesized, there are only about 5 different ways, not 50! And it will take only a few experiments to distinguish these." One of the young men added: "It is essentially the old question: How small and elegant an experiment can you perform?" These comments upset a number of those present. An electron microscopist said. "Gentlemen, this is off the track. This is philosophy of science." Szilard retorted. "I was not quarreling with third-rate scientists: I was quarreling with first-rate scientists."
  • Any criticism or challenge to consider changing our methods strikes of course at all our ego-defenses. But in this case the analytical method offers the possibility of such great increases in effectiveness that it is unfortunate that it cannot be regarded more often as a challenge to learning rather than as challenge to combat. Many of the recent triumphs in molecular biology have in fact been achieved on just such "oversimplified model systems," very much along the analytical lines laid down in the 1958 discussion. They have not fallen to the kind of men who justify themselves by saying "No two cells are alike," regardless of how true that may ultimately be. The triumphs are in fact triumphs of a new way of thinking.
  • the emphasis on strong inference
  • is also partly due to the nature of the fields themselves. Biology, with its vast informational detail and complexity, is a "high-information" field, where years and decades can easily be wasted on the usual type of "low-information" observations or experiments if one does not think carefully in advance about what the most important and conclusive experiments would be. And in high-energy physics, both the "information flux" of particles from the new accelerators and the million-dollar costs of operation have forced a similar analytical approach. It pays to have a top-notch group debate every experiment ahead of time; and the habit spreads throughout the field.
  • Historically, I think, there have been two main contributions to the development of a satisfactory strong-inference method. The first is that of Francis Bacon (13). He wanted a "surer method" of "finding out nature" than either the logic-chopping or all-inclusive theories of the time or the laudable but crude attempts to make inductions "by simple enumeration." He did not merely urge experiments as some suppose, he showed the fruitfulness of interconnecting theory and experiment so that the one checked the other. Of the many inductive procedures he suggested, the most important, I think, was the conditional inductive tree, which proceeded from alternative hypothesis (possible "causes," as he calls them), through crucial experiments ("Instances of the Fingerpost"), to exclusion of some alternatives and adoption of what is left ("establishing axioms"). His Instances of the Fingerpost are explicitly at the forks in the logical tree, the term being borrowed "from the fingerposts which are set up where roads part, to indicate the several directions."
  • ere was a method that could separate off the empty theories! Bacon, said the inductive method could be learned by anybody, just like learning to "draw a straighter line or more perfect circle . . . with the help of a ruler or a pair of compasses." "My way of discovering sciences goes far to level men's wit and leaves but little to individual excellence, because it performs everything by the surest rules and demonstrations." Even occasional mistakes would not be fatal. "Truth will sooner come out from error than from confusion."
  • Nevertheless there is a difficulty with this method. As Bacon emphasizes, it is necessary to make "exclusions." He says, "The induction which is to be available for the discovery and demonstration of sciences and arts, must analyze nature by proper rejections and exclusions, and then, after a sufficient number of negatives come to a conclusion on the affirmative instances." "[To man] it is granted only to proceed at first by negatives, and at last to end in affirmatives after exclusion has been exhausted." Or, as the philosopher Karl Popper says today there is no such thing as proof in science - because some later alternative explanation may be as good or better - so that science advances only by disproofs. There is no point in making hypotheses that are not falsifiable because such hypotheses do not say anything, "it must be possible for all empirical scientific system to be refuted by experience" (14).
  • The difficulty is that disproof is a hard doctrine. If you have a hypothesis and I have another hypothesis, evidently one of them must be eliminated. The scientist seems to have no choice but to be either soft-headed or disputatious. Perhaps this is why so many tend to resist the strong analytical approach and why some great scientists are so disputatious.
  • Fortunately, it seems to me, this difficulty can be removed by the use of a second great intellectual invention, the "method of multiple hypotheses," which is what was needed to round out the Baconian scheme. This is a method that was put forward by T.C. Chamberlin (15), a geologist at Chicago at the turn of the century, who is best known for his contribution to the Chamberlain-Moulton hypothesis of the origin of the solar system.
  • Chamberlin says our trouble is that when we make a single hypothesis, we become attached to it. "The moment one has offered an original explanation for a phenomenon which seems satisfactory, that moment affection for his intellectual child springs into existence, and as the explanation grows into a definite theory his parental affections cluster about his offspring and it grows more and more dear to him. . . . There springs up also unwittingly a pressing of the theory to make it fit the facts and a pressing of the facts to make them fit the theory..." "To avoid this grave danger, the method of multiple working hypotheses is urged. It differs from the simple working hypothesis in that it distributes the effort and divides the affections. . . . Each hypothesis suggests its own criteria, its own method of proof, its own method of developing the truth, and if a group of hypotheses encompass the subject on all sides, the total outcome of means and of methods is full and rich."
  • The conflict and exclusion of alternatives that is necessary to sharp inductive inference has been all too often a conflict between men, each with his single Ruling Theory. But whenever each man begins to have multiple working hypotheses, it becomes purely a conflict between ideas. It becomes much easier then for each of us to aim every day at conclusive disproofs - at strong inference - without either reluctance or combativeness. In fact, when there are multiple hypotheses, which are not anyone's "personal property," and when there are crucial experiments to test them, the daily life in the laboratory takes on an interest and excitement it never had, and the students can hardly wait to get to work to see how the detective story will come out. It seems to me that this is the reason for the development of those distinctive habits of mind and the "complex thought" that Chamberlin described, the reason for the sharpness, the excitement, the zeal, the teamwork - yes, even international teamwork - in molecular biology and high- energy physics today. What else could be so effective?
  • Unfortunately, I think, there are other other areas of science today that are sick by comparison, because they have forgotten the necessity for alternative hypotheses and disproof. Each man has only one branch - or none - on the logical tree, and it twists at random without ever coming to the need for a crucial decision at any point. We can see from the external symptoms that there is something scientifically wrong. The Frozen Method, The Eternal Surveyor, The Never Finished, The Great Man With a Single Hypothcsis, The Little Club of Dependents, The Vendetta, The All-Encompassing Theory Which Can Never Be Falsified.
  • a "theory" of this sort is not a theory at all, because it does not exclude anything. It predicts everything, and therefore does not predict anything. It becomes simply a verbal formula which the graduate student repeats and believes because the professor has said it so often. This is not science, but faith; not theory, but theology. Whether it is hand-waving or number-waving, or equation-waving, a theory is not a theory unless it can be disproved. That is, unless it can be falsified by some possible experimental outcome.
  • the work methods of a number of scientists have been testimony to the power of strong inference. Is success not due in many cases to systematic use of Bacon's "surest rules and demonstrations" as much as to rare and unattainable intellectual power? Faraday's famous diary (16), or Fermi's notebooks (3, 17), show how these men believed in the effectiveness of daily steps in applying formal inductive methods to one problem after another.
  • Surveys, taxonomy, design of equipment, systematic measurements and tables, theoretical computations - all have their proper and honored place, provided they are parts of a chain of precise induction of how nature works. Unfortunately, all too often they become ends in themselves, mere time-serving from the point of view of real scientific advance, a hypertrophied methodology that justifies itself as a lore of respectability.
  • We speak piously of taking measurements and making small studies that will "add another brick to the temple of science." Most such bricks just lie around the brickyard (20). Tables of constraints have their place and value, but the study of one spectrum after another, if not frequently re-evaluated, may become a substitute for thinking, a sad waste of intelligence in a research laboratory, and a mistraining whose crippling effects may last a lifetime.
  • Beware of the man of one method or one instrument, either experimental or theoretical. He tends to become method-oriented rather than problem-oriented. The method-oriented man is shackled; the problem-oriented man is at least reaching freely toward that is most important. Strong inference redirects a man to problem-orientation, but it requires him to be willing repeatedly to put aside his last methods and teach himself new ones.
  • anyone who asks the question about scientific effectiveness will also conclude that much of the mathematizing in physics and chemistry today is irrelevant if not misleading. The great value of mathematical formulation is that when an experiment agrees with a calculation to five decimal places, a great many alternative hypotheses are pretty well excluded (though the Bohr theory and the Schrödinger theory both predict exactly the same Rydberg constant!). But when the fit is only to two decimal places, or one, it may be a trap for the unwary; it may be no better than any rule-of-thumb extrapolation, and some other kind of qualitative exclusion might be more rigorous for testing the assumptions and more important to scientific understanding than the quantitative fit.
  • Today we preach that science is not science unless it is quantitative. We substitute correlations for causal studies, and physical equations for organic reasoning. Measurements and equations are supposed to sharpen thinking, but, in my observation, they more often tend to make the thinking noncausal and fuzzy. They tend to become the object of scientific manipulation instead of auxiliary tests of crucial inferences.
  • Many - perhaps most - of the great issues of science are qualitative, not quantitative, even in physics and chemistry. Equations and measurements are useful when and only when they are related to proof; but proof or disproof comes first and is in fact strongest when it is absolutely convincing without any quantitative measurement.
  • you can catch phenomena in a logical box or in a mathematical box. The logical box is coarse but strong. The mathematical box is fine-grained but flimsy. The mathematical box is a beautiful way of wrapping up a problem, but it will not hold the phenomena unless they have been caught in a logical box to begin with.
  • Of course it is easy - and all too common - for one scientist to call the others unscientific. My point is not that my particular conclusions here are necessarily correct, but that we have long needed some absolute standard of possible scientific effectiveness by which to measure how well we are succeeding in various areas - a standard that many could agree on and one that would be undistorted by the scientific pressures and fashions of the times and the vested interests and busywork that they develop. It is not public evaluation I am interested in so much as a private measure by which to compare one's own scientific performance with what it might be. I believe that strong inference provides this kind of standard of what the maximum possible scientific effectiveness could be - as well as a recipe for reaching it.
  • The strong-inference point of view is so resolutely critical of methods of work and values in science that any attempt to compare specific cases is likely to sound but smug and destructive. Mainly one should try to teach it by example and by exhorting to self-analysis and self-improvement only in general terms
  • one severe but useful private test - a touchstone of strong inference - that removes the necessity for third-person criticism, because it is a test that anyone can learn to carry with him for use as needed. It is our old friend the Baconian "exclusion," but I call it "The Question." Obviously it should be applied as much to one's own thinking as to others'. It consists of asking in your own mind, on hearing any scientific explanation or theory put forward, "But sir, what experiment could disprove your hypothesis?"; or, on hearing a scientific experiment described, "But sir, what hypothesis does your experiment disprove?"
  • It is not true that all science is equal; or that we cannot justly compare the effectiveness of scientists by any method other than a mutual-recommendation system. The man to watch, the man to put your money on, is not the man who wants to make "a survey" or a "more detailed study" but the man with the notebook, the man with the alternative hypotheses and the crucial experiments, the man who knows how to answer your Question of disproof and is already working on it.
  • Weiye Loh on 20 Mar 11
     
    There is so much bad science and bad statistics information in media reports, publications, and shared between conversants that I think it is important to understand about facts and proofs and the associated pitfalls.
Weiye Loh

Do avatars have digital rights? - 20 views

hi weiye, i agree with you that this brings in the topic of representation. maybe you should try taking media and representation by Dr. Ingrid to discuss more on this. Going back to your questio...

avatars

started by Elaine Ong on 16 Sep 09 7 follow-ups, last by Weiye Loh on 18 Sep 09
Weiye Loh

The Black Swan of Cairo | Foreign Affairs - 0 views

www.foreignaffairs.com/...the-black-swan-of-cairo

Complex System Black Swan Falsifiability Politics Economics

shared by Weiye Loh on 01 May 11 - No Cached
  • It is both misguided and dangerous to push unobserved risks further into the statistical tails of the probability distribution of outcomes and allow these high-impact, low-probability "tail risks" to disappear from policymakers' fields of observation.
  • Such environments eventually experience massive blowups, catching everyone off-guard and undoing years of stability or, in some cases, ending up far worse than they were in their initial volatile state. Indeed, the longer it takes for the blowup to occur, the worse the resulting harm in both economic and political systems.
  • Seeking to restrict variability seems to be good policy (who does not prefer stability to chaos?), so it is with very good intentions that policymakers unwittingly increase the risk of major blowups. And it is the same misperception of the properties of natural systems that led to both the economic crisis of 2007-8 and the current turmoil in the Arab world. The policy implications are identical: to make systems robust, all risks must be visible and out in the open -- fluctuat nec mergitur (it fluctuates but does not sink) goes the Latin saying.
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  • Just as a robust economic system is one that encourages early failures (the concepts of "fail small" and "fail fast"), the U.S. government should stop supporting dictatorial regimes for the sake of pseudostability and instead allow political noise to rise to the surface. Making an economy robust in the face of business swings requires allowing risk to be visible; the same is true in politics.
  • Both the recent financial crisis and the current political crisis in the Middle East are grounded in the rise of complexity, interdependence, and unpredictability. Policymakers in the United Kingdom and the United States have long promoted policies aimed at eliminating fluctuation -- no more booms and busts in the economy, no more "Iranian surprises" in foreign policy. These policies have almost always produced undesirable outcomes. For example, the U.S. banking system became very fragile following a succession of progressively larger bailouts and government interventions, particularly after the 1983 rescue of major banks (ironically, by the same Reagan administration that trumpeted free markets). In the United States, promoting these bad policies has been a bipartisan effort throughout. Republicans have been good at fragilizing large corporations through bailouts, and Democrats have been good at fragilizing the government. At the same time, the financial system as a whole exhibited little volatility; it kept getting weaker while providing policymakers with the illusion of stability, illustrated most notably when Ben Bernanke, who was then a member of the Board of Governors of the U.S. Federal Reserve, declared the era of "the great moderation" in 2004.
  • Washington stabilized the market with bailouts and by allowing certain companies to grow "too big to fail." Because policymakers believed it was better to do something than to do nothing, they felt obligated to heal the economy rather than wait and see if it healed on its own.
  • The foreign policy equivalent is to support the incumbent no matter what. And just as banks took wild risks thanks to Greenspan's implicit insurance policy, client governments such as Hosni Mubarak's in Egypt for years engaged in overt plunder thanks to similarly reliable U.S. support.
  • Those who seek to prevent volatility on the grounds that any and all bumps in the road must be avoided paradoxically increase the probability that a tail risk will cause a major explosion.
  • In the realm of economics, price controls are designed to constrain volatility on the grounds that stable prices are a good thing. But although these controls might work in some rare situations, the long-term effect of any such system is an eventual and extremely costly blowup whose cleanup costs can far exceed the benefits accrued. The risks of a dictatorship, no matter how seemingly stable, are no different, in the long run, from those of an artificially controlled price.
  • Such attempts to institutionally engineer the world come in two types: those that conform to the world as it is and those that attempt to reform the world. The nature of humans, quite reasonably, is to intervene in an effort to alter their world and the outcomes it produces. But government interventions are laden with unintended -- and unforeseen -- consequences, particularly in complex systems, so humans must work with nature by tolerating systems that absorb human imperfections rather than seek to change them.
  • What is needed is a system that can prevent the harm done to citizens by the dishonesty of business elites; the limited competence of forecasters, economists, and statisticians; and the imperfections of regulation, not one that aims to eliminate these flaws. Humans must try to resist the illusion of control: just as foreign policy should be intelligence-proof (it should minimize its reliance on the competence of information-gathering organizations and the predictions of "experts" in what are inherently unpredictable domains), the economy should be regulator-proof, given that some regulations simply make the system itself more fragile. Due to the complexity of markets, intricate regulations simply serve to generate fees for lawyers and profits for sophisticated derivatives traders who can build complicated financial products that skirt those regulations.
  • The life of a turkey before Thanksgiving is illustrative: the turkey is fed for 1,000 days and every day seems to confirm that the farmer cares for it -- until the last day, when confidence is maximal. The "turkey problem" occurs when a naive analysis of stability is derived from the absence of past variations. Likewise, confidence in stability was maximal at the onset of the financial crisis in 2007.
  • The turkey problem for humans is the result of mistaking one environment for another. Humans simultaneously inhabit two systems: the linear and the complex. The linear domain is characterized by its predictability and the low degree of interaction among its components, which allows the use of mathematical methods that make forecasts reliable. In complex systems, there is an absence of visible causal links between the elements, masking a high degree of interdependence and extremely low predictability. Nonlinear elements are also present, such as those commonly known, and generally misunderstood, as "tipping points." Imagine someone who keeps adding sand to a sand pile without any visible consequence, until suddenly the entire pile crumbles. It would be foolish to blame the collapse on the last grain of sand rather than the structure of the pile, but that is what people do consistently, and that is the policy error.
  • Engineering, architecture, astronomy, most of physics, and much of common science are linear domains. The complex domain is the realm of the social world, epidemics, and economics. Crucially, the linear domain delivers mild variations without large shocks, whereas the complex domain delivers massive jumps and gaps. Complex systems are misunderstood, mostly because humans' sophistication, obtained over the history of human knowledge in the linear domain, does not transfer properly to the complex domain. Humans can predict a solar eclipse and the trajectory of a space vessel, but not the stock market or Egyptian political events. All man-made complex systems have commonalities and even universalities. Sadly, deceptive calm (followed by Black Swan surprises) seems to be one of those properties.
  • The system is responsible, not the components. But after the financial crisis of 2007-8, many people thought that predicting the subprime meltdown would have helped. It would not have, since it was a symptom of the crisis, not its underlying cause. Likewise, Obama's blaming "bad intelligence" for his administration's failure to predict the crisis in Egypt is symptomatic of both the misunderstanding of complex systems and the bad policies involved.
  • Obama's mistake illustrates the illusion of local causal chains -- that is, confusing catalysts for causes and assuming that one can know which catalyst will produce which effect. The final episode of the upheaval in Egypt was unpredictable for all observers, especially those involved. As such, blaming the CIA is as foolish as funding it to forecast such events. Governments are wasting billions of dollars on attempting to predict events that are produced by interdependent systems and are therefore not statistically understandable at the individual level.
  • Political and economic "tail events" are unpredictable, and their probabilities are not scientifically measurable. No matter how many dollars are spent on research, predicting revolutions is not the same as counting cards; humans will never be able to turn politics into the tractable randomness of blackjack.
  • Most explanations being offered for the current turmoil in the Middle East follow the "catalysts as causes" confusion. The riots in Tunisia and Egypt were initially attributed to rising commodity prices, not to stifling and unpopular dictatorships. But Bahrain and Libya are countries with high gdps that can afford to import grain and other commodities. Again, the focus is wrong even if the logic is comforting. It is the system and its fragility, not events, that must be studied -- what physicists call "percolation theory," in which the properties of the terrain are studied rather than those of a single element of the terrain.
  • When dealing with a system that is inherently unpredictable, what should be done? Differentiating between two types of countries is useful. In the first, changes in government do not lead to meaningful differences in political outcomes (since political tensions are out in the open). In the second type, changes in government lead to both drastic and deeply unpredictable changes.
  • Humans fear randomness -- a healthy ancestral trait inherited from a different environment. Whereas in the past, which was a more linear world, this trait enhanced fitness and increased chances of survival, it can have the reverse effect in today's complex world, making volatility take the shape of nasty Black Swans hiding behind deceptive periods of "great moderation." This is not to say that any and all volatility should be embraced. Insurance should not be banned, for example.
  • But alongside the "catalysts as causes" confusion sit two mental biases: the illusion of control and the action bias (the illusion that doing something is always better than doing nothing). This leads to the desire to impose man-made solutions
  • Variation is information. When there is no variation, there is no information. This explains the CIA's failure to predict the Egyptian revolution and, a generation before, the Iranian Revolution -- in both cases, the revolutionaries themselves did not have a clear idea of their relative strength with respect to the regime they were hoping to topple. So rather than subsidize and praise as a "force for stability" every tin-pot potentate on the planet, the U.S. government should encourage countries to let information flow upward through the transparency that comes with political agitation. It should not fear fluctuations per se, since allowing them to be in the open, as Italy and Lebanon both show in different ways, creates the stability of small jumps.
  • As Seneca wrote in De clementia, "Repeated punishment, while it crushes the hatred of a few, stirs the hatred of all . . . just as trees that have been trimmed throw out again countless branches." The imposition of peace through repeated punishment lies at the heart of many seemingly intractable conflicts, including the Israeli-Palestinian stalemate. Furthermore, dealing with seemingly reliable high-level officials rather than the people themselves prevents any peace treaty signed from being robust. The Romans were wise enough to know that only a free man under Roman law could be trusted to engage in a contract; by extension, only a free people can be trusted to abide by a treaty. Treaties that are negotiated with the consent of a broad swath of the populations on both sides of a conflict tend to survive. Just as no central bank is powerful enough to dictate stability, no superpower can be powerful enough to guarantee solid peace alone.
  • As Jean-Jacques Rousseau put it, "A little bit of agitation gives motivation to the soul, and what really makes the species prosper is not peace so much as freedom." With freedom comes some unpredictable fluctuation. This is one of life's packages: there is no freedom without noise -- and no stability without volatility.∂
Weiye Loh

Odds Are, It's Wrong - Science News - 0 views

www.sciencenews.org/...Odds_Are%2C_Its_Wrong

Statistics Science

shared by Weiye Loh on 20 Mar 10 - Cached
  • science has long been married to mathematics. Generally it has been for the better. Especially since the days of Galileo and Newton, math has nurtured science. Rigorous mathematical methods have secured science’s fidelity to fact and conferred a timeless reliability to its findings.
  • a mutant form of math has deflected science’s heart from the modes of calculation that had long served so faithfully. Science was seduced by statistics, the math rooted in the same principles that guarantee profits for Las Vegas casinos. Supposedly, the proper use of statistics makes relying on scientific results a safe bet. But in practice, widespread misuse of statistical methods makes science more like a crapshoot.
  • science’s dirtiest secret: The “scientific method” of testing hypotheses by statistical analysis stands on a flimsy foundation. Statistical tests are supposed to guide scientists in judging whether an experimental result reflects some real effect or is merely a random fluke, but the standard methods mix mutually inconsistent philosophies and offer no meaningful basis for making such decisions. Even when performed correctly, statistical tests are widely misunderstood and frequently misinterpreted. As a result, countless conclusions in the scientific literature are erroneous, and tests of medical dangers or treatments are often contradictory and confusing.
  • ...24 more annotations...
  • Experts in the math of probability and statistics are well aware of these problems and have for decades expressed concern about them in major journals. Over the years, hundreds of published papers have warned that science’s love affair with statistics has spawned countless illegitimate findings. In fact, if you believe what you read in the scientific literature, you shouldn’t believe what you read in the scientific literature.
  • “There are more false claims made in the medical literature than anybody appreciates,” he says. “There’s no question about that.”Nobody contends that all of science is wrong, or that it hasn’t compiled an impressive array of truths about the natural world. Still, any single scientific study alone is quite likely to be incorrect, thanks largely to the fact that the standard statistical system for drawing conclusions is, in essence, illogical. “A lot of scientists don’t understand statistics,” says Goodman. “And they don’t understand statistics because the statistics don’t make sense.”
  • In 2007, for instance, researchers combing the medical literature found numerous studies linking a total of 85 genetic variants in 70 different genes to acute coronary syndrome, a cluster of heart problems. When the researchers compared genetic tests of 811 patients that had the syndrome with a group of 650 (matched for sex and age) that didn’t, only one of the suspect gene variants turned up substantially more often in those with the syndrome — a number to be expected by chance.“Our null results provide no support for the hypothesis that any of the 85 genetic variants tested is a susceptibility factor” for the syndrome, the researchers reported in the Journal of the American Medical Association.How could so many studies be wrong? Because their conclusions relied on “statistical significance,” a concept at the heart of the mathematical analysis of modern scientific experiments.
  • Statistical significance is a phrase that every science graduate student learns, but few comprehend. While its origins stretch back at least to the 19th century, the modern notion was pioneered by the mathematician Ronald A. Fisher in the 1920s. His original interest was agriculture. He sought a test of whether variation in crop yields was due to some specific intervention (say, fertilizer) or merely reflected random factors beyond experimental control.Fisher first assumed that fertilizer caused no difference — the “no effect” or “null” hypothesis. He then calculated a number called the P value, the probability that an observed yield in a fertilized field would occur if fertilizer had no real effect. If P is less than .05 — meaning the chance of a fluke is less than 5 percent — the result should be declared “statistically significant,” Fisher arbitrarily declared, and the no effect hypothesis should be rejected, supposedly confirming that fertilizer works.Fisher’s P value eventually became the ultimate arbiter of credibility for science results of all sorts
  • But in fact, there’s no logical basis for using a P value from a single study to draw any conclusion. If the chance of a fluke is less than 5 percent, two possible conclusions remain: There is a real effect, or the result is an improbable fluke. Fisher’s method offers no way to know which is which. On the other hand, if a study finds no statistically significant effect, that doesn’t prove anything, either. Perhaps the effect doesn’t exist, or maybe the statistical test wasn’t powerful enough to detect a small but real effect.
  • Soon after Fisher established his system of statistical significance, it was attacked by other mathematicians, notably Egon Pearson and Jerzy Neyman. Rather than testing a null hypothesis, they argued, it made more sense to test competing hypotheses against one another. That approach also produces a P value, which is used to gauge the likelihood of a “false positive” — concluding an effect is real when it actually isn’t. What  eventually emerged was a hybrid mix of the mutually inconsistent Fisher and Neyman-Pearson approaches, which has rendered interpretations of standard statistics muddled at best and simply erroneous at worst. As a result, most scientists are confused about the meaning of a P value or how to interpret it. “It’s almost never, ever, ever stated correctly, what it means,” says Goodman.
  • experimental data yielding a P value of .05 means that there is only a 5 percent chance of obtaining the observed (or more extreme) result if no real effect exists (that is, if the no-difference hypothesis is correct). But many explanations mangle the subtleties in that definition. A recent popular book on issues involving science, for example, states a commonly held misperception about the meaning of statistical significance at the .05 level: “This means that it is 95 percent certain that the observed difference between groups, or sets of samples, is real and could not have arisen by chance.”
  • That interpretation commits an egregious logical error (technical term: “transposed conditional”): confusing the odds of getting a result (if a hypothesis is true) with the odds favoring the hypothesis if you observe that result. A well-fed dog may seldom bark, but observing the rare bark does not imply that the dog is hungry. A dog may bark 5 percent of the time even if it is well-fed all of the time. (See Box 2)
    • Weiye Loh
      Weiye Loh on 20 Mar 10
       
      Does the problem then, lie not in statistics, but the interpretation of statistics? Is the fallacy of appeal to probability is at work in such interpretation? 
  • Another common error equates statistical significance to “significance” in the ordinary use of the word. Because of the way statistical formulas work, a study with a very large sample can detect “statistical significance” for a small effect that is meaningless in practical terms. A new drug may be statistically better than an old drug, but for every thousand people you treat you might get just one or two additional cures — not clinically significant. Similarly, when studies claim that a chemical causes a “significantly increased risk of cancer,” they often mean that it is just statistically significant, possibly posing only a tiny absolute increase in risk.
  • Statisticians perpetually caution against mistaking statistical significance for practical importance, but scientific papers commit that error often. Ziliak studied journals from various fields — psychology, medicine and economics among others — and reported frequent disregard for the distinction.
  • “I found that eight or nine of every 10 articles published in the leading journals make the fatal substitution” of equating statistical significance to importance, he said in an interview. Ziliak’s data are documented in the 2008 book The Cult of Statistical Significance, coauthored with Deirdre McCloskey of the University of Illinois at Chicago.
  • Multiplicity of mistakesEven when “significance” is properly defined and P values are carefully calculated, statistical inference is plagued by many other problems. Chief among them is the “multiplicity” issue — the testing of many hypotheses simultaneously. When several drugs are tested at once, or a single drug is tested on several groups, chances of getting a statistically significant but false result rise rapidly.
  • Recognizing these problems, some researchers now calculate a “false discovery rate” to warn of flukes disguised as real effects. And genetics researchers have begun using “genome-wide association studies” that attempt to ameliorate the multiplicity issue (SN: 6/21/08, p. 20).
  • Many researchers now also commonly report results with confidence intervals, similar to the margins of error reported in opinion polls. Such intervals, usually given as a range that should include the actual value with 95 percent confidence, do convey a better sense of how precise a finding is. But the 95 percent confidence calculation is based on the same math as the .05 P value and so still shares some of its problems.
  • Statistical problems also afflict the “gold standard” for medical research, the randomized, controlled clinical trials that test drugs for their ability to cure or their power to harm. Such trials assign patients at random to receive either the substance being tested or a placebo, typically a sugar pill; random selection supposedly guarantees that patients’ personal characteristics won’t bias the choice of who gets the actual treatment. But in practice, selection biases may still occur, Vance Berger and Sherri Weinstein noted in 2004 in ControlledClinical Trials. “Some of the benefits ascribed to randomization, for example that it eliminates all selection bias, can better be described as fantasy than reality,” they wrote.
  • Randomization also should ensure that unknown differences among individuals are mixed in roughly the same proportions in the groups being tested. But statistics do not guarantee an equal distribution any more than they prohibit 10 heads in a row when flipping a penny. With thousands of clinical trials in progress, some will not be well randomized. And DNA differs at more than a million spots in the human genetic catalog, so even in a single trial differences may not be evenly mixed. In a sufficiently large trial, unrandomized factors may balance out, if some have positive effects and some are negative. (See Box 3) Still, trial results are reported as averages that may obscure individual differences, masking beneficial or harm­ful effects and possibly leading to approval of drugs that are deadly for some and denial of effective treatment to others.
  • nother concern is the common strategy of combining results from many trials into a single “meta-analysis,” a study of studies. In a single trial with relatively few participants, statistical tests may not detect small but real and possibly important effects. In principle, combining smaller studies to create a larger sample would allow the tests to detect such small effects. But statistical techniques for doing so are valid only if certain criteria are met. For one thing, all the studies conducted on the drug must be included — published and unpublished. And all the studies should have been performed in a similar way, using the same protocols, definitions, types of patients and doses. When combining studies with differences, it is necessary first to show that those differences would not affect the analysis, Goodman notes, but that seldom happens. “That’s not a formal part of most meta-analyses,” he says.
  • Meta-analyses have produced many controversial conclusions. Common claims that antidepressants work no better than placebos, for example, are based on meta-analyses that do not conform to the criteria that would confer validity. Similar problems afflicted a 2007 meta-analysis, published in the New England Journal of Medicine, that attributed increased heart attack risk to the diabetes drug Avandia. Raw data from the combined trials showed that only 55 people in 10,000 had heart attacks when using Avandia, compared with 59 people per 10,000 in comparison groups. But after a series of statistical manipulations, Avandia appeared to confer an increased risk.
  • combining small studies in a meta-analysis is not a good substitute for a single trial sufficiently large to test a given question. “Meta-analyses can reduce the role of chance in the interpretation but may introduce bias and confounding,” Hennekens and DeMets write in the Dec. 2 Journal of the American Medical Association. “Such results should be considered more as hypothesis formulating than as hypothesis testing.”
  • Some studies show dramatic effects that don’t require sophisticated statistics to interpret. If the P value is 0.0001 — a hundredth of a percent chance of a fluke — that is strong evidence, Goodman points out. Besides, most well-accepted science is based not on any single study, but on studies that have been confirmed by repetition. Any one result may be likely to be wrong, but confidence rises quickly if that result is independently replicated.“Replication is vital,” says statistician Juliet Shaffer, a lecturer emeritus at the University of California, Berkeley. And in medicine, she says, the need for replication is widely recognized. “But in the social sciences and behavioral sciences, replication is not common,” she noted in San Diego in February at the annual meeting of the American Association for the Advancement of Science. “This is a sad situation.”
  • Most critics of standard statistics advocate the Bayesian approach to statistical reasoning, a methodology that derives from a theorem credited to Bayes, an 18th century English clergyman. His approach uses similar math, but requires the added twist of a “prior probability” — in essence, an informed guess about the expected probability of something in advance of the study. Often this prior probability is more than a mere guess — it could be based, for instance, on previous studies.
  • it basically just reflects the need to include previous knowledge when drawing conclusions from new observations. To infer the odds that a barking dog is hungry, for instance, it is not enough to know how often the dog barks when well-fed. You also need to know how often it eats — in order to calculate the prior probability of being hungry. Bayesian math combines a prior probability with observed data to produce an estimate of the likelihood of the hunger hypothesis. “A scientific hypothesis cannot be properly assessed solely by reference to the observational data,” but only by viewing the data in light of prior belief in the hypothesis, wrote George Diamond and Sanjay Kaul of UCLA’s School of Medicine in 2004 in the Journal of the American College of Cardiology. “Bayes’ theorem is ... a logically consistent, mathematically valid, and intuitive way to draw inferences about the hypothesis.” (See Box 4)
  • In many real-life contexts, Bayesian methods do produce the best answers to important questions. In medical diagnoses, for instance, the likelihood that a test for a disease is correct depends on the prevalence of the disease in the population, a factor that Bayesian math would take into account.
  • But Bayesian methods introduce a confusion into the actual meaning of the mathematical concept of “probability” in the real world. Standard or “frequentist” statistics treat probabilities as objective realities; Bayesians treat probabilities as “degrees of belief” based in part on a personal assessment or subjective decision about what to include in the calculation. That’s a tough placebo to swallow for scientists wedded to the “objective” ideal of standard statistics. “Subjective prior beliefs are anathema to the frequentist, who relies instead on a series of ad hoc algorithms that maintain the facade of scientific objectivity,” Diamond and Kaul wrote.Conflict between frequentists and Bayesians has been ongoing for two centuries. So science’s marriage to mathematics seems to entail some irreconcilable differences. Whether the future holds a fruitful reconciliation or an ugly separation may depend on forging a shared understanding of probability.“What does probability mean in real life?” the statistician David Salsburg asked in his 2001 book The Lady Tasting Tea. “This problem is still unsolved, and ... if it remains un­solved, the whole of the statistical approach to science may come crashing down from the weight of its own inconsistencies.”
  • Weiye Loh on 20 Mar 10
     
    Odds Are, It's Wrong Science fails to face the shortcomings of statistics
Weiye Loh

Can a group of scientists in California end the war on climate change? | Science | The ... - 0 views

www.guardian.co.uk/...entists-end-climate-change-war

Climate Science Academic Research Data Mining Data Open Source Funding ClimateGate Polarisation Statistics

shared by Weiye Loh on 01 Mar 11 - No Cached
  • Muller calls his latest obsession the Berkeley Earth project. The aim is so simple that the complexity and magnitude of the undertaking is easy to miss. Starting from scratch, with new computer tools and more data than has ever been used, they will arrive at an independent assessment of global warming. The team will also make every piece of data it uses – 1.6bn data points – freely available on a website. It will post its workings alongside, including full information on how more than 100 years of data from thousands of instruments around the world are stitched together to give a historic record of the planet's temperature.
  • Muller is fed up with the politicised row that all too often engulfs climate science. By laying all its data and workings out in the open, where they can be checked and challenged by anyone, the Berkeley team hopes to achieve something remarkable: a broader consensus on global warming. In no other field would Muller's dream seem so ambitious, or perhaps, so naive.
  • "We are bringing the spirit of science back to a subject that has become too argumentative and too contentious," Muller says, over a cup of tea. "We are an independent, non-political, non-partisan group. We will gather the data, do the analysis, present the results and make all of it available. There will be no spin, whatever we find." Why does Muller feel compelled to shake up the world of climate change? "We are doing this because it is the most important project in the world today. Nothing else comes close," he says.
  • ...20 more annotations...
  • There are already three heavyweight groups that could be considered the official keepers of the world's climate data. Each publishes its own figures that feed into the UN's Intergovernmental Panel on Climate Change. Nasa's Goddard Institute for Space Studies in New York City produces a rolling estimate of the world's warming. A separate assessment comes from another US agency, the National Oceanic and Atmospheric Administration (Noaa). The third group is based in the UK and led by the Met Office. They all take readings from instruments around the world to come up with a rolling record of the Earth's mean surface temperature. The numbers differ because each group uses its own dataset and does its own analysis, but they show a similar trend. Since pre-industrial times, all point to a warming of around 0.75C.
  • You might think three groups was enough, but Muller rolls out a list of shortcomings, some real, some perceived, that he suspects might undermine public confidence in global warming records. For a start, he says, warming trends are not based on all the available temperature records. The data that is used is filtered and might not be as representative as it could be. He also cites a poor history of transparency in climate science, though others argue many climate records and the tools to analyse them have been public for years.
  • Then there is the fiasco of 2009 that saw roughly 1,000 emails from a server at the University of East Anglia's Climatic Research Unit (CRU) find their way on to the internet. The fuss over the messages, inevitably dubbed Climategate, gave Muller's nascent project added impetus. Climate sceptics had already attacked James Hansen, head of the Nasa group, for making political statements on climate change while maintaining his role as an objective scientist. The Climategate emails fuelled their protests. "With CRU's credibility undergoing a severe test, it was all the more important to have a new team jump in, do the analysis fresh and address all of the legitimate issues raised by sceptics," says Muller.
  • This latest point is where Muller faces his most delicate challenge. To concede that climate sceptics raise fair criticisms means acknowledging that scientists and government agencies have got things wrong, or at least could do better. But the debate around global warming is so highly charged that open discussion, which science requires, can be difficult to hold in public. At worst, criticising poor climate science can be taken as an attack on science itself, a knee-jerk reaction that has unhealthy consequences. "Scientists will jump to the defence of alarmists because they don't recognise that the alarmists are exaggerating," Muller says.
  • The Berkeley Earth project came together more than a year ago, when Muller rang David Brillinger, a statistics professor at Berkeley and the man Nasa called when it wanted someone to check its risk estimates of space debris smashing into the International Space Station. He wanted Brillinger to oversee every stage of the project. Brillinger accepted straight away. Since the first meeting he has advised the scientists on how best to analyse their data and what pitfalls to avoid. "You can think of statisticians as the keepers of the scientific method, " Brillinger told me. "Can scientists and doctors reasonably draw the conclusions they are setting down? That's what we're here for."
  • For the rest of the team, Muller says he picked scientists known for original thinking. One is Saul Perlmutter, the Berkeley physicist who found evidence that the universe is expanding at an ever faster rate, courtesy of mysterious "dark energy" that pushes against gravity. Another is Art Rosenfeld, the last student of the legendary Manhattan Project physicist Enrico Fermi, and something of a legend himself in energy research. Then there is Robert Jacobsen, a Berkeley physicist who is an expert on giant datasets; and Judith Curry, a climatologist at Georgia Institute of Technology, who has raised concerns over tribalism and hubris in climate science.
  • Robert Rohde, a young physicist who left Berkeley with a PhD last year, does most of the hard work. He has written software that trawls public databases, themselves the product of years of painstaking work, for global temperature records. These are compiled, de-duplicated and merged into one huge historical temperature record. The data, by all accounts, are a mess. There are 16 separate datasets in 14 different formats and they overlap, but not completely. Muller likens Rohde's achievement to Hercules's enormous task of cleaning the Augean stables.
  • The wealth of data Rohde has collected so far – and some dates back to the 1700s – makes for what Muller believes is the most complete historical record of land temperatures ever compiled. It will, of itself, Muller claims, be a priceless resource for anyone who wishes to study climate change. So far, Rohde has gathered records from 39,340 individual stations worldwide.
  • Publishing an extensive set of temperature records is the first goal of Muller's project. The second is to turn this vast haul of data into an assessment on global warming.
  • The big three groups – Nasa, Noaa and the Met Office – work out global warming trends by placing an imaginary grid over the planet and averaging temperatures records in each square. So for a given month, all the records in England and Wales might be averaged out to give one number. Muller's team will take temperature records from individual stations and weight them according to how reliable they are.
  • This is where the Berkeley group faces its toughest task by far and it will be judged on how well it deals with it. There are errors running through global warming data that arise from the simple fact that the global network of temperature stations was never designed or maintained to monitor climate change. The network grew in a piecemeal fashion, starting with temperature stations installed here and there, usually to record local weather.
  • Among the trickiest errors to deal with are so-called systematic biases, which skew temperature measurements in fiendishly complex ways. Stations get moved around, replaced with newer models, or swapped for instruments that record in celsius instead of fahrenheit. The times measurements are taken varies, from say 6am to 9pm. The accuracy of individual stations drift over time and even changes in the surroundings, such as growing trees, can shield a station more from wind and sun one year to the next. Each of these interferes with a station's temperature measurements, perhaps making it read too cold, or too hot. And these errors combine and build up.
  • This is the real mess that will take a Herculean effort to clean up. The Berkeley Earth team is using algorithms that automatically correct for some of the errors, a strategy Muller favours because it doesn't rely on human interference. When the team publishes its results, this is where the scrutiny will be most intense.
  • Despite the scale of the task, and the fact that world-class scientific organisations have been wrestling with it for decades, Muller is convinced his approach will lead to a better assessment of how much the world is warming. "I've told the team I don't know if global warming is more or less than we hear, but I do believe we can get a more precise number, and we can do it in a way that will cool the arguments over climate change, if nothing else," says Muller. "Science has its weaknesses and it doesn't have a stranglehold on the truth, but it has a way of approaching technical issues that is a closer approximation of truth than any other method we have."
  • It might not be a good sign that one prominent climate sceptic contacted by the Guardian, Canadian economist Ross McKitrick, had never heard of the project. Another, Stephen McIntyre, whom Muller has defended on some issues, hasn't followed the project either, but said "anything that [Muller] does will be well done". Phil Jones at the University of East Anglia was unclear on the details of the Berkeley project and didn't comment.
  • Elsewhere, Muller has qualified support from some of the biggest names in the business. At Nasa, Hansen welcomed the project, but warned against over-emphasising what he expects to be the minor differences between Berkeley's global warming assessment and those from the other groups. "We have enough trouble communicating with the public already," Hansen says. At the Met Office, Peter Stott, head of climate monitoring and attribution, was in favour of the project if it was open and peer-reviewed.
  • Peter Thorne, who left the Met Office's Hadley Centre last year to join the Co-operative Institute for Climate and Satellites in North Carolina, is enthusiastic about the Berkeley project but raises an eyebrow at some of Muller's claims. The Berkeley group will not be the first to put its data and tools online, he says. Teams at Nasa and Noaa have been doing this for many years. And while Muller may have more data, they add little real value, Thorne says. Most are records from stations installed from the 1950s onwards, and then only in a few regions, such as North America. "Do you really need 20 stations in one region to get a monthly temperature figure? The answer is no. Supersaturating your coverage doesn't give you much more bang for your buck," he says. They will, however, help researchers spot short-term regional variations in climate change, something that is likely to be valuable as climate change takes hold.
  • Despite his reservations, Thorne says climate science stands to benefit from Muller's project. "We need groups like Berkeley stepping up to the plate and taking this challenge on, because it's the only way we're going to move forwards. I wish there were 10 other groups doing this," he says.
  • Muller's project is organised under the auspices of Novim, a Santa Barbara-based non-profit organisation that uses science to find answers to the most pressing issues facing society and to publish them "without advocacy or agenda". Funding has come from a variety of places, including the Fund for Innovative Climate and Energy Research (funded by Bill Gates), and the Department of Energy's Lawrence Berkeley Lab. One donor has had some climate bloggers up in arms: the man behind the Charles G Koch Charitable Foundation owns, with his brother David, Koch Industries, a company Greenpeace called a "kingpin of climate science denial". On this point, Muller says the project has taken money from right and left alike.
  • No one who spoke to the Guardian about the Berkeley Earth project believed it would shake the faith of the minority who have set their minds against global warming. "As new kids on the block, I think they will be given a favourable view by people, but I don't think it will fundamentally change people's minds," says Thorne. Brillinger has reservations too. "There are people you are never going to change. They have their beliefs and they're not going to back away from them."
Weiye Loh

The Death of Postmodernism And Beyond | Philosophy Now - 0 views

www.philosophynow.org/...th_of_Postmodernism_And_Beyond

Postmodernism Foucault Philosophy Modernism Spectacle Reality Reality TV

shared by Weiye Loh on 28 Apr 12 - No Cached
  • Most of the undergraduates who will take ‘Postmodern Fictions’ this year will have been born in 1985 or after, and all but one of the module’s primary texts were written before their lifetime. Far from being ‘contemporary’, these texts were published in another world, before the students were born: The French Lieutenant’s Woman, Nights at the Circus, If on a Winter’s Night a Traveller, Do Androids Dream of Electric Sheep? (and Blade Runner), White Noise: this is Mum and Dad’s culture. Some of the texts (‘The Library of Babel’) were written even before their parents were born. Replace this cache with other postmodern stalwarts – Beloved, Flaubert’s Parrot, Waterland, The Crying of Lot 49, Pale Fire, Slaughterhouse 5, Lanark, Neuromancer, anything by B.S. Johnson – and the same applies. It’s all about as contemporary as The Smiths, as hip as shoulder pads, as happening as Betamax video recorders. These are texts which are just coming to grips with the existence of rock music and television; they mostly do not dream even of the possibility of the technology and communications media – mobile phones, email, the internet, computers in every house powerful enough to put a man on the moon – which today’s undergraduates take for granted.
  • somewhere in the late 1990s or early 2000s, the emergence of new technologies re-structured, violently and forever, the nature of the author, the reader and the text, and the relationships between them.
  • Postmodernism, like modernism and romanticism before it, fetishised [ie placed supreme importance on] the author, even when the author chose to indict or pretended to abolish him or herself. But the culture we have now fetishises the recipient of the text to the degree that they become a partial or whole author of it. Optimists may see this as the democratisation of culture; pessimists will point to the excruciating banality and vacuity of the cultural products thereby generated (at least so far).
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  • Pseudo-modernism also encompasses contemporary news programmes, whose content increasingly consists of emails or text messages sent in commenting on the news items. The terminology of ‘interactivity’ is equally inappropriate here, since there is no exchange: instead, the viewer or listener enters – writes a segment of the programme – then departs, returning to a passive role. Pseudo-modernism also includes computer games, which similarly place the individual in a context where they invent the cultural content, within pre-delineated limits. The content of each individual act of playing the game varies according to the particular player.
  • The pseudo-modern cultural phenomenon par excellence is the internet. Its central act is that of the individual clicking on his/her mouse to move through pages in a way which cannot be duplicated, inventing a pathway through cultural products which has never existed before and never will again. This is a far more intense engagement with the cultural process than anything literature can offer, and gives the undeniable sense (or illusion) of the individual controlling, managing, running, making up his/her involvement with the cultural product. Internet pages are not ‘authored’ in the sense that anyone knows who wrote them, or cares. The majority either require the individual to make them work, like Streetmap or Route Planner, or permit him/her to add to them, like Wikipedia, or through feedback on, for instance, media websites. In all cases, it is intrinsic to the internet that you can easily make up pages yourself (eg blogs).
  • Where once special effects were supposed to make the impossible appear credible, CGI frequently [inadvertently] works to make the possible look artificial, as in much of Lord of the Rings or Gladiator. Battles involving thousands of individuals have really happened; pseudo-modern cinema makes them look as if they have only ever happened in cyberspace.
  • Similarly, television in the pseudo-modern age favours not only reality TV (yet another unapt term), but also shopping channels, and quizzes in which the viewer calls to guess the answer to riddles in the hope of winning money.
  • The purely ‘spectacular’ function of television, as with all the arts, has become a marginal one: what is central now is the busy, active, forging work of the individual who would once have been called its recipient. In all of this, the ‘viewer’ feels powerful and is indeed necessary; the ‘author’ as traditionally understood is either relegated to the status of the one who sets the parameters within which others operate, or becomes simply irrelevant, unknown, sidelined; and the ‘text’ is characterised both by its hyper-ephemerality and by its instability. It is made up by the ‘viewer’, if not in its content then in its sequence – you wouldn’t read Middlemarch by going from page 118 to 316 to 401 to 501, but you might well, and justifiably, read Ceefax that way.
  • A pseudo-modern text lasts an exceptionally brief time. Unlike, say, Fawlty Towers, reality TV programmes cannot be repeated in their original form, since the phone-ins cannot be reproduced, and without the possibility of phoning-in they become a different and far less attractive entity.
  • If scholars give the date they referenced an internet page, it is because the pages disappear or get radically re-cast so quickly. Text messages and emails are extremely difficult to keep in their original form; printing out emails does convert them into something more stable, like a letter, but only by destroying their essential, electronic state.
  • The cultural products of pseudo-modernism are also exceptionally banal
  • Much text messaging and emailing is vapid in comparison with what people of all educational levels used to put into letters.
  • A triteness, a shallowness dominates all.
  • In music, the pseudo-modern supersedingof the artist-dominated album as monolithic text by the downloading and mix-and-matching of individual tracks on to an iPod, selected by the listener, was certainly prefigured by the music fan’s creation of compilation tapes a generation ago. But a shift has occurred, in that what was a marginal pastime of the fan has become the dominant and definitive way of consuming music, rendering the idea of the album as a coherent work of art, a body of integrated meaning, obsolete.
  • To a degree, pseudo-modernism is no more than a technologically motivated shift to the cultural centre of something which has always existed (similarly, metafiction has always existed, but was never so fetishised as it was by postmodernism). Television has always used audience participation, just as theatre and other performing arts did before it; but as an option, not as a necessity: pseudo-modern TV programmes have participation built into them.
  • Whereas postmodernism called ‘reality’ into question, pseudo-modernism defines the real implicitly as myself, now, ‘interacting’ with its texts. Thus, pseudo-modernism suggests that whatever it does or makes is what is reality, and a pseudo-modern text may flourish the apparently real in an uncomplicated form: the docu-soap with its hand-held cameras (which, by displaying individuals aware of being regarded, give the viewer the illusion of participation); The Office and The Blair Witch Project, interactive pornography and reality TV; the essayistic cinema of Michael Moore or Morgan Spurlock.
  • whereas postmodernism favoured the ironic, the knowing and the playful, with their allusions to knowledge, history and ambivalence, pseudo-modernism’s typical intellectual states are ignorance, fanaticism and anxiety
  • pseudo-modernism lashes fantastically sophisticated technology to the pursuit of medieval barbarism – as in the uploading of videos of beheadings onto the internet, or the use of mobile phones to film torture in prisons. Beyond this, the destiny of everyone else is to suffer the anxiety of getting hit in the cross-fire. But this fatalistic anxiety extends far beyond geopolitics, into every aspect of contemporary life; from a general fear of social breakdown and identity loss, to a deep unease about diet and health; from anguish about the destructiveness of climate change, to the effects of a new personal ineptitude and helplessness, which yield TV programmes about how to clean your house, bring up your children or remain solvent.
  • Pseudo-modernism belongs to a world pervaded by the encounter between a religiously fanatical segment of the United States, a largely secular but definitionally hyper-religious Israel, and a fanatical sub-section of Muslims scattered across the planet: pseudo-modernism was not born on 11 September 2001, but postmodernism was interred in its rubble.
  • pseudo-modernist communicates constantly with the other side of the planet, yet needs to be told to eat vegetables to be healthy, a fact self-evident in the Bronze Age. He or she can direct the course of national television programmes, but does not know how to make him or herself something to eat – a characteristic fusion of the childish and the advanced, the powerful and the helpless. For varying reasons, these are people incapable of the “disbelief of Grand Narratives” which Lyotard argued typified postmodernists
  • Weiye Loh on 28 Apr 12
     
    Postmodern philosophy emphasises the elusiveness of meaning and knowledge. This is often expressed in postmodern art as a concern with representation and an ironic self-awareness. And the argument that postmodernism is over has already been made philosophically. There are people who have essentially asserted that for a while we believed in postmodern ideas, but not any more, and from now on we're going to believe in critical realism. The weakness in this analysis is that it centres on the academy, on the practices and suppositions of philosophers who may or may not be shifting ground or about to shift - and many academics will simply decide that, finally, they prefer to stay with Foucault [arch postmodernist] than go over to anything else. However, a far more compelling case can be made that postmodernism is dead by looking outside the academy at current cultural production.
Jody Poh

Subtitles, Lip Synching and Covers on YouTube - 13 views

I think that companies concerned over this issue due to the loss of potential income constitutes egoism. They mainly want to defend their interests without considering the beneficial impact of the ...

copyright youtube parody

started by Ang Yao Zong on 25 Aug 09 1 follow-up, last by Jody Poh on 25 Aug 09
Weiye Loh

The Fake Scandal of Climategate - 0 views

www.skepticalscience.com/fake-scandal-Climategate.html

Science Internet Climate Science ClimateGate

shared by Weiye Loh on 21 Nov 10 - No Cached
  • The most comprehensive inquiry was the Independent Climate Change Email Review led by Sir Muir Russell, commissioned by UEA to examine the behaviour of the CRU scientists (but not the scientific validity of their work). It published its final report in July 2010
  • It focused on what the CRU scientists did, not what they said, investigating the evidence for and against each allegation. It interviewed CRU and UEA staff, and took 111 submissions including one from CRU itself. And it also did something the media completely failed to do: it attempted to put the actions of CRU scientists into context.
    • Weiye Loh
      Weiye Loh on 21 Nov 10
       
      Data, in the form of email correspondence, requires context to be interpreted "objectively" and "accurately" =)
  • The Review went back to primary sources to see if CRU really was hiding or falsifying their data. It considered how much CRU’s actions influenced the IPCC’s conclusions about temperatures during the past millennium. It commissioned a paper by Dr Richard Horton, editor of The Lancet, on the context of scientific peer review. And it asked IPCC Review Editors how much influence individuals could wield on writing groups.
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  • Many of these are things any journalist could have done relatively easily, but few ever bothered to do.
  • the emergence of the blogosphere requires significantly more openness from scientists. However, providing the details necessary to validate large datasets can be difficult and time-consuming, and how FoI laws apply to research is still an evolving area. Meanwhile, the public needs to understand that science cannot and does not produce absolutely precise answers. Though the uncertainties may become smaller and better constrained over time, uncertainty in science is a fact of life which policymakers have to deal with. The chapter concludes: “the Review would urge all scientists to learn to communicate their work in ways that the public can access and understand”.
  • email is less formal than other forms of communication: “Extreme forms of language are frequently applied to quite normal situations by people who would never use it in other communication channels.” The CRU scientists assumed their emails to be private, so they used “slang, jargon and acronyms” which would have been more fully explained had they been talking to the public. And although some emails suggest CRU went out of their way to make life difficult for their critics, there are others which suggest they were bending over backwards to be honest. Therefore the Review found “the e-mails cannot always be relied upon as evidence of what actually occurred, nor indicative of actual behaviour that is extreme, exceptional or unprofessional.” [section 4.3]
  • when put into the proper context, what do these emails actually reveal about the behaviour of the CRU scientists? The report concluded (its emphasis):
  • we find that their rigour and honesty as scientists are not in doubt.
  • we did not find any evidence of behaviour that might undermine the conclusions of the IPCC assessments.
  • “But we do find that there has been a consistent pattern of failing to display the proper degree of openness, both on the part of the CRU scientists and on the part of the UEA, who failed to recognize not only the significance of statutory requirements but also the risk to the reputation of the University and indeed, to the credibility of UK climate science.” [1.3]
  • The argument that Climategate reveals an international climate science conspiracy is not really a very skeptical one. Sure, it is skeptical in the weak sense of questioning authority, but it stops there. Unlike true skepticism, it doesn’t go on to objectively examine all the evidence and draw a conclusion based on that evidence. Instead, it cherry-picks suggestive emails, seeing everything as incontrovertible evidence of a conspiracy, and concludes all of mainstream climate science is guilty by association. This is not skepticism; this is conspiracy theory.
    • Weiye Loh
      Weiye Loh on 21 Nov 10
       
      How then do we know that we have examined ALL the evidence? What about the context of evidence then? 
  • The media dropped the ball There is a famous quotation attributed to Mark Twain: “A lie can travel halfway around the world while the truth is putting on its shoes.” This is more true in the internet age than it was when Mark Twain was alive. Unfortunately, it took months for the Climategate inquiries to put on their shoes, and by the time they reported, the damage had already been done. The media acted as an uncritical loudspeaker for the initial allegations, which will now continue to circulate around the world forever, then failed to give anywhere near the same amount of coverage to the inquiries clearing the scientists involved. For instance, Rupert Murdoch’s The Australian published no less than 85 stories about Climategate, but not one about the Muir Russell inquiry.
  • Even the Guardian, who have a relatively good track record on environmental reporting and were quick to criticize the worst excesses of climate conspiracy theorists, could not resist the lure of stolen emails. As George Monbiot writes, journalists see FoI requests and email hacking as a way of keeping people accountable, rather than the distraction from actual science which they are to scientists. In contrast, CRU director Phil Jones says: “I wish people would spend as much time reading my scientific papers as they do reading my e-mails.”
  • This is part of a broader problem with climate change reporting: the media holds scientists to far higher standards than it does contrarians. Climate scientists have to be right 100% of the time, but contrarians apparently can get away with being wrong nearly 100% of the time. The tiniest errors of climate scientists are nitpicked and blown out of all proportion, but contrarians get away with monstrous distortions and cherry-picking of evidence. Around the same time The Australian was bashing climate scientists, the same newspaper had no problem publishing Viscount Monckton’s blatant misrepresentations of IPCC projections (not to mention his demonstrably false conspiracy theory that the Copenhagen summit was a plot to establish a world government).
  • In the current model of environmental reporting, the contrarians do not lose anything by making baseless accusations. In fact, it is in their interests to throw as much mud at scientists as possible to increase the chance that some of it will stick in the public consciousness. But there is untold damage to the reputation of the scientists against whom the accusations are being made. We can only hope that in future the media will be less quick to jump to conclusions. If only editors and producers would stop and think for a moment about what they’re doing: they are playing with the future of the planet.
  • As worthy as this defense is, surely this is the kind of political bun-fight SkS has resolutely stayed away from since its inception. The debate can only become a quagmire of competing claims, because this is part of an adversarial process that does not depend on, or even require, scientific evidence. Only by sticking resolutely to the science and the advocacy of the scientific method can SkS continue to avoid being drowned in the kind of mud through which we are obliged to wade elsewhere.
  • I disagree with gp. It is past time we all got angry, very angry, at what these people have done and continue to do. Dispassionate science doesn't cut it with the denial industry or with the media (and that "or" really isn't there). It's time to fight back with everything we can throw back at them.
  • The fact that three quick fire threads have been run on Climatgate on this excellent blog in the last few days is an indication that Climategate (fairly or not) has does serious damage to the cause of AGW activism. Mass media always overshoots and exaggerates. The AGW alarmists had a very good run - here in Australia protagonists like Tim Flannery and our living science legend Robin Williams were talking catastrophe - the 10 year drought was definitely permanent climate change - rivers might never run again - Robin (100 metre sea level rise) Williams refused to even read the Climategate emails. Climategate swung the pendumum to the other extreme - the scientists (nearly all funded by you and me) were under the pump. Their socks rubbed harder on their sandals as they scrambled for clear air. Cries about criminal hackers funded by big oil, tobacco, rightist conspirators etc were heard. Panchuri cried 'voodoo science' as he denied ever knowing about objections to the preposterous 2035 claim. How things change in a year. The drought is broken over most of Australia - Tim Flannery has gone quiet and Robin Williams is airing a science journo who says that AGW scares have been exaggerated. Some balance might have been restored as the pendulum swung, and our hard working misunderstood scientist bretheren will take more care with their emails in future.
  • "Perhaps a more precise description would be that a common pattern in global warming skeptic arguments is to focus on narrow pieces of evidence while ignoring other evidence that contradicts their argument." And this is the issue the article discuss, but in my opinion this article is in guilt of this as well. It focus on a narrow set of non representative claims, claims which is indeed pure propaganda by some skeptics, however the article also suggest guilt buy association and as such these propaganda claims then gets attributed to the be opinions of the entire skeptic camp. In doing so, the OP becomes guilty of the very same issue the OP tries to address. In other words, the issue I try to raise is not about the exact numbers or figures or any particular facts but the fact that the claim I quoted is obvious nonsense. It is nonsense because it a sweeping statement with no specifics and as such it is an empty statement and means nothing. A second point I been thinking about when reading this article is why should scientist be granted immunity to dirty tricks/propaganda in a political debate? Is it because they speak under the name of science? If that is the case, why shall we not grant the same right to other spokesmen for other organization?
    • Weiye Loh
      Weiye Loh on 21 Nov 10
       
      The aspiration to examine ALL evidence is again called into question here. Is it really possible to examine ALL evidence? Even if we have examined them, can we fully represent our examination? From our lab, to the manuscript, to the journal paper, to the news article, to 140characters tweets?
Weiye Loh

How the Internet Gets Inside Us : The New Yorker - 0 views

www.newyorker.com/...110214crat_atlarge_gopnik

Technology Internet Tech-Social Deterninisn

shared by Weiye Loh on 25 Feb 11 - No Cached
  • N.Y.U. professor Clay Shirky—the author of “Cognitive Surplus” and many articles and blog posts proclaiming the coming of the digital millennium—is the breeziest and seemingly most self-confident
  • Shirky believes that we are on the crest of an ever-surging wave of democratized information: the Gutenberg printing press produced the Reformation, which produced the Scientific Revolution, which produced the Enlightenment, which produced the Internet, each move more liberating than the one before.
  • The idea, for instance, that the printing press rapidly gave birth to a new order of information, democratic and bottom-up, is a cruel cartoon of the truth. If the printing press did propel the Reformation, one of the biggest ideas it propelled was Luther’s newly invented absolutist anti-Semitism. And what followed the Reformation wasn’t the Enlightenment, a new era of openness and freely disseminated knowledge. What followed the Reformation was, actually, the Counter-Reformation, which used the same means—i.e., printed books—to spread ideas about what jerks the reformers were, and unleashed a hundred years of religious warfare.
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  • If ideas of democracy and freedom emerged at the end of the printing-press era, it wasn’t by some technological logic but because of parallel inventions, like the ideas of limited government and religious tolerance, very hard won from history.
  • As Andrew Pettegree shows in his fine new study, “The Book in the Renaissance,” the mainstay of the printing revolution in seventeenth-century Europe was not dissident pamphlets but royal edicts, printed by the thousand: almost all the new media of that day were working, in essence, for kinglouis.gov.
  • Even later, full-fledged totalitarian societies didn’t burn books. They burned some books, while keeping the printing presses running off such quantities that by the mid-fifties Stalin was said to have more books in print than Agatha Christie.
  • Many of the more knowing Never-Betters turn for cheer not to messy history and mixed-up politics but to psychology—to the actual expansion of our minds.
  • The argument, advanced in Andy Clark’s “Supersizing the Mind” and in Robert K. Logan’s “The Sixth Language,” begins with the claim that cognition is not a little processing program that takes place inside your head, Robby the Robot style. It is a constant flow of information, memory, plans, and physical movements, in which as much thinking goes on out there as in here. If television produced the global village, the Internet produces the global psyche: everyone keyed in like a neuron, so that to the eyes of a watching Martian we are really part of a single planetary brain. Contraptions don’t change consciousness; contraptions are part of consciousness. We may not act better than we used to, but we sure think differently than we did.
  • Cognitive entanglement, after all, is the rule of life. My memories and my wife’s intermingle. When I can’t recall a name or a date, I don’t look it up; I just ask her. Our machines, in this way, become our substitute spouses and plug-in companions.
  • But, if cognitive entanglement exists, so does cognitive exasperation. Husbands and wives deny each other’s memories as much as they depend on them. That’s fine until it really counts (say, in divorce court). In a practical, immediate way, one sees the limits of the so-called “extended mind” clearly in the mob-made Wikipedia, the perfect product of that new vast, supersized cognition: when there’s easy agreement, it’s fine, and when there’s widespread disagreement on values or facts, as with, say, the origins of capitalism, it’s fine, too; you get both sides. The trouble comes when one side is right and the other side is wrong and doesn’t know it. The Shakespeare authorship page and the Shroud of Turin page are scenes of constant conflict and are packed with unreliable information. Creationists crowd cyberspace every bit as effectively as evolutionists, and extend their minds just as fully. Our trouble is not the over-all absence of smartness but the intractable power of pure stupidity, and no machine, or mind, seems extended enough to cure that.
  • Nicholas Carr, in “The Shallows,” William Powers, in “Hamlet’s BlackBerry,” and Sherry Turkle, in “Alone Together,” all bear intimate witness to a sense that the newfound land, the ever-present BlackBerry-and-instant-message world, is one whose price, paid in frayed nerves and lost reading hours and broken attention, is hardly worth the gains it gives us. “The medium does matter,” Carr has written. “As a technology, a book focuses our attention, isolates us from the myriad distractions that fill our everyday lives. A networked computer does precisely the opposite. It is designed to scatter our attention. . . . Knowing that the depth of our thought is tied directly to the intensity of our attentiveness, it’s hard not to conclude that as we adapt to the intellectual environment of the Net our thinking becomes shallower.
  • Carr is most concerned about the way the Internet breaks down our capacity for reflective thought.
  • Powers’s reflections are more family-centered and practical. He recounts, very touchingly, stories of family life broken up by the eternal consultation of smartphones and computer monitors
  • He then surveys seven Wise Men—Plato, Thoreau, Seneca, the usual gang—who have something to tell us about solitude and the virtues of inner space, all of it sound enough, though he tends to overlook the significant point that these worthies were not entirely in favor of the kinds of liberties that we now take for granted and that made the new dispensation possible.
  • Similarly, Nicholas Carr cites Martin Heidegger for having seen, in the mid-fifties, that new technologies would break the meditational space on which Western wisdoms depend. Since Heidegger had not long before walked straight out of his own meditational space into the arms of the Nazis, it’s hard to have much nostalgia for this version of the past. One feels the same doubts when Sherry Turkle, in “Alone Together,” her touching plaint about the destruction of the old intimacy-reading culture by the new remote-connection-Internet culture, cites studies that show a dramatic decline in empathy among college students, who apparently are “far less likely to say that it is valuable to put oneself in the place of others or to try and understand their feelings.” What is to be done?
  • Among Ever-Wasers, the Harvard historian Ann Blair may be the most ambitious. In her book “Too Much to Know: Managing Scholarly Information Before the Modern Age,” she makes the case that what we’re going through is like what others went through a very long while ago. Against the cartoon history of Shirky or Tooby, Blair argues that the sense of “information overload” was not the consequence of Gutenberg but already in place before printing began. She wants us to resist “trying to reduce the complex causal nexus behind the transition from Renaissance to Enlightenment to the impact of a technology or any particular set of ideas.” Anyway, the crucial revolution was not of print but of paper: “During the later Middle Ages a staggering growth in the production of manuscripts, facilitated by the use of paper, accompanied a great expansion of readers outside the monastic and scholastic contexts.” For that matter, our minds were altered less by books than by index slips. Activities that seem quite twenty-first century, she shows, began when people cut and pasted from one manuscript to another; made aggregated news in compendiums; passed around précis. “Early modern finding devices” were forced into existence: lists of authorities, lists of headings.
  • Everyone complained about what the new information technologies were doing to our minds. Everyone said that the flood of books produced a restless, fractured attention. Everyone complained that pamphlets and poems were breaking kids’ ability to concentrate, that big good handmade books were ignored, swept aside by printed works that, as Erasmus said, “are foolish, ignorant, malignant, libelous, mad.” The reader consulting a card catalogue in a library was living a revolution as momentous, and as disorienting, as our own.
  • The book index was the search engine of its era, and needed to be explained at length to puzzled researchers
  • That uniquely evil and necessary thing the comprehensive review of many different books on a related subject, with the necessary oversimplification of their ideas that it demanded, was already around in 1500, and already being accused of missing all the points. In the period when many of the big, classic books that we no longer have time to read were being written, the general complaint was that there wasn’t enough time to read big, classic books.
  • at any given moment, our most complicated machine will be taken as a model of human intelligence, and whatever media kids favor will be identified as the cause of our stupidity. When there were automatic looms, the mind was like an automatic loom; and, since young people in the loom period liked novels, it was the cheap novel that was degrading our minds. When there were telephone exchanges, the mind was like a telephone exchange, and, in the same period, since the nickelodeon reigned, moving pictures were making us dumb. When mainframe computers arrived and television was what kids liked, the mind was like a mainframe and television was the engine of our idiocy. Some machine is always showing us Mind; some entertainment derived from the machine is always showing us Non-Mind.
Weiye Loh

Rationally Speaking: The problem of replicability in science - 0 views

rationallyspeaking.blogspot.com/...-replicability-in-science.html

Truth Science Evidence Data

shared by Weiye Loh on 05 Jan 11 - No Cached
  • The problem of replicability in science from xkcdby Massimo Pigliucci
  • In recent months much has been written about the apparent fact that a surprising, indeed disturbing, number of scientific findings cannot be replicated, or when replicated the effect size turns out to be much smaller than previously thought.
  • Arguably, the recent streak of articles on this topic began with one penned by David Freedman in The Atlantic, and provocatively entitled “Lies, Damned Lies, and Medical Science.” In it, the major character was John Ioannidis, the author of some influential meta-studies about the low degree of replicability and high number of technical flaws in a significant portion of published papers in the biomedical literature.
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  • As Freedman put it in The Atlantic: “80 percent of non-randomized studies (by far the most common type) turn out to be wrong, as do 25 percent of supposedly gold-standard randomized trials, and as much as 10 percent of the platinum-standard large randomized trials.” Ioannidis himself was quoted uttering some sobering words for the medical community (and the public at large): “Science is a noble endeavor, but it’s also a low-yield endeavor. I’m not sure that more than a very small percentage of medical research is ever likely to lead to major improvements in clinical outcomes and quality of life. We should be very comfortable with that fact.”
  • Julia and I actually addressed this topic during a Rationally Speaking podcast, featuring as guest our friend Steve Novella, of Skeptics’ Guide to the Universe and Science-Based Medicine fame. But while Steve did quibble with the tone of the Atlantic article, he agreed that Ioannidis’ results are well known and accepted by the medical research community. Steve did point out that it should not be surprising that results get better and better as one moves toward more stringent protocols like large randomized trials, but it seems to me that one should be surprised (actually, appalled) by the fact that even there the percentage of flawed studies is high — not to mention the fact that most studies are in fact neither large nor properly randomized.
  • The second big recent blow to public perception of the reliability of scientific results is an article published in The New Yorker by Jonah Lehrer, entitled “The truth wears off.” Lehrer also mentions Ioannidis, but the bulk of his essay is about findings in psychiatry, psychology and evolutionary biology (and even in research on the paranormal!).
  • In these disciplines there are now several documented cases of results that were initially spectacularly positive — for instance the effects of second generation antipsychotic drugs, or the hypothesized relationship between a male’s body symmetry and the quality of his genes — that turned out to be increasingly difficult to replicate over time, with the original effect sizes being cut down dramatically, or even disappearing altogether.
  • As Lehrer concludes at the end of his article: “Such anomalies demonstrate the slipperiness of empiricism. Although many scientific ideas generate conflicting results and suffer from falling effect sizes, they continue to get cited in the textbooks and drive standard medical practice. Why? Because these ideas seem true. Because they make sense. Because we can’t bear to let them go. And this is why the decline effect is so troubling.”
  • None of this should actually be particularly surprising to any practicing scientist. If you have spent a significant time of your life in labs and reading the technical literature, you will appreciate the difficulties posed by empirical research, not to mention a number of issues such as the fact that few scientists ever actually bother to replicate someone else’s results, for the simple reason that there is no Nobel (or even funded grant, or tenured position) waiting for the guy who arrived second.
  • n the midst of this I was directed by a tweet by my colleague Neil deGrasse Tyson (who has also appeared on the RS podcast, though in a different context) to a recent ABC News article penned by John Allen Paulos, which meant to explain the decline effect in science.
  • Paulos’ article is indeed concise and on the mark (though several of the explanations he proposes were already brought up in both the Atlantic and New Yorker essays), but it doesn’t really make things much better.
  • Paulos suggests that one explanation for the decline effect is the well known statistical phenomenon of the regression toward the mean. This phenomenon is responsible, among other things, for a fair number of superstitions: you’ve probably heard of some athletes’ and other celebrities’ fear of being featured on the cover of a magazine after a particularly impressive series of accomplishments, because this brings “bad luck,” meaning that the following year one will not be able to repeat the performance at the same level. This is actually true, not because of magical reasons, but simply as a result of the regression to the mean: extraordinary performances are the result of a large number of factors that have to line up just right for the spectacular result to be achieved. The statistical chances of such an alignment to repeat itself are low, so inevitably next year’s performance will likely be below par. Paulos correctly argues that this also explains some of the decline effect of scientific results: the first discovery might have been the result of a number of factors that are unlikely to repeat themselves in exactly the same way, thus reducing the effect size when the study is replicated.
  • nother major determinant of the unreliability of scientific results mentioned by Paulos is the well know problem of publication bias: crudely put, science journals (particularly the high-profile ones, like Nature and Science) are interested only in positive, spectacular, “sexy” results. Which creates a powerful filter against negative, or marginally significant results. What you see in science journals, in other words, isn’t a statistically representative sample of scientific results, but a highly biased one, in favor of positive outcomes. No wonder that when people try to repeat the feat they often come up empty handed.
  • A third cause for the problem, not mentioned by Paulos but addressed in the New Yorker article, is the selective reporting of results by scientists themselves. This is essentially the same phenomenon as the publication bias, except that this time it is scientists themselves, not editors and reviewers, who don’t bother to submit for publication results that are either negative or not strongly conclusive. Again, the outcome is that what we see in the literature isn’t all the science that we ought to see. And it’s no good to argue that it is the “best” science, because the quality of scientific research is measured by the appropriateness of the experimental protocols (including the use of large samples) and of the data analyses — not by whether the results happen to confirm the scientist’s favorite theory.
  • The conclusion of all this is not, of course, that we should throw the baby (science) out with the bath water (bad or unreliable results). But scientists should also be under no illusion that these are rare anomalies that do not affect scientific research at large. Too much emphasis is being put on the “publish or perish” culture of modern academia, with the result that graduate students are explicitly instructed to go for the SPU’s — Smallest Publishable Units — when they have to decide how much of their work to submit to a journal. That way they maximize the number of their publications, which maximizes the chances of landing a postdoc position, and then a tenure track one, and then of getting grants funded, and finally of getting tenure. The result is that, according to statistics published by Nature, it turns out that about ⅓ of published studies is never cited (not to mention replicated!).
  • “Scientists these days tend to keep up the polite fiction that all science is equal. Except for the work of the misguided opponent whose arguments we happen to be refuting at the time, we speak as though every scientist’s field and methods of study are as good as every other scientist’s, and perhaps a little better. This keeps us all cordial when it comes to recommending each other for government grants. ... We speak piously of taking measurements and making small studies that will ‘add another brick to the temple of science.’ Most such bricks lie around the brickyard.”
    • Weiye Loh
      Weiye Loh on 05 Jan 11
       
      Written by John Platt in a "Science" article published in 1964
  • Most damning of all, however, is the potential effect that all of this may have on science’s already dubious reputation with the general public (think evolution-creation, vaccine-autism, or climate change)
  • “If we don’t tell the public about these problems, then we’re no better than non-scientists who falsely claim they can heal. If the drugs don’t work and we’re not sure how to treat something, why should we claim differently? Some fear that there may be less funding because we stop claiming we can prove we have miraculous treatments. But if we can’t really provide those miracles, how long will we be able to fool the public anyway? The scientific enterprise is probably the most fantastic achievement in human history, but that doesn’t mean we have a right to overstate what we’re accomplishing.”
  • Joseph T. Lapp said... But is any of this new for science? Perhaps science has operated this way all along, full of fits and starts, mostly duds. How do we know that this isn't the optimal way for science to operate?My issues are with the understanding of science that high school graduates have, and with the reporting of science.
    • Weiye Loh
      Weiye Loh on 05 Jan 11
       
      It's the media at fault again.
  • What seems to have emerged in recent decades is a change in the institutional setting that got science advancing spectacularly since the establishment of the Royal Society. Flaws in the system such as corporate funded research, pal-review instead of peer-review, publication bias, science entangled with policy advocacy, and suchlike, may be distorting the environment, making it less suitable for the production of good science, especially in some fields.
  • Remedies should exist, but they should evolve rather than being imposed on a reluctant sociological-economic science establishment driven by powerful motives such as professional advance or funding. After all, who or what would have the authority to impose those rules, other than the scientific establishment itself?
Weiye Loh

Edge: HOW DOES OUR LANGUAGE SHAPE THE WAY WE THINK? By Lera Boroditsky - 0 views

www.edge.org/...boroditsky09_index.html

Language Sapir-Whorf Thesis Social Construction

shared by Weiye Loh on 22 Apr 11 - Cached
  • Do the languages we speak shape the way we see the world, the way we think, and the way we live our lives? Do people who speak different languages think differently simply because they speak different languages? Does learning new languages change the way you think? Do polyglots think differently when speaking different languages?
  • For a long time, the idea that language might shape thought was considered at best untestable and more often simply wrong. Research in my labs at Stanford University and at MIT has helped reopen this question. We have collected data around the world: from China, Greece, Chile, Indonesia, Russia, and Aboriginal Australia.
  • What we have learned is that people who speak different languages do indeed think differently and that even flukes of grammar can profoundly affect how we see the world.
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  • Suppose you want to say, "Bush read Chomsky's latest book." Let's focus on just the verb, "read." To say this sentence in English, we have to mark the verb for tense; in this case, we have to pronounce it like "red" and not like "reed." In Indonesian you need not (in fact, you can't) alter the verb to mark tense. In Russian you would have to alter the verb to indicate tense and gender. So if it was Laura Bush who did the reading, you'd use a different form of the verb than if it was George. In Russian you'd also have to include in the verb information about completion. If George read only part of the book, you'd use a different form of the verb than if he'd diligently plowed through the whole thing. In Turkish you'd have to include in the verb how you acquired this information: if you had witnessed this unlikely event with your own two eyes, you'd use one verb form, but if you had simply read or heard about it, or inferred it from something Bush said, you'd use a different verb form.
  • Clearly, languages require different things of their speakers. Does this mean that the speakers think differently about the world? Do English, Indonesian, Russian, and Turkish speakers end up attending to, partitioning, and remembering their experiences differently just because they speak different languages?
  • For some scholars, the answer to these questions has been an obvious yes. Just look at the way people talk, they might say. Certainly, speakers of different languages must attend to and encode strikingly different aspects of the world just so they can use their language properly. Scholars on the other side of the debate don't find the differences in how people talk convincing. All our linguistic utterances are sparse, encoding only a small part of the information we have available. Just because English speakers don't include the same information in their verbs that Russian and Turkish speakers do doesn't mean that English speakers aren't paying attention to the same things; all it means is that they're not talking about them. It's possible that everyone thinks the same way, notices the same things, but just talks differently.
  • Believers in cross-linguistic differences counter that everyone does not pay attention to the same things: if everyone did, one might think it would be easy to learn to speak other languages. Unfortunately, learning a new language (especially one not closely related to those you know) is never easy; it seems to require paying attention to a new set of distinctions. Whether it's distinguishing modes of being in Spanish, evidentiality in Turkish, or aspect in Russian, learning to speak these languages requires something more than just learning vocabulary: it requires paying attention to the right things in the world so that you have the correct information to include in what you say.
  • Follow me to Pormpuraaw, a small Aboriginal community on the western edge of Cape York, in northern Australia. I came here because of the way the locals, the Kuuk Thaayorre, talk about space. Instead of words like "right," "left," "forward," and "back," which, as commonly used in English, define space relative to an observer, the Kuuk Thaayorre, like many other Aboriginal groups, use cardinal-direction terms — north, south, east, and west — to define space.1 This is done at all scales, which means you have to say things like "There's an ant on your southeast leg" or "Move the cup to the north northwest a little bit." One obvious consequence of speaking such a language is that you have to stay oriented at all times, or else you cannot speak properly. The normal greeting in Kuuk Thaayorre is "Where are you going?" and the answer should be something like " Southsoutheast, in the middle distance." If you don't know which way you're facing, you can't even get past "Hello."
  • The result is a profound difference in navigational ability and spatial knowledge between speakers of languages that rely primarily on absolute reference frames (like Kuuk Thaayorre) and languages that rely on relative reference frames (like English).2 Simply put, speakers of languages like Kuuk Thaayorre are much better than English speakers at staying oriented and keeping track of where they are, even in unfamiliar landscapes or inside unfamiliar buildings. What enables them — in fact, forces them — to do this is their language. Having their attention trained in this way equips them to perform navigational feats once thought beyond human capabilities. Because space is such a fundamental domain of thought, differences in how people think about space don't end there. People rely on their spatial knowledge to build other, more complex, more abstract representations. Representations of such things as time, number, musical pitch, kinship relations, morality, and emotions have been shown to depend on how we think about space. So if the Kuuk Thaayorre think differently about space, do they also think differently about other things, like time? This is what my collaborator Alice Gaby and I came to Pormpuraaw to find out.
  • To test this idea, we gave people sets of pictures that showed some kind of temporal progression (e.g., pictures of a man aging, or a crocodile growing, or a banana being eaten). Their job was to arrange the shuffled photos on the ground to show the correct temporal order. We tested each person in two separate sittings, each time facing in a different cardinal direction. If you ask English speakers to do this, they'll arrange the cards so that time proceeds from left to right. Hebrew speakers will tend to lay out the cards from right to left, showing that writing direction in a language plays a role.3 So what about folks like the Kuuk Thaayorre, who don't use words like "left" and "right"? What will they do? The Kuuk Thaayorre did not arrange the cards more often from left to right than from right to left, nor more toward or away from the body. But their arrangements were not random: there was a pattern, just a different one from that of English speakers. Instead of arranging time from left to right, they arranged it from east to west. That is, when they were seated facing south, the cards went left to right. When they faced north, the cards went from right to left. When they faced east, the cards came toward the body and so on. This was true even though we never told any of our subjects which direction they faced. The Kuuk Thaayorre not only knew that already (usually much better than I did), but they also spontaneously used this spatial orientation to construct their representations of time.
  • I have described how languages shape the way we think about space, time, colors, and objects. Other studies have found effects of language on how people construe events, reason about causality, keep track of number, understand material substance, perceive and experience emotion, reason about other people's minds, choose to take risks, and even in the way they choose professions and spouses.8 Taken together, these results show that linguistic processes are pervasive in most fundamental domains of thought, unconsciously shaping us from the nuts and bolts of cognition and perception to our loftiest abstract notions and major life decisions. Language is central to our experience of being human, and the languages we speak profoundly shape the way we think, the way we see the world, the way we live our lives.
  • The fact that even quirks of grammar, such as grammatical gender, can affect our thinking is profound. Such quirks are pervasive in language; gender, for example, applies to all nouns, which means that it is affecting how people think about anything that can be designated by a noun.
  • How does an artist decide whether death, say, or time should be painted as a man or a woman? It turns out that in 85 percent of such personifications, whether a male or female figure is chosen is predicted by the grammatical gender of the word in the artist's native language. So, for example, German painters are more likely to paint death as a man, whereas Russian painters are more likely to paint death as a woman.
  • Does treating chairs as masculine and beds as feminine in the grammar make Russian speakers think of chairs as being more like men and beds as more like women in some way? It turns out that it does. In one study, we asked German and Spanish speakers to describe objects having opposite gender assignment in those two languages. The descriptions they gave differed in a way predicted by grammatical gender. For example, when asked to describe a "key" — a word that is masculine in German and feminine in Spanish — the German speakers were more likely to use words like "hard," "heavy," "jagged," "metal," "serrated," and "useful," whereas Spanish speakers were more likely to say "golden," "intricate," "little," "lovely," "shiny," and "tiny." To describe a "bridge," which is feminine in German and masculine in Spanish, the German speakers said "beautiful," "elegant," "fragile," "peaceful," "pretty," and "slender," and the Spanish speakers said "big," "dangerous," "long," "strong," "sturdy," and "towering." This was true even though all testing was done in English, a language without grammatical gender. The same pattern of results also emerged in entirely nonlinguistic tasks (e.g., rating similarity between pictures). And we can also show that it is aspects of language per se that shape how people think: teaching English speakers new grammatical gender systems influences mental representations of objects in the same way it does with German and Spanish speakers. Apparently even small flukes of grammar, like the seemingly arbitrary assignment of gender to a noun, can have an effect on people's ideas of concrete objects in the world.
  • Even basic aspects of time perception can be affected by language. For example, English speakers prefer to talk about duration in terms of length (e.g., "That was a short talk," "The meeting didn't take long"), while Spanish and Greek speakers prefer to talk about time in terms of amount, relying more on words like "much" "big", and "little" rather than "short" and "long" Our research into such basic cognitive abilities as estimating duration shows that speakers of different languages differ in ways predicted by the patterns of metaphors in their language. (For example, when asked to estimate duration, English speakers are more likely to be confused by distance information, estimating that a line of greater length remains on the test screen for a longer period of time, whereas Greek speakers are more likely to be confused by amount, estimating that a container that is fuller remains longer on the screen.)
  • An important question at this point is: Are these differences caused by language per se or by some other aspect of culture? Of course, the lives of English, Mandarin, Greek, Spanish, and Kuuk Thaayorre speakers differ in a myriad of ways. How do we know that it is language itself that creates these differences in thought and not some other aspect of their respective cultures? One way to answer this question is to teach people new ways of talking and see if that changes the way they think. In our lab, we've taught English speakers different ways of talking about time. In one such study, English speakers were taught to use size metaphors (as in Greek) to describe duration (e.g., a movie is larger than a sneeze), or vertical metaphors (as in Mandarin) to describe event order. Once the English speakers had learned to talk about time in these new ways, their cognitive performance began to resemble that of Greek or Mandarin speakers. This suggests that patterns in a language can indeed play a causal role in constructing how we think.6 In practical terms, it means that when you're learning a new language, you're not simply learning a new way of talking, you are also inadvertently learning a new way of thinking. Beyond abstract or complex domains of thought like space and time, languages also meddle in basic aspects of visual perception — our ability to distinguish colors, for example. Different languages divide up the color continuum differently: some make many more distinctions between colors than others, and the boundaries often don't line up across languages.
  • To test whether differences in color language lead to differences in color perception, we compared Russian and English speakers' ability to discriminate shades of blue. In Russian there is no single word that covers all the colors that English speakers call "blue." Russian makes an obligatory distinction between light blue (goluboy) and dark blue (siniy). Does this distinction mean that siniy blues look more different from goluboy blues to Russian speakers? Indeed, the data say yes. Russian speakers are quicker to distinguish two shades of blue that are called by the different names in Russian (i.e., one being siniy and the other being goluboy) than if the two fall into the same category. For English speakers, all these shades are still designated by the same word, "blue," and there are no comparable differences in reaction time. Further, the Russian advantage disappears when subjects are asked to perform a verbal interference task (reciting a string of digits) while making color judgments but not when they're asked to perform an equally difficult spatial interference task (keeping a novel visual pattern in memory). The disappearance of the advantage when performing a verbal task shows that language is normally involved in even surprisingly basic perceptual judgments — and that it is language per se that creates this difference in perception between Russian and English speakers.
  • What it means for a language to have grammatical gender is that words belonging to different genders get treated differently grammatically and words belonging to the same grammatical gender get treated the same grammatically. Languages can require speakers to change pronouns, adjective and verb endings, possessives, numerals, and so on, depending on the noun's gender. For example, to say something like "my chair was old" in Russian (moy stul bil' stariy), you'd need to make every word in the sentence agree in gender with "chair" (stul), which is masculine in Russian. So you'd use the masculine form of "my," "was," and "old." These are the same forms you'd use in speaking of a biological male, as in "my grandfather was old." If, instead of speaking of a chair, you were speaking of a bed (krovat'), which is feminine in Russian, or about your grandmother, you would use the feminine form of "my," "was," and "old."
  • Weiye Loh on 22 Apr 11
     
    For a long time, the idea that language might shape thought was considered at best untestable and more often simply wrong. Research in my labs at Stanford University and at MIT has helped reopen this question. We have collected data around the world: from China, Greece, Chile, Indonesia, Russia, and Aboriginal Australia. What we have learned is that people who speak different languages do indeed think differently and that even flukes of grammar can profoundly affect how we see the world. Language is a uniquely human gift, central to our experience of being human. Appreciating its role in constructing our mental lives brings us one step closer to understanding the very nature of humanity.
Weiye Loh

Adventures in Flay-land: Dealing with Denialists - Delingpole Part III - 0 views

flay.jellybee.co.uk/...enialists-delingpole-part.html

Skepticism Science Denial Climate Science

shared by Weiye Loh on 30 Jan 11 - No Cached
  • This post is about how one should deal with a denialist of Delingpole's ilk.
  • I saw someone I follow on Twitter retweet an update from another Twitter user called @AGW_IS_A_HOAX, which was this: "NZ #Climate Scientists Admit Faking Temperatures http://bit.ly/fHbdPI RT @admrich #AGW #Climategate #Cop16 #ClimateChange #GlobalWarming".
  • So I click on it. And this is how you deal with a denialist claim. You actually look into it. Here is the text of that article reproduced in full: New Zealand Climate Scientists Admit To Faking Temperatures: The Actual Temps Show Little Warming Over Last 50 YearsRead here and here. Climate "scientists" across the world have been blatantly fabricating temperatures in hopes of convincing the public and politicians that modern global warming is unprecedented and accelerating. The scientists doing the fabrication are usually employed by the government agencies or universities, which thrive and exist on taxpayer research dollars dedicated to global warming research. A classic example of this is the New Zealand climate agency, which is now admitting their scientists produced bogus "warming" temperatures for New Zealand. "NIWA makes the huge admission that New Zealand has experienced hardly any warming during the last half-century. For all their talk about warming, for all their rushed invention of the “Eleven-Station Series” to prove warming, this new series shows that no warming has occurred here since about 1960. Almost all the warming took place from 1940-60, when the IPCC says that the effect of CO2 concentrations was trivial. Indeed, global temperatures were falling during that period.....Almost all of the 34 adjustments made by Dr Jim Salinger to the 7SS have been abandoned, along with his version of the comparative station methodology."A collection of temperature-fabrication charts.
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  • I check out the first link, the first "here" where the article says "Read here and here". I can see that there's been some sort of dispute between two New Zealand groups associated with climate change. One is New Zealand’s Climate Science Coalition (NZCSC) and the other is New Zealand’s National Institute of Water and Atmospheric Research (NIWA), but it doesn't tell me a whole lot more than I already got from the other article.
  • I check the second source behind that article. The second article, I now realize, is published on the website of a person called Andrew Montford with whom I've been speaking recently and who is the author of a book titled The Hockey Stick Illusion. I would not label Andrew a denialist. He makes some good points and seems to be a decent guy and geniune sceptic (This is not to suggest all denialists are outwardly dishonest; however, they do tend to be hard to reason with). Again, this article doesn't give me anything that I haven't already seen, except a link to another background source. I go there.
  • From this piece written up on Scoop NZNEWSUK I discover that a coalition group consisting of the NZCSC and the Climate Conversation Group (CCG) has pressured the NIWA into abandoning a set of temperature record adjustments of which the coalition dispute the validity. This was the culmination of a court proceeding in December 2010, last month. In dispute were 34 adjustments that had been made by Dr Jim Salinger to the 7SS temperature series, though I don't know what that is exactly. I also discover that there is a guy called Richard Treadgold, Convenor of the CCG, who is quoted several times. Some of the statements he makes are quoted in the articles I've already seen. They are of a somewhat snide tenor. The CSC object to the methodology used by the NIWA to adjust temperature measurements (one developed as part of a PhD thesis), which they critique in a paper in November 2009 with the title "Are we feeling warmer yet?", and are concerned about how this public agency is spending its money. I'm going to have to dig a bit deeper if I want to find out more. There is a section with links under the heading "Related Stories on Scoop". I click on a few of those.
  • One of these leads me to more. Of particular interest is a fairly neutral article outlining the progress of the court action. I get some more background: For the last ten years, visitors to NIWA’s official website have been greeted by a graph of the “seven-station series” (7SS), under the bold heading “New Zealand Temperature Record”. The graph covers the period from 1853 to the present, and is adorned by a prominent trend-line sloping sharply upwards. Accompanying text informs the world that “New Zealand has experienced a warming trend of approximately 0.9°C over the past 100 years.” The 7SS has been updated and used in every monthly issue of NIWA’s “Climate Digest” since January 1993. Its 0.9°C (sometimes 1.0°C) of warming has appeared in the Australia/NZ Chapter of the IPCC’s 2001 and 2007 Assessment Reports. It has been offered as sworn evidence in countless tribunals and judicial enquiries, and provides the historical base for all of NIWA’s reports to both Central and Local Governments on climate science issues and future projections.
  • now I can see why this is so important. The temperature record informs the conclusions of the IPCC assessment reports and provides crucial evidence for global warming.
  • Further down we get: NIWA announces that it has now completed a full internal examination of the Salinger adjustments in the 7SS, and has forwarded its “review papers” to its Australian counterpart, the Bureau of Meteorology (BOM) for peer review.and: So the old 7SS has already been repudiated. A replacement NZTR [New Zealand Temperature Record] is being prepared by NIWA – presumably the best effort they are capable of producing. NZCSC is about to receive what it asked for. On the face of it, there’s nothing much left for the Court to adjudicate.
  • NIWA has been forced to withdraw its earlier temperature record and replace it with a new one. Treadgold quite clearly states that "NIWA makes the huge admission that New Zealand has experienced hardly any warming during the last half-century" and that "the new temperature record shows no evidence of a connection with global warming." Earlier in the article he also stresses the role of the CSC in achieving these revisions, saying "after 12 months of futile attempts to persuade the public, misleading answers to questions in the Parliament from ACT and reluctant but gradual capitulation from NIWA, their relentless defence of the old temperature series has simply evaporated. They’ve finally given in, but without our efforts the faulty graph would still be there."
  • All this leads me to believe that if I look at the website of NIWA I will see a retraction of the earlier position and a new position that New Zealand has experienced no unusual warming. This is easy enough to check. I go there. Actually, I search for it to find the exact page. Here is the 7SS page on the NIWA site. Am I surprised that NIWA have retracted nothing and that in fact their revised graph shows similar results? Not really. However, I am somewhat surprised by this page on the Climate Conversation Group website which claims that the 7SS temperature record is as dead as the parrot in the Monty Python sketch. It says "On the eve of Christmas, when nobody was looking, NIWA declared that New Zealand had a new official temperature record (the NZT7) and whipped the 7SS off its website." However, I've already seen that this is not true. Perhaps there was once a 7SS graph and information about the temperature record on the site's homepage that can no longer be seen. I don't know. I can only speculate. I know that there is a section on the NIWA site about the 7SS temperature record that contains a number of graphs and figures and discusses recent revisions. It has been updated as recently as December 2010, last month. The NIWA page talks all about the 7SS series and has a heading that reads "Our new analysis confirms the warming trend".
  • The CCG page claims that the new NZT7 is not in fact a revision but rather a replacement. Although it results in a similar curve, the adjustments that were made are very different. Frankly I can't see how that matters at the end of the day. Now, I don't really know whether I can believe that the NIWA analysis is true, but what I am in no doubt of whatsoever is that the statements made by Richard Treadgold that were quoted in so many places are at best misleading. The NIWA has not changed its position in the slightest. The assertion that the NIWA have admitted that New Zealand has not warmed much since 1960 is a politician's careful argument. Both analyses showed the same result. This is a fact that NIWA have not disputed; however, they still maintain a connection to global warming. A document explaining the revisions talks about why the warming has slowed after 1960: The unusually steep warming in the 1940-1960 period is paralleled by an unusually large increase in northerly flow* during this same period. On a longer timeframe, there has been a trend towards less northerly flow (more southerly) since about 1960. However, New Zealand temperatures have continued to increase over this time, albeit at a reduced rate compared with earlier in the 20th century. This is consistent with a warming of the whole region of the southwest Pacific within which New Zealand is situated.
  • Denialists have taken Treadgold's misleading mantra and spread it far and wide including on Twitter and fringe websites, but it is faulty as I've just demonstrated. Why do people do this? Perhaps they are hoping that others won't check the sources. Most people don't. I hope this serves as a lesson for why you always should.
Weiye Loh

Hayek, The Use of Knowledge in Society | Library of Economics and Liberty - 0 views

www.econlib.org/...hykKnw1.html

Knowledge Economics Data

shared by Weiye Loh on 11 Sep 10 - Cached
  • the "data" from which the economic calculus starts are never for the whole society "given" to a single mind which could work out the implications and can never be so given.
  • The peculiar character of the problem of a rational economic order is determined precisely by the fact that the knowledge of the circumstances of which we must make use never exists in concentrated or integrated form but solely as the dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess.
  • The economic problem of society
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  • is a problem of the utilization of knowledge which is not given to anyone in its totality.
  • who is to do the planning. It is about this question that all the dispute about "economic planning" centers. This is not a dispute about whether planning is to be done or not. It is a dispute as to whether planning is to be done centrally, by one authority for the whole economic system, or is to be divided among many individuals. Planning in the specific sense in which the term is used in contemporary controversy necessarily means central planning—direction of the whole economic system according to one unified plan. Competition, on the other hand, means decentralized planning by many separate persons. The halfway house between the two, about which many people talk but which few like when they see it, is the
  • Which of these systems is likely to be more efficient depends mainly on the question under which of them we can expect that fuller use will be made of the existing knowledge.
  • It may be admitted that, as far as scientific knowledge is concerned, a body of suitably chosen experts may be in the best position to command all the best knowledge available—though this is of course merely shifting the difficulty to the problem of selecting the experts.
  • Today it is almost heresy to suggest that scientific knowledge is not the sum of all knowledge. But a little reflection will show that there is beyond question a body of very important but unorganized knowledge which cannot possibly be called scientific in the sense of knowledge of general rules: the knowledge of the particular circumstances of time and place. It is with respect to this that practically every individual has some advantage over all others because he possesses unique information of which beneficial use might be made, but of which use can be made only if the decisions depending on it are left to him or are made with his active coöperation.
  • the relative importance of the different kinds of knowledge; those more likely to be at the disposal of particular individuals and those which we should with greater confidence expect to find in the possession of an authority made up of suitably chosen experts. If it is today so widely assumed that the latter will be in a better position, this is because one kind of knowledge, namely, scientific knowledge, occupies now so prominent a place in public imagination that we tend to forget that it is not the only kind that is relevant.
  • It is a curious fact that this sort of knowledge should today be generally regarded with a kind of contempt and that anyone who by such knowledge gains an advantage over somebody better equipped with theoretical or technical knowledge is thought to have acted almost disreputably. To gain an advantage from better knowledge of facilities of communication or transport is sometimes regarded as almost dishonest, although it is quite as important that society make use of the best opportunities in this respect as in using the latest scientific discoveries.
  • The common idea now seems to be that all such knowledge should as a matter of course be readily at the command of everybody, and the reproach of irrationality leveled against the existing economic order is frequently based on the fact that it is not so available. This view disregards the fact that the method by which such knowledge can be made as widely available as possible is precisely the problem to which we have to find an answer.
  • One reason why economists are increasingly apt to forget about the constant small changes which make up the whole economic picture is probably their growing preoccupation with statistical aggregates, which show a very much greater stability than the movements of the detail. The comparative stability of the aggregates cannot, however, be accounted for—as the statisticians occasionally seem to be inclined to do—by the "law of large numbers" or the mutual compensation of random changes.
  • the sort of knowledge with which I have been concerned is knowledge of the kind which by its nature cannot enter into statistics and therefore cannot be conveyed to any central authority in statistical form. The statistics which such a central authority would have to use would have to be arrived at precisely by abstracting from minor differences between the things, by lumping together, as resources of one kind, items which differ as regards location, quality, and other particulars, in a way which may be very significant for the specific decision. It follows from this that central planning based on statistical information by its nature cannot take direct account of these circumstances of time and place and that the central planner will have to find some way or other in which the decisions depending on them can be left to the "man on the spot."
  • We need decentralization because only thus can we insure that the knowledge of the particular circumstances of time and place will be promptly used. But the "man on the spot" cannot decide solely on the basis of his limited but intimate knowledge of the facts of his immediate surroundings. There still remains the problem of communicating to him such further information as he needs to fit his decisions into the whole pattern of changes of the larger economic system.
  • The problem which we meet here is by no means peculiar to economics but arises in connection with nearly all truly social phenomena, with language and with most of our cultural inheritance, and constitutes really the central theoretical problem of all social science. As Alfred Whitehead has said in another connection, "It is a profoundly erroneous truism, repeated by all copy-books and by eminent people when they are making speeches, that we should cultivate the habit of thinking what we are doing. The precise opposite is the case. Civilization advances by extending the number of important operations which we can perform without thinking about them." This is of profound significance in the social field. We make constant use of formulas, symbols, and rules whose meaning we do not understand and through the use of which we avail ourselves of the assistance of knowledge which individually we do not possess. We have developed these practices and institutions by building upon habits and institutions which have proved successful in their own sphere and which have in turn become the foundation of the civilization we have built up.
  • To assume all the knowledge to be given to a single mind in the same manner in which we assume it to be given to us as the explaining economists is to assume the problem away and to disregard everything that is important and significant in the real world.
  • That an economist of Professor Schumpeter's standing should thus have fallen into a trap which the ambiguity of the term "datum" sets to the unwary can hardly be explained as a simple error. It suggests rather that there is something fundamentally wrong with an approach which habitually disregards an essential part of the phenomena with which we have to deal: the unavoidable imperfection of man's knowledge and the consequent need for a process by which knowledge is constantly communicated and acquired. Any approach, such as that of much of mathematical economics with its simultaneous equations, which in effect starts from the assumption that people's knowledge corresponds with the objective facts of the situation, systematically leaves out what is our main task to explain. I am far from denying that in our system equilibrium analysis has a useful function to perform. But when it comes to the point where it misleads some of our leading thinkers into believing that the situation which it describes has direct relevance to the solution of practical problems, it is high time that we remember that it does not deal with the social process at all and that it is no more than a useful preliminary to the study of the main problem.
  • Weiye Loh on 11 Sep 10
     
    The Use of Knowledge in Society Hayek, Friedrich A.(1899-1992)
Weiye Loh

Religion: Faith in science : Nature News - 0 views

www.nature.com/...470323a.html

Science Religion Faith Funding Academic Academic Research

shared by Weiye Loh on 18 Feb 11 - No Cached
  • The Templeton Foundation claims to be a friend of science. So why does it make so many researchers uneasy?
  • With a current endowment estimated at US$2.1 billion, the organization continues to pursue Templeton's goal of building bridges between science and religion. Each year, it doles out some $70 million in grants, more than $40 million of which goes to research in fields such as cosmology, evolutionary biology and psychology.
  • however, many scientists find it troubling — and some see it as a threat. Jerry Coyne, an evolutionary biologist at the University of Chicago, Illinois, calls the foundation "sneakier than the creationists". Through its grants to researchers, Coyne alleges, the foundation is trying to insinuate religious values into science. "It claims to be on the side of science, but wants to make faith a virtue," he says.
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  • But other researchers, both with and without Templeton grants, say that they find the foundation remarkably open and non-dogmatic. "The Templeton Foundation has never in my experience pressured, suggested or hinted at any kind of ideological slant," says Michael Shermer, editor of Skeptic, a magazine that debunks pseudoscience, who was hired by the foundation to edit an essay series entitled 'Does science make belief in God obsolete?'
  • The debate highlights some of the challenges facing the Templeton Foundation after the death of its founder in July 2008, at the age of 95.
  • With the help of a $528-million bequest from Templeton, the foundation has been radically reframing its research programme. As part of that effort, it is reducing its emphasis on religion to make its programmes more palatable to the broader scientific community. Like many of his generation, Templeton was a great believer in progress, learning, initiative and the power of human imagination — not to mention the free-enterprise system that allowed him, a middle-class boy from Winchester, Tennessee, to earn billions of dollars on Wall Street. The foundation accordingly allocates 40% of its annual grants to programmes with names such as 'character development', 'freedom and free enterprise' and 'exceptional cognitive talent and genius'.
  • Unlike most of his peers, however, Templeton thought that the principles of progress should also apply to religion. He described himself as "an enthusiastic Christian" — but was also open to learning from Hinduism, Islam and other religious traditions. Why, he wondered, couldn't religious ideas be open to the type of constructive competition that had produced so many advances in science and the free market?
  • That question sparked Templeton's mission to make religion "just as progressive as medicine or astronomy".
  • Early Templeton prizes had nothing to do with science: the first went to the Catholic missionary Mother Theresa of Calcutta in 1973.
  • By the 1980s, however, Templeton had begun to realize that fields such as neuroscience, psychology and physics could advance understanding of topics that are usually considered spiritual matters — among them forgiveness, morality and even the nature of reality. So he started to appoint scientists to the prize panel, and in 1985 the award went to a research scientist for the first time: Alister Hardy, a marine biologist who also investigated religious experience. Since then, scientists have won with increasing frequency.
  • "There's a distinct feeling in the research community that Templeton just gives the award to the most senior scientist they can find who's willing to say something nice about religion," says Harold Kroto, a chemist at Florida State University in Tallahassee, who was co-recipient of the 1996 Nobel Prize in Chemistry and describes himself as a devout atheist.
  • Yet Templeton saw scientists as allies. They had what he called "the humble approach" to knowledge, as opposed to the dogmatic approach. "Almost every scientist will agree that they know so little and they need to learn," he once said.
  • Templeton wasn't interested in funding mainstream research, says Barnaby Marsh, the foundation's executive vice-president. Templeton wanted to explore areas — such as kindness and hatred — that were not well known and did not attract major funding agencies. Marsh says Templeton wondered, "Why is it that some conflicts go on for centuries, yet some groups are able to move on?"
  • Templeton's interests gave the resulting list of grants a certain New Age quality (See Table 1). For example, in 1999 the foundation gave $4.6 million for forgiveness research at the Virginia Commonwealth University in Richmond, and in 2001 it donated $8.2 million to create an Institute for Research on Unlimited Love (that is, altruism and compassion) at Case Western Reserve University in Cleveland, Ohio. "A lot of money wasted on nonsensical ideas," says Kroto. Worse, says Coyne, these projects are profoundly corrupting to science, because the money tempts researchers into wasting time and effort on topics that aren't worth it. If someone is willing to sell out for a million dollars, he says, "Templeton is there to oblige him".
  • At the same time, says Marsh, the 'dean of value investing', as Templeton was known on Wall Street, had no intention of wasting his money on junk science or unanswerables such as whether God exists. So before pursuing a scientific topic he would ask his staff to get an assessment from appropriate scholars — a practice that soon evolved into a peer-review process drawing on experts from across the scientific community.
  • Because Templeton didn't like bureaucracy, adds Marsh, the foundation outsourced much of its peer review and grant giving. In 1996, for example, it gave $5.3 million to the American Association for the Advancement of Science (AAAS) in Washington DC, to fund efforts that work with evangelical groups to find common ground on issues such as the environment, and to get more science into seminary curricula. In 2006, Templeton gave $8.8 million towards the creation of the Foundational Questions Institute (FQXi), which funds research on the origins of the Universe and other fundamental issues in physics, under the leadership of Anthony Aguirre, an astrophysicist at the University of California, Santa Cruz, and Max Tegmark, a cosmologist at the Massachusetts Institute of Technology in Cambridge.
  • But external peer review hasn't always kept the foundation out of trouble. In the 1990s, for example, Templeton-funded organizations gave book-writing grants to Guillermo Gonzalez, an astrophysicist now at Grove City College in Pennsylvania, and William Dembski, a philosopher now at the Southwestern Baptist Theological Seminary in Fort Worth, Texas. After obtaining the grants, both later joined the Discovery Institute — a think-tank based in Seattle, Washington, that promotes intelligent design. Other Templeton grants supported a number of college courses in which intelligent design was discussed. Then, in 1999, the foundation funded a conference at Concordia University in Mequon, Wisconsin, in which intelligent-design proponents confronted critics. Those awards became a major embarrassment in late 2005, during a highly publicized court fight over the teaching of intelligent design in schools in Dover, Pennsylvania. A number of media accounts of the intelligent design movement described the Templeton Foundation as a major supporter — a charge that Charles Harper, then senior vice-president, was at pains to deny.
  • Some foundation officials were initially intrigued by intelligent design, Harper told The New York Times. But disillusionment set in — and Templeton funding stopped — when it became clear that the theory was part of a political movement from the Christian right wing, not science. Today, the foundation website explicitly warns intelligent-design researchers not to bother submitting proposals: they will not be considered.
  • Avowedly antireligious scientists such as Coyne and Kroto see the intelligent-design imbroglio as a symptom of their fundamental complaint that religion and science should not mix at all. "Religion is based on dogma and belief, whereas science is based on doubt and questioning," says Coyne, echoing an argument made by many others. "In religion, faith is a virtue. In science, faith is a vice." The purpose of the Templeton Foundation is to break down that wall, he says — to reconcile the irreconcilable and give religion scholarly legitimacy.
  • Foundation officials insist that this is backwards: questioning is their reason for being. Religious dogma is what they are fighting. That does seem to be the experience of many scientists who have taken Templeton money. During the launch of FQXi, says Aguirre, "Max and I were very suspicious at first. So we said, 'We'll try this out, and the minute something smells, we'll cut and run.' It never happened. The grants we've given have not been connected with religion in any way, and they seem perfectly happy about that."
  • John Cacioppo, a psychologist at the University of Chicago, also had concerns when he started a Templeton-funded project in 2007. He had just published a paper with survey data showing that religious affiliation had a negative correlation with health among African-Americans — the opposite of what he assumed the foundation wanted to hear. He was bracing for a protest when someone told him to look at the foundation's website. They had displayed his finding on the front page. "That made me relax a bit," says Cacioppo.
  • Yet, even scientists who give the foundation high marks for openness often find it hard to shake their unease. Sean Carroll, a physicist at the California Institute of Technology in Pasadena, is willing to participate in Templeton-funded events — but worries about the foundation's emphasis on research into 'spiritual' matters. "The act of doing science means that you accept a purely material explanation of the Universe, that no spiritual dimension is required," he says.
  • It hasn't helped that Jack Templeton is much more politically and religiously conservative than his father was. The foundation shows no obvious rightwards trend in its grant-giving and other activities since John Templeton's death — and it is barred from supporting political activities by its legal status as a not-for-profit corporation. Still, many scientists find it hard to trust an organization whose president has used his personal fortune to support right-leaning candidates and causes such as the 2008 ballot initiative that outlawed gay marriage in California.
  • Scientists' discomfort with the foundation is probably inevitable in the current political climate, says Scott Atran, an anthropologist at the University of Michigan in Ann Arbor. The past 30 years have seen the growing power of the Christian religious right in the United States, the rise of radical Islam around the world, and religiously motivated terrorist attacks such as those in the United States on 11 September 2001. Given all that, says Atran, many scientists find it almost impossible to think of religion as anything but fundamentalism at war with reason.
  • the foundation has embraced the theme of 'science and the big questions' — an open-ended list that includes topics such as 'Does the Universe have a purpose?'
  • Towards the end of Templeton's life, says Marsh, he became increasingly concerned that this reaction was getting in the way of the foundation's mission: that the word 'religion' was alienating too many good scientists.
  • The peer-review and grant-making system has also been revamped: whereas in the past the foundation ran an informal mix of projects generated by Templeton and outside grant seekers, the system is now organized around an annual list of explicit funding priorities.
  • The foundation is still a work in progress, says Jack Templeton — and it always will be. "My father believed," he says, "we were all called to be part of an ongoing creative process. He was always trying to make people think differently." "And he always said, 'If you're still doing today what you tried to do two years ago, then you're not making progress.'" 
Weiye Loh

Why do we care where we publish? - 0 views

arstechnica.com/...o-we-care-where-we-publish.ars

Academic Journal Academic Research Publication Latour Representation Science Journalism

shared by Weiye Loh on 12 Mar 11 - No Cached
  • being both a working scientist and a science writer gives me a unique perspective on science, scientific publications, and the significance of scientific work. The final disclosure should be that I have never published in any of the top rank physics journals or in Science, Nature, or PNAS. I don't believe I have an axe to grind about that, but I am also sure that you can ascribe some of my opinions to PNAS envy.
  • If you asked most scientists what their goals were, the answer would boil down to the generation of new knowledge. But, at some point, science and scientists have to interact with money and administrators, which has significant consequences for science. For instance, when trying to employ someone to do a job, you try to objectively decide if the skills set of the prospective employee matches that required to do the job. In science, the same question has to be asked—instead of being asked once per job interview, however, this question gets asked all the time.
  • Because science requires funding, and no one gets a lifetime dollop-o-cash to explore their favorite corner of the universe. So, the question gets broken down to "how competent is the scientist?" "Is the question they want to answer interesting?" "Do they have the resources to do what they say they will?" We will ignore the last question and focus on the first two.
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  • How can we assess the competence of a scientist? Past performance is, realistically, the only way to judge future performance. Past performance can only be assessed by looking at their publications. Were they in a similar area? Are they considered significant? Are they numerous? Curiously, though, the second question is also answered by looking at publications—if a topic is considered significant, then there will be lots of publications in that area, and those publications will be of more general interest, and so end up in higher ranking journals.
  • So we end up in the situation that the editors of major journals are in the position to influence the direction of scientific funding, meaning that there is a huge incentive for everyone to make damn sure that their work ends up in Science or Nature. But why are Science, Nature, and PNAS considered the place to put significant work? Why isn't a new optical phenomena, published in Optics Express, as important as a new optical phenomena published in Science?
  • The big three try to be general; they will, in principle, publish reports from any discipline, and they anticipate readership from a range of disciplines. This explicit generality means that the scientific results must not only be of general interest, but also highly significant. The remaining journals become more specialized, covering perhaps only physics, or optics, or even just optical networking. However, they all claim to only publish work that is highly original in nature.
  • Are standards really so different? Naturally, the more specialized a journal is, the fewer people it appeals to. However, the major difference in determining originality is one of degree and referee. A more specialized journal has more detailed articles, so the differences between experiments stand out more obviously, while appealing to general interest changes the emphasis of the article away from details toward broad conclusions.
  • as the audience becomes broader, more technical details get left by the wayside. Note that none of the gene sequences published in Science have the actual experimental and analysis details. What ends up published is really a broad-brush description of the work, with the important details either languishing as supplemental information, or even published elsewhere, in a more suitable journal. Yet, the high profile paper will get all the citations, while the more detailed—the unkind would say accurate—description of the work gets no attention.
  • And that is how journals are ranked. Count the number of citations for each journal per volume, run it through a magic number generator, and the impact factor jumps out (make your checks out to ISI Thomson please). That leaves us with the following formula: grants require high impact publications, high impact publications need citations, and that means putting research in a journal that gets lots of citations. Grants follow the concepts that appear to be currently significant, and that's decided by work that is published in high impact journals.
  • This system would be fine if it did not ignore the fact that performing science and reporting scientific results are two very different skills, and not everyone has both in equal quantity. The difference between a Nature-worthy finding and a not-Nature-worthy finding is often in the quality of the writing. How skillfully can I relate this bit of research back to general or topical interests? It really is this simple. Over the years, I have seen quite a few physics papers with exaggerated claims of significance (or even results) make it into top flight journals, and the only differences I can see between those works and similar works published elsewhere is that the presentation and level of detail are different.
  • articles from the big three are much easier to cover on Nobel Intent than articles from, say Physical Review D. Nevertheless, when we do cover them, sometimes the researchers suddenly realize that they could have gotten a lot more mileage out of their work. It changes their approach to reporting their results, which I see as evidence that writing skill counts for as much as scientific quality.
  • If that observation is generally true, then it raises questions about the whole process of evaluating a researcher's competence and a field's significance, because good writers corrupt the process by publishing less significant work in journals that only publish significant findings. In fact, I think it goes further than that, because Science, Nature, and PNAS actively promote themselves as scientific compasses. Want to find the most interesting and significant research? Read PNAS.
  • The publishers do this by extensively publicizing science that appears in their own journals. Their news sections primarily summarize work published in the same issue of the same magazine. This lets them create a double-whammy of scientific significance—not only was the work published in Nature, they also summarized it in their News and Views section.
  • Furthermore, the top three work very hard at getting other journalists to cover their articles. This is easy to see by simply looking at Nobel Intent's coverage. Most of the work we discuss comes from Science and Nature. Is this because we only read those two publications? No, but they tell us ahead of time what is interesting in their upcoming issue. They even provide short summaries of many papers that practically guide people through writing the story, meaning reporter Jim at the local daily doesn't need a science degree to cover the science beat.
  • Very few of the other journals do this. I don't get early access to the Physical Review series, even though I love reporting from them. In fact, until this year, they didn't even highlight interesting papers for their own readers. This makes it incredibly hard for a science reporter to cover science outside of the major journals. The knock-on effect is that Applied Physics Letters never appears in the news, which means you can't evaluate recent news coverage to figure out what's of general interest, leaving you with... well, the big three journals again, which mostly report on themselves. On the other hand, if a particular scientific topic does start to receive some press attention, it is much more likely that similar work will suddenly be acceptable in the big three journals.
  • That said, I should point out that judging the significance of scientific work is a process fraught with difficulty. Why do you think it takes around 10 years from the publication of first results through to obtaining a Nobel Prize? Because it can take that long for the implications of the results to sink in—or, more commonly, sink without trace.
  • I don't think that we can reasonably expect journal editors and peer reviewers to accurately assess the significance (general or otherwise) of a new piece of research. There are, of course, exceptions: the first genome sequences, the first observation that the rate of the expansion of the universe is changing. But the point is that these are exceptions, and most work's significance is far more ambiguous, and even goes unrecognized (or over-celebrated) by scientists in the field.
  • The conclusion is that the top three journals are significantly gamed by scientists who are trying to get ahead in their careers—citations always lag a few years behind, so a PNAS paper with less than ten citations can look good for quite a few years, even compared to an Optics Letters with 50 citations. The top three journals overtly encourage this, because it is to their advantage if everyone agrees that they are the source of the most interesting science. Consequently, scientists who are more honest in self-assessing their work, or who simply aren't word-smiths, end up losing out.
  • scientific competence should not be judged by how many citations the author's work has received or where it was published. Instead, we should consider using a mathematical graph analysis to look at the networks of publications and citations, which should help us judge how central to a field a particular researcher is. This would have the positive influence of a publication mattering less than who thought it was important.
  • Science and Nature should either eliminate their News and Views section, or implement a policy of not reporting on their own articles. This would open up one of the major sources of "science news for scientists" to stories originating in other journals.
Weiye Loh

BrainGate gives paralysed the power of mind control | Science | The Observer - 0 views

www.guardian.co.uk/...ain-implant-paralysis-movement

Brain Transhumanism

shared by Weiye Loh on 18 Apr 11 - No Cached
  • brain-computer interface, or BCI
  • is a branch of science exploring how computers and the human brain can be meshed together. It sounds like science fiction (and can look like it too), but it is motivated by a desire to help chronically injured people. They include those who have lost limbs, people with Lou Gehrig's disease, or those who have been paralysed by severe spinal-cord injuries. But the group of people it might help the most are those whom medicine assumed were beyond all hope: sufferers of "locked-in syndrome".
  • These are often stroke victims whose perfectly healthy minds end up trapped inside bodies that can no longer move. The most famous example was French magazine editor Jean-Dominique Bauby who managed to dictate a memoir, The Diving Bell and the Butterfly, by blinking one eye. In the book, Bauby, who died in 1997 shortly after the book was published, described the prison his body had become for a mind that still worked normally.
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  • Now the project is involved with a second set of human trials, pushing the technology to see how far it goes and trying to miniaturise it and make it wireless for a better fit in the brain. BrainGate's concept is simple. It posits that the problem for most patients does not lie in the parts of the brain that control movement, but with the fact that the pathways connecting the brain to the rest of the body, such as the spinal cord, have been broken. BrainGate plugs into the brain, picks up the right neural signals and beams them into a computer where they are translated into moving a cursor or controlling a computer keyboard. By this means, paralysed people can move a robot arm or drive their own wheelchair, just by thinking about it.
  • he and his team are decoding the language of the human brain. This language is made up of electronic signals fired by billions of neurons and it controls everything from our ability to move, to think, to remember and even our consciousness itself. Donoghue's genius was to develop a deceptively small device that can tap directly into the brain and pick up those signals for a computer to translate them. Gold wires are implanted into the brain's tissue at the motor cortex, which controls movement. Those wires feed back to a tiny array – an information storage device – attached to a "pedestal" in the skull. Another wire feeds from the array into a computer. A test subject with BrainGate looks like they have a large plug coming out the top of their heads. Or, as Donoghue's son once described it, they resemble the "human batteries" in The Matrix.
  • BrainGate's highly advanced computer programs are able to decode the neuron signals picked up by the wires and translate them into the subject's desired movement. In crude terms, it is a form of mind-reading based on the idea that thinking about moving a cursor to the right will generate detectably different brain signals than thinking about moving it to the left.
  • The technology has developed rapidly, and last month BrainGate passed a vital milestone when one paralysed patient went past 1,000 days with the implant still in her brain and allowing her to move a computer cursor with her thoughts. The achievement, reported in the prestigious Journal of Neural Engineering, showed that the technology can continue to work inside the human body for unprecedented amounts of time.
  • Donoghue talks enthusiastically of one day hooking up BrainGate to a system of electronic stimulators plugged into the muscles of the arm or legs. That would open up the prospect of patients moving not just a cursor or their wheelchair, but their own bodies.
  • If Nagle's motor cortex was no longer working healthily, the entire BrainGate project could have been rendered pointless. But when Nagle was plugged in and asked to imagine moving his limbs, the signals beamed out with a healthy crackle. "We asked him to imagine moving his arm to the left and to the right and we could hear the activity," Donoghue says. When Nagle first moved a cursor on a screen using only his thoughts, he exclaimed: "Holy shit!"
  • BrainGate and other BCI projects have also piqued the interest of the government and the military. BCI is melding man and machine like no other sector of medicine or science and there are concerns about some of the implications. First, beyond detecting and translating simple movement commands, BrainGate may one day pave the way for mind-reading. A device to probe the innermost thoughts of captured prisoners or dissidents would prove very attractive to some future military or intelligence service. Second, there is the idea that BrainGate or other BCI technologies could pave the way for robot warriors controlled by distant humans using only their minds. At a conference in 2002, a senior American defence official, Anthony Tether, enthused over BCI. "Imagine a warrior with the intellect of a human and the immortality of a machine." Anyone who has seen Terminator might worry about that.
  • Donoghue acknowledges the concerns but has little time for them. When it comes to mind-reading, current BrainGate technology has enough trouble with translating commands for making a fist, let alone probing anyone's mental secrets
  • As for robot warriors, Donoghue was slightly more circumspect. At the moment most BCI research, including BrainGate projects, that touch on the military is focused on working with prosthetic limbs for veterans who have lost arms and legs. But Donoghue thinks it is healthy for scientists to be aware of future issues. "As long as there is a rational dialogue and scientists think about where this is going and what is the reasonable use of the technology, then we are on a good path," he says.
  • Weiye Loh on 18 Apr 11
     
    The robotic arm clutched a glass and swung it over a series of coloured dots that resembled a Twister gameboard. Behind it, a woman sat entirely immobile in a wheelchair. Slowly, the arm put the glass down, narrowly missing one of the dots. "She's doing that!" exclaims Professor John Donoghue, watching a video of the scene on his office computer - though the woman onscreen had not moved at all. "She actually has the arm under her control," he says, beaming with pride. "We told her to put the glass down on that dot." The woman, who is almost completely paralysed, was using Donoghue's groundbreaking technology to control the robot arm using only her thoughts. Called BrainGate, the device is implanted into her brain and hooked up to a computer to which she sends mental commands. The video played on, giving Donoghue, a silver-haired and neatly bearded man of 62, even more reason to feel pleased. The patient was not satisfied with her near miss and the robot arm lifted the glass again. After a brief hover, the arm positioned the glass on the dot.
Weiye Loh

BioCentre - 0 views

www.bioethics.ac.uk/...reject-radical-enhancement.php

Human Technology Transhumanism Bioethics

shared by Weiye Loh on 14 Apr 11 - No Cached
  • Humanity’s End. The main premise of the book is that proposals that would supposedly promise to make us smarter like never before or add thousands of years to our live seem rather far fetched and the domain of mere fantasy. However, it is these very proposals which form the basis of many of the ideas and thoughts presented by advocates of radical enhancement and which are beginning to move from the sidelines to the centre of main stream discussion. A variety of technologies and therapies are being presented to us as options to expand our capabilities and capacities in order for us to become something other than human.
  • Agar takes issue with this and argues against radical human enhancement. He structures his analysis and discussion by focusing on four key figures and their proposals which help to form the core of the case for radical enhancement debate.  First to be examined by Agar is Ray Kurzweil who argues that Man and Machine will become one as technology allows us to transcend our biology. Second, is Aubrey de Grey who is a passionate advocate and pioneer of anti-ageing therapies which allow us to achieve “longevity escape velocity”. Next is Nick Bostrom, a leading transhumanist who defends the morality and rationality of enhancement and finally James Hughes who is a keen advocate of a harmonious democracy of the enhanced and un-enhanced.
  • He avoids falling into any of the pitfalls of basing his argument solely upon the “playing God” question but instead seeks to posit a well founded argument in favour of the precautionary principle.
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  • Agar directly tackles Hughes’ ideas of a “democratic transhumanism.” Here as post-humans and humans live shoulder to shoulder in wonderful harmony, all persons have access to the technologies they want in order to promote their own flourishing.  Under girding all of this is the belief that no human should feel pressurised to become enhance. Agar finds no comfort with this and instead can foresee a situation where it would be very difficult for humans to ‘choose’ to remain human.  The pressure to radically enhance would be considerable given the fact that the radically enhanced would no doubt be occupying the positions of power in society and would consider the moral obligation to utilise to the full enhancement techniques as being a moral imperative for the good of society.  For those who were able to withstand then a new underclass would no doubt emerge between the enhanced and the un-enhanced. This is precisely the kind of society which Hughes appears to be overly optimistic will not emerge but which is more akin to Lee Silver’s prediction of the future with the distinction made between the "GenRich" and the "naturals”.  This being the case, the author proposes that we have two options: radical enhancement is either enforced across the board or banned outright. It is the latter option which Agar favours but crucially does not elaborate further on so it is unclear as to how he would attempt such a ban given the complexity of the issue. This is disappointing as any general initial reflections which the author felt able to offer would have added to the discussion and added further strength to his line of argument.
  • A Transhuman Manifesto The final focus for Agar is James Hughes, who published his transhumanist manifesto Citizen Cyborg in 2004. Given the direct connection with politics and public policy this for me was a particularly interesting read. The basic premise to Hughes argument is that once humans and post humans recognise each other as citizens then this will mark the point at which they will be able to get along with each other.
  • Agar takes to task the argument Bostrom made with Toby Ord, concerning claims against enhancement. Bostrom and Ord argue that it boils down to a preference for the status quo; current human intellects and life spans are preferred and deemed best because they are what we have now and what we are familiar with (p. 134).  Agar discusses the fact that in his view, Bostrom falls into a focalism – focusing on and magnifying the positives whilst ignoring the negative implications.  Moreover, Agar goes onto develop and reiterate his earlier point that the sort of radical enhancements Bostrom et al enthusiastically support and promote take us beyond what is human so they are no longer human. It therefore cannot be said to be human enhancement given the fact that the traits or capacities that such enhancement afford us would be in many respects superior to ours, but they would not be ours.
  • With his law of accelerating returns and talk of the Singularity Ray Kurzweil proposes that we are speeding towards a time when our outdated systems of neurons and synapses will be traded for far more efficient electronic circuits, allowing us to become artificially super-intelligent and transferring our minds from brains into machines.
  • Having laid out the main ideas and thinking behind Kurzweil’s proposals, Agar makes the perceptive comment that despite the apparent appeal of greater processing power it would nevertheless be no longer human. Introducing chips to the human body and linking into the human nervous system to computers as per Ray Kurzweil’s proposals will prove interesting but it goes beyond merely creating a copy of us in order to that future replication and uploading can take place. Rather it will constitute something more akin to an upgrade. Electrochemical signals that the brain use to achieve thought travel at 100 metres per second. This is impressive but contrast this with the electrical signals in a computer which travel at 300 million metres per second then the distinction is clear. If the predictions are true how will such radically enhanced and empowered beings live not only the unenhanced but also what will there quality of life really be? In response, Agar favours something what he calls “rational biological conservatism” (pg. 57) where we set limits on how intelligent we can become in light of the fact that it will never be rational to us for human beings to completely upload their minds onto computers.
  • Agar then proceeds to argue that in the pursuit of Kurzweil enhanced capacities and capabilities we might accidentally undermine capacities of equal value. This line of argument would find much sympathy from those who consider human organisms in “ecological” terms, representing a profound interconnectedness which when interfered with presents a series of unknown and unexpected consequences. In other words, our specifies-specific form of intelligence may well be linked to species-specific form of desire. Thus, if we start building upon and enhancing our capacity to protect and promote deeply held convictions and beliefs then due to the interconnectedness, it may well affect and remove our desire to perform such activities (page 70). Agar’s subsequent discussion and reference to the work of Jerry Foder, philosopher and cognitive scientist is particularly helpful in terms of the functioning of the mind by modules and the implications of human-friendly AI verses human-unfriendly AI.
  • In terms of the author’s discussion of Aubrey de Grey, what is refreshing to read from the outset is the author’s clear grasp of Aubrey’s ideas and motivation. Some make the mistake of thinking he is the man who wants to live forever, when in actual fact this is not the case.  De Grey wants to reverse the ageing process - Strategies for Engineered Negligible Senescence (SENS) so that people are living longer and healthier lives. Establishing this clear distinction affords the author the opportunity to offer more grounded critiques of de Grey’s than some of his other critics. The author makes plain that de Grey’s immediate goal is to achieve longevity escape velocity (LEV), where anti-ageing therapies add years to life expectancy faster than age consumes them.
  • In weighing up the benefits of living significantly longer lives, Agar posits a compelling argument that I had not fully seen before. In terms of risk, those radically enhanced to live longer may actually be the most risk adverse and fearful people to live. Taking the example of driving a car, a forty year-old senescing human being who gets into their car to drive to work and is involved in a fatal accident “stands to lose, at most, a few healthy, youthful years and a slightly larger number of years with reduced quality” (p.116). In stark contrast should a negligibly senescent being who drives a car and is involved in an accident resulting in their death, stands to lose on average one thousand, healthy, youthful years (p.116).  
  • De Grey’s response to this seems a little flippant; with the end of ageing comes an increased sense of risk-aversion so the desire for risky activity such as driving will no longer be prevalent. Moreover, plus because we are living for longer we will not be in such a hurry to get to places!  Virtual reality comes into its own at this point as a means by which the negligibly senescent being ‘adrenaline junkie’ can be engaged with activities but without the associated risks. But surely the risk is part of the reason why they would want to engage in snow boarding, bungee jumping et al in the first place. De Grey’s strategy seemingly fails to appreciate the extent to which human beings want “direct” contact with the “real” world.
  • Continuing this idea further though, Agar’s subsequent discussion of the role of fire-fighters is an interesting one.  A negligibly senescent fire fighter may stand to loose more when they are trapped in a burning inferno but being negligibly senescent means that they are better fire-fighters by virtue of increase vitality. Having recently heard de Grey speak and had the privilege of discussing his ideas further with him, Agar’s discussion of De Grey were a particular highlight of the book and made for an engaging discussion. Whilst expressing concern and doubt in relation to De Grey’s ideas, Agar is nevertheless quick and gracious enough to acknowledge that if such therapies could be achieved then De Grey is probably the best person to comment on and achieve such therapies given the depth of knowledge and understanding that he has built up in this area.
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