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Weiye Loh

New voting methods and fair elections : The New Yorker - 0 views

www.newyorker.com/...100726crbo_books_gottlieb

Democracy Methodology

shared by Weiye Loh on 29 Jul 10 - Cached
  • history of voting math comes mainly in two chunks: the period of the French Revolution, when some members of France’s Academy of Sciences tried to deduce a rational way of conducting elections, and the nineteen-fifties onward, when economists and game theorists set out to show that this was impossible
  • The first mathematical account of vote-splitting was given by Jean-Charles de Borda, a French mathematician and a naval hero of the American Revolutionary War. Borda concocted examples in which one knows the order in which each voter would rank the candidates in an election, and then showed how easily the will of the majority could be frustrated in an ordinary vote. Borda’s main suggestion was to require voters to rank candidates, rather than just choose one favorite, so that a winner could be calculated by counting points awarded according to the rankings. The key idea was to find a way of taking lower preferences, as well as first preferences, into account.Unfortunately, this method may fail to elect the majority’s favorite—it could, in theory, elect someone who was nobody’s favorite. It is also easy to manipulate by strategic voting.
  • If the candidate who is your second preference is a strong challenger to your first preference, you may be able to help your favorite by putting the challenger last. Borda’s response was to say that his system was intended only for honest men.
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  • After the Academy dropped Borda’s method, it plumped for a simple suggestion by the astronomer and mathematician Pierre-Simon Laplace, who was an important contributor to the theory of probability. Laplace’s rule insisted on an over-all majority: at least half the votes plus one. If no candidate achieved this, nobody was elected to the Academy.
  • Another early advocate of proportional representation was John Stuart Mill, who, in 1861, wrote about the critical distinction between “government of the whole people by the whole people, equally represented,” which was the ideal, and “government of the whole people by a mere majority of the people exclusively represented,” which is what winner-takes-all elections produce. (The minority that Mill was most concerned to protect was the “superior intellects and characters,” who he feared would be swamped as more citizens got the vote.)
  • The key to proportional representation is to enlarge constituencies so that more than one winner is elected in each, and then try to align the share of seats won by a party with the share of votes it receives. These days, a few small countries, including Israel and the Netherlands, treat their entire populations as single constituencies, and thereby get almost perfectly proportional representation. Some places require a party to cross a certain threshold of votes before it gets any seats, in order to filter out extremists.
  • The main criticisms of proportional representation are that it can lead to unstable coalition governments, because more parties are successful in elections, and that it can weaken the local ties between electors and their representatives. Conveniently for its critics, and for its defenders, there are so many flavors of proportional representation around the globe that you can usually find an example of whatever point you want to make. Still, more than three-quarters of the world’s rich countries seem to manage with such schemes.
  • The alternative voting method that will be put to a referendum in Britain is not proportional representation: it would elect a single winner in each constituency, and thus steer clear of what foreigners put up with. Known in the United States as instant-runoff voting, the method was developed around 1870 by William Ware
  • In instant-runoff elections, voters rank all or some of the candidates in order of preference, and votes may be transferred between candidates. The idea is that your vote may count even if your favorite loses. If any candidate gets more than half of all the first-preference votes, he or she wins, and the game is over. But, if there is no majority winner, the candidate with the fewest first-preference votes is eliminated. Then the second-preference votes of his or her supporters are distributed to the other candidates. If there is still nobody with more than half the votes, another candidate is eliminated, and the process is repeated until either someone has a majority or there are only two candidates left, in which case the one with the most votes wins. Third, fourth, and lower preferences will be redistributed if a voter’s higher preferences have already been transferred to candidates who were eliminated earlier.
  • At first glance, this is an appealing approach: it is guaranteed to produce a clear winner, and more voters will have a say in the election’s outcome. Look more closely, though, and you start to see how peculiar the logic behind it is. Although more people’s votes contribute to the result, they do so in strange ways. Some people’s second, third, or even lower preferences count for as much as other people’s first preferences. If you back the loser of the first tally, then in the subsequent tallies your second (and maybe lower) preferences will be added to that candidate’s first preferences. The winner’s pile of votes may well be a jumble of first, second, and third preferences.
  • Such transferrable-vote elections can behave in topsy-turvy ways: they are what mathematicians call “non-monotonic,” which means that something can go up when it should go down, or vice versa. Whether a candidate who gets through the first round of counting will ultimately be elected may depend on which of his rivals he has to face in subsequent rounds, and some votes for a weaker challenger may do a candidate more good than a vote for that candidate himself. In short, a candidate may lose if certain voters back him, and would have won if they hadn’t. Supporters of instant-runoff voting say that the problem is much too rare to worry about in real elections, but recent work by Robert Norman, a mathematician at Dartmouth, suggests otherwise. By Norman’s calculations, it would happen in one in five close contests among three candidates who each have between twenty-five and forty per cent of first-preference votes. With larger numbers of candidates, it would happen even more often. It’s rarely possible to tell whether past instant-runoff elections have gone topsy-turvy in this way, because full ballot data aren’t usually published. But, in Burlington’s 2006 and 2009 mayoral elections, the data were published, and the 2009 election did go topsy-turvy.
  • Kenneth Arrow, an economist at Stanford, examined a set of requirements that you’d think any reasonable voting system could satisfy, and proved that nothing can meet them all when there are more than two candidates. So designing elections is always a matter of choosing a lesser evil. When the Royal Swedish Academy of Sciences awarded Arrow a Nobel Prize, in 1972, it called his result “a rather discouraging one, as regards the dream of a perfect democracy.” Szpiro goes so far as to write that “the democratic world would never be the same again,
  • There is something of a loophole in Arrow’s demonstration. His proof applies only when voters rank candidates; it would not apply if, instead, they rated candidates by giving them grades. First-past-the-post voting is, in effect, a crude ranking method in which voters put one candidate in first place and everyone else last. Similarly, in the standard forms of proportional representation voters rank one party or group of candidates first, and all other parties and candidates last. With rating methods, on the other hand, voters would give all or some candidates a score, to say how much they like them. They would not have to say which is their favorite—though they could in effect do so, by giving only him or her their highest score—and they would not have to decide on an order of preference for the other candidates.
  • One such method is widely used on the Internet—to rate restaurants, movies, books, or other people’s comments or reviews, for example. You give numbers of stars or points to mark how much you like something. To convert this into an election method, count each candidate’s stars or points, and the winner is the one with the highest average score (or the highest total score, if voters are allowed to leave some candidates unrated). This is known as range voting, and it goes back to an idea considered by Laplace at the start of the nineteenth century. It also resembles ancient forms of acclamation in Sparta. The more you like something, the louder you bash your shield with your spear, and the biggest noise wins. A recent variant, developed by two mathematicians in Paris, Michel Balinski and Rida Laraki, uses familiar language rather than numbers for its rating scale. Voters are asked to grade each candidate as, for example, “Excellent,” “Very Good,” “Good,” “Insufficient,” or “Bad.” Judging politicians thus becomes like judging wines, except that you can drive afterward.
  • Range and approval voting deal neatly with the problem of vote-splitting: if a voter likes Nader best, and would rather have Gore than Bush, he or she can approve Nader and Gore but not Bush. Above all, their advocates say, both schemes give voters more options, and would elect the candidate with the most over-all support, rather than the one preferred by the largest minority. Both can be modified to deliver forms of proportional representation.
  • Whether such ideas can work depends on how people use them. If enough people are carelessly generous with their approval votes, for example, there could be some nasty surprises. In an unlikely set of circumstances, the candidate who is the favorite of more than half the voters could lose. Parties in an approval election might spend less time attacking their opponents, in order to pick up positive ratings from rivals’ supporters, and critics worry that it would favor bland politicians who don’t stand for anything much. Defenders insist that such a strategy would backfire in subsequent elections, if not before, and the case of Ronald Reagan suggests that broad appeal and strong views aren’t mutually exclusive.
  • Why are the effects of an unfamiliar electoral system so hard to puzzle out in advance? One reason is that political parties will change their campaign strategies, and voters the way they vote, to adapt to the new rules, and such variables put us in the realm of behavior and culture. Meanwhile, the technical debate about electoral systems generally takes place in a vacuum from which voters’ capriciousness and local circumstances have been pumped out. Although almost any alternative voting scheme now on offer is likely to be better than first past the post, it’s unrealistic to think that one voting method would work equally well for, say, the legislature of a young African republic, the Presidency of an island in Oceania, the school board of a New England town, and the assembly of a country still scarred by civil war. If winner takes all is a poor electoral system, one size fits all is a poor way to pick its replacements.
  • Mathematics can suggest what approaches are worth trying, but it can’t reveal what will suit a particular place, and best deliver what we want from a democratic voting system: to create a government that feels legitimate to people—to reconcile people to being governed, and give them reason to feel that, win or lose (especially lose), the game is fair.
  • Weiye Loh on 29 Jul 10
     
    WIN OR LOSE No voting system is flawless. But some are less democratic than others. by Anthony Gottlieb
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

Climategate Coverage: Unfair & Unbalanced « Global Warming: Man or Myth? - 0 views

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ClimateGate media bias

shared by Weiye Loh on 08 Jul 10 - Cached
  • There have been several investigations into the allegations of misconduct by Drs. Jones and Mann and every one has shown that the allegations were baseless.
  • Did these exonerations receive the same coverage as the groundless accusations? NO! Not even close.
  • Web Coverage: I used Google to search the Web for the following phrases: “phil jones” climatic research unit “michael mann” climatic research unit For each search I used two filters.  1)  Web hits in the first two weeks after the Climategate story broke (11/19/09 – 12/03/09) and 2) Web hits in the first two weeks after Dr. Jones’ exoneration (03/31/2010 to 04/14/2010) and the first two weeks after Dr. Mann’s exoneration (02/03/2010 to 02/17/2010).  It should be noted that using the term climate research unit instead of climatic research unit (a common mistake) did not alter the results significantly.  The results appear below:
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  • Dr. Phil Jones accused of wrongdoing resulted in 64,700 hits in the first two weeks after the story broke. Dr. Michael Mann accused of wrongdoing resulted in 39,000 hits in the first two weeks after the story broke. When Dr. Jones was exonerated there were only 22,700 hits in the first two weeks after exoneration. When Dr. Mann was exonerated there were only 17,700 hits in the first two weeks after exoneration.
  • the Web is fast and loose with the truth so let us take a look at how news organizations reported.  I used Google News to do the search.  Google News is a computer-generated news site that aggregates headlines from news sources worldwide.  Google News archives only allow a monthly filter.  Climategate broke on November 19, 2009 so I filtered the search to include just November 2009 (11 days) and December 2009.  Because Dr. Mann was exonerated in early February, February 2010 was used as the filter.  Dr. Jones first and most important exoneration was released on March 31, 2010 so I included April 2010 in the filter and the seven news stories from March 31, 2010 that appeared in the March 2010 list.
  • Dr. Phil Jones accused of wrongdoing resulted in 263 headlines in the first 42 days after the story broke. Dr. Michael Mann accused of wrongdoing resulted in 143 headlines in the first 42 days after the story broke. When Dr. Jones was exonerated there were only 24 headlines in the first 19 days after exoneration. When Dr. Mann was exonerated there were only 27 headlines in the first 25 days after exoneration.
  • Weiye Loh on 08 Jul 10
     
    Climategate Coverage: Unfair & Unbalanced
Weiye Loh

Net-Neutrality: The First Amendment of the Internet | LSE Media Policy Project - 0 views

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Net Neutrality

shared by Weiye Loh on 30 Mar 11 - No Cached
  • debates about the nature, the architecture and the governing principles of the internet are not merely technical or economic discussions.  Above all, these debates have deep political, social, and cultural implications and become a matter of public, national and global interest.
  • In many ways, net neutrality could be considered the first amendment of the internet; no pun intended here. However, just as with freedom of speech the principle of net neutrality cannot be approached as absolute or as a fetish. Even in a democracy we cannot say everything applies all the time in all contexts. Limiting the core principle of freedom of speech in a democracy is only possible in very specific circumstances, such as harm, racism or in view of the public interest. Along the same lines, compromising on the principle of net neutrality should be for very specific and clearly defined reasons that are transparent and do not serve commercial private interests, but rather public interests or are implemented in view of guaranteeing an excellent quality of service for all.
  • One of the only really convincing arguments of those challenging net neutrality is that due to the dramatic increases in streaming activity and data-exchange through peer-to-peer networks, the overall quality of service risks being compromised if we stick to data being treated on a first come first serve basis. We are being told that popular content will need to be stored closer to the consumer, which evidently comes at an extra cost.
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  • Implicitly two separate debates are being collapsed here and I would argue that we need to separate both. The first one relates to the stability of the internet as an information and communication infrastructure because of the way we collectively use that infrastructure. The second debate is whether ISPs and telecommunication companies should be allowed to differentiate in their pricing between different levels of quality of access, both towards consumers and content providers.
  • Just as with freedom of speech, circumstances can be found in which the principle while still cherished and upheld, can be adapted and constrained to some extent. To paraphrase Tim Wu (2008), the aspiration should still be ‘to treat all content, sites, and platforms equally’, but maybe some forms of content should be treated more equally than others in order to guarantee an excellent quality of service for all. However, the societal and political implications of this need to be thought through in detail and as with freedom of speech itself, it will, I believe, require strict regulation and conditions.
  • In regards to the first debate on internet stability, a case can be made for allowing internet operators to differentiate between different types of data with different needs – if for any reason the quality of service of the internet as a whole cannot be guaranteed anymore. 
  • Concerning the second debate on differential pricing, it is fair to say that from a public interest and civic liberty perspective the consolidation and institutionalization of a commercially driven two-tiered internet is not acceptable and impossible to legitimate. As is allowing operators to differentiate in the quality of provision of certain kind of content above others.  A core principle such as net neutrality should never be relinquished for the sake of private interests and profit-making strategies – on behalf of industry or for others. If we need to compromise on net neutrality it would always have to be partial, to be circumscribed and only to improve the quality of service for all, not just for the few who can afford it.
  • Separating these two debates exposes the crux of the current net-neutrality debate. In essence, we are being urged to give up on the principle of net-neutrality to guarantee a good quality of service.  However, this argument is actually a pre-text for the telecom industry to make content-providers pay for the facilitation of access to their audiences – the internet subscribers. And this again can be linked to another debate being waged amongst content providers: how do we make internet users pay for the content they access online? I won’t open that can of worms here, but I will make my point clear.  Telecommunication industry efforts to make content providers pay for access to their audiences do not offer legitimate reasons to suspend the first amendment of the internet.
Weiye Loh

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

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Truth Science Evidence Data

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.
  • 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.
  • 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.”
  • 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.”
  • 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

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

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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

Short Sharp Science: Computer beats human at Japanese chess for first time - 0 views

www.newscientist.com/...ter-makes-the-winning-mov.html

Computer Human Chess

shared by Weiye Loh on 21 Oct 10 - No Cached
  • A computer has beaten a human at shogi, otherwise known as Japanese chess, for the first time.
  • computers have been beating humans at western chess for years, and when IBM's Deep Blue beat Gary Kasparov in 1997, it was greeted in some quarters as if computers were about to overthrow humanity. That hasn't happened yet, but after all, western chess is a relatively simple game, with only about 10123 possible games existing that can be played out. Shogi is a bit more complex, though, offering about 10224 possible games.
  • Japan's national broadcaster, NHK, reported that Akara "aggressively pursued Shimizu from the beginning". It's the first time a computer has beaten a professional human player.
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  • The Japan Shogi Association, incidentally, seems to have a deep fear of computers beating humans. In 2005, it introduced a ban on professional members playing computers without permission, and Shimizu's defeat was the first since a simpler computer system was beaten by a (male) champion, Akira Watanabe, in 2007.
  • Perhaps the association doesn't mind so much if a woman is beaten: NHK reports that the JSA will conduct an in-depth analysis of the match before it decides whether to allow the software to challenge a higher-ranking male professional player.
  • Weiye Loh on 21 Oct 10
     
    Computer beats human at Japanese chess for first time
Weiye Loh

The Creativity Crisis - Newsweek - 0 views

www.newsweek.com/...the-creativity-crisis.html

Creativity Education

shared by Weiye Loh on 11 Jul 10 - Cached
  • The accepted definition of creativity is production of something original and useful, and that’s what’s reflected in the tests. There is never one right answer. To be creative requires divergent thinking (generating many unique ideas) and then convergent thinking (combining those ideas into the best result).
  • Torrance’s tasks, which have become the gold standard in creativity assessment, measure creativity perfectly. What’s shocking is how incredibly well Torrance’s creativity index predicted those kids’ creative accomplishments as adults.
  • The correlation to lifetime creative accomplishment was more than three times stronger for childhood creativity than childhood IQ.
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  • there is one crucial difference between IQ and CQ scores. With intelligence, there is a phenomenon called the Flynn effect—each generation, scores go up about 10 points. Enriched environments are making kids smarter. With creativity, a reverse trend has just been identified and is being reported for the first time here: American creativity scores are falling.
  • creativity scores had been steadily rising, just like IQ scores, until 1990. Since then, creativity scores have consistently inched downward.
  • It is the scores of younger children in America—from kindergarten through sixth grade—for whom the decline is “most serious.”
  • It’s too early to determine conclusively why U.S. creativity scores are declining. One likely culprit is the number of hours kids now spend in front of the TV and playing videogames rather than engaging in creative activities. Another is the lack of creativity development in our schools. In effect, it’s left to the luck of the draw who becomes creative: there’s no concerted effort to nurture the creativity of all children.
  • Around the world, though, other countries are making creativity development a national priority.
  • In China there has been widespread education reform to extinguish the drill-and-kill teaching style. Instead, Chinese schools are also adopting a problem-based learning approach.
  • When faculty of a major Chinese university asked Plucker to identify trends in American education, he described our focus on standardized curriculum, rote memorization, and nationalized testing.
  • Overwhelmed by curriculum standards, American teachers warn there’s no room in the day for a creativity class.
  • The age-old belief that the arts have a special claim to creativity is unfounded. When scholars gave creativity tasks to both engineering majors and music majors, their scores laid down on an identical spectrum, with the same high averages and standard deviations.
  • The argument that we can’t teach creativity because kids already have too much to learn is a false trade-off. Creativity isn’t about freedom from concrete facts. Rather, fact-finding and deep research are vital stages in the creative process.
  • The lore of pop psychology is that creativity occurs on the right side of the brain. But we now know that if you tried to be creative using only the right side of your brain, it’d be like living with ideas perpetually at the tip of your tongue, just beyond reach.
  • Creativity requires constant shifting, blender pulses of both divergent thinking and convergent thinking, to combine new information with old and forgotten ideas. Highly creative people are very good at marshaling their brains into bilateral mode, and the more creative they are, the more they dual-activate.
  • “Creativity can be taught,” says James C. Kaufman, professor at California State University, San Bernardino. What’s common about successful programs is they alternate maximum divergent thinking with bouts of intense convergent thinking, through several stages. Real improvement doesn’t happen in a weekend workshop. But when applied to the everyday process of work or school, brain function improves.
  • highly creative adults tended to grow up in families embodying opposites. Parents encouraged uniqueness, yet provided stability. They were highly responsive to kids’ needs, yet challenged kids to develop skills. This resulted in a sort of adaptability: in times of anxiousness, clear rules could reduce chaos—yet when kids were bored, they could seek change, too. In the space between anxiety and boredom was where creativity flourished.
  • highly creative adults frequently grew up with hardship. Hardship by itself doesn’t lead to creativity, but it does force kids to become more flexible—and flexibility helps with creativity.
  • In early childhood, distinct types of free play are associated with high creativity. Preschoolers who spend more time in role-play (acting out characters) have higher measures of creativity: voicing someone else’s point of view helps develop their ability to analyze situations from different perspectives. When playing alone, highly creative first graders may act out strong negative emotions: they’ll be angry, hostile, anguished.
  • In middle childhood, kids sometimes create paracosms—fantasies of entire alternative worlds. Kids revisit their paracosms repeatedly, sometimes for months, and even create languages spoken there. This type of play peaks at age 9 or 10, and it’s a very strong sign of future creativity.
  • From fourth grade on, creativity no longer occurs in a vacuum; researching and studying become an integral part of coming up with useful solutions. But this transition isn’t easy. As school stuffs more complex information into their heads, kids get overloaded, and creativity suffers. When creative children have a supportive teacher—someone tolerant of unconventional answers, occasional disruptions, or detours of curiosity—they tend to excel. When they don’t, they tend to underperform and drop out of high school or don’t finish college at high rates.
  • They’re quitting because they’re discouraged and bored, not because they’re dark, depressed, anxious, or neurotic. It’s a myth that creative people have these traits. (Those traits actually shut down creativity; they make people less open to experience and less interested in novelty.) Rather, creative people, for the most part, exhibit active moods and positive affect. They’re not particularly happy—contentment is a kind of complacency creative people rarely have. But they’re engaged, motivated, and open to the world.
  • A similar study of 1,500 middle schoolers found that those high in creative self-efficacy had more confidence about their future and ability to succeed. They were sure that their ability to come up with alternatives would aid them, no matter what problems would arise.
  • Weiye Loh on 11 Jul 10
     
    The Creativity Crisis For the first time, research shows that American creativity is declining. What went wrong-and how we can fix it.
Weiye Loh

Meet the man who broke the vaccine-autism scandal - The Globe and Mail - 0 views

www.theglobeandmail.com/...singlepage

Truth Ethics Journalism Vaccine Medicine Health

shared by Weiye Loh on 21 Feb 11 - No Cached
  • Brian Deer radiates a remarkably bland persona for someone who stunned the global medical community and unravelled what he calls “one of those Aristotelian stories where you have both pity and fear.” This is the journalist behind the series of stories that completely discredited the research linking the measles mumps rubella (MMR) vaccine to autism. First published in The Lancet in 1998, it unleashed a worldwide public health scare and gave distressed parents of autistic children a place to lay blame for the devastation of the diagnosis.
  • Seven years ago, Mr. Deer, a freelance journalist who works mostly for The Sunday Times in London, began an investigation into research conducted in the 1990s, which had spawned a worldwide debate about the safety and well-being of children. The published research showed a link between the MMR vaccine, routinely given to children in the first years of life, to the onset of autism, a developmental disorder that appears in the first three years, and affects a child’s social behaviour and communication skills. Out of fear, many parents refused to immunize their children.The final outcome of Mr. Deer’s investigation came last month, when Andrew Wakefield, the lead researcher, as well as two of his colleagues, saw their reputations torn to shreds in a medical misconduct inquiry, the longest in history, by the General Medical Council in the United Kingdom. More than 30 charges, including four counts of dishonesty in regard to money, research and public statements, were proven against Dr. Wakefield. The Lancet retracted the paper in 2010.
  • The MMR research paper, which triggered a high-profile anti-vaccine campaign, led by such celebrities as actress Jenny McCarthy, involved 12 children between the ages of three and nine. All had brain disorders. The parents of eight of them reported that signs of autism arose within days of the children receiving the MMR vaccine.“It was just too cute,” Mr. Deer says of the findings. Through the Freedom of Information Act, he discovered that Dr. Wakefield’s research had been funded by the British Legal Aid fund, and that the children had been recruited through lawyers and anti-vaccine groups.
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  • Dr. Wakefield sued him and The Sunday Times for libel, but later withdrew the charges and was forced to pay Mr. Deer’s legal costs, which amounted to £1.4 million (almost $3-million). In the subsequent medical inquiry, Dr. Wakefield was shown to have had “a callous disregard” for the “distress and pain” of the developmentally challenged children, some of whom were subjected to invasive “high risk” procedures, including lumbar punctures, without clinical reasons.After the first story ran in 2004, Mr. Deer, who is unmarried and has no children, also revealed that Dr. Wakefield had patented a single measles vaccine after creating fear about the standard MMR shot.
  • To this day, Dr. Wakefield remains unrepentant. He boycotted the legal inquiry just as he has avoided any interview with Mr. Deer. A father of four children, he has a large ranch in Austin, Texas. Some parents in the anti-vaccine community, enabled by the Internet, have falsely accused Mr. Deer of mounting a kangaroo court against Dr. Wakefield.
  • While the consequences of Dr. Wakefield’s research were serious – immunization rates in Britain dropped dramatically and measles outbreaks ensued – it also gave parents of autistic children a purpose (however ill-founded) in which to find solace. How does he feel about taking that away?“I can’t think through the consequences of trying to tell the truth,” he stutters, seemingly surprised by the question. After a thoughtful pause he adds: “I think those parents are freer for having the truth than being caught in denial and deception.”
    • Weiye Loh
      Weiye Loh on 21 Feb 11
       
      Truth hurts. That's why people prefer to live in denial. 
Weiye Loh

homunculus: I can see clearly now - 0 views

philipball.blogspot.com/...i-can-see-clearly-now.html

Instrument Microscope Science Latour History Images Representation

shared by Weiye Loh on 06 Mar 11 - No Cached
  • Here’s a little piece I wrote for Nature news. To truly appreciate this stuff you need to take a look at the slideshow. There will be a great deal more on early microscopy in my next book, probably called Curiosity and scheduled for next year.
  • The first microscopes were a lot better than they are given credit for. That’s the claim of microscopist Brian Ford, based at Cambridge University and a specialist in the history and development of these instruments.
  • Ford says it is often suggested that the microscopes used by the earliest pioneers in the seventeenth century, such as Robert Hooke and Antony van Leeuwenhoek, gave only very blurred images of structures such as cells and micro-organisms. Hooke was the first to record cells, seen in thin slices of cork, while Leeuwenhoek described tiny ‘animalcules’, invisible to the naked eye, in rain water in 1676. The implication is that these breakthroughs in microscopic biology involved more than a little guesswork and invention. But Ford has looked again at the capabilities of some of Leeuwenhoek’s microscopes, and says ‘the results were breathtaking’. ‘The images were comparable with those you would obtain from a modern light microscope’, he adds in an account of his experiments in Microscopy and Analysis [1].
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  • The poor impression of the seventeenth-century instruments, says Ford, is due to bad technique in modern reconstructions. In contrast to the hazy images shown in some museums and television documentaries, careful attention to such factors as lighting can produce micrographs of startling clarity using original microscopes or modern replicas.
  • Ford was able to make some of these improvements when he was granted access to one of Leeuwenhoek’s original microscopes owned by the Utrecht University Museum in the Netherlands. Leeuwenhoek made his own instruments, which had only a single lens made from a tiny bead of glass mounted in a metal frame. These simple microscopes were harder to make and to use than the more familiar two-lens compound microscope, but offered greater resolution.
  • Hooke popularized microscopy in his 1665 masterpiece Micrographia, which included stunning engravings of fleas, mites and the compound eyes of flies. The diarist Samuel Pepys judged it ‘the most ingenious book that I ever read in my life’. Ford’s findings show that Hooke was not, as some have imagined, embellishing his drawings from imagination, but should genuinely have been able to see such things as the tiny hairs on the flea’s legs.
  • Even Hooke was temporarily foxed, however, when he was given the duty of reproducing the results described by Leeuwenhoek, a linen merchant of Delft, in a letter to the Royal Society. It took him over a year before he could see these animalcules, whereupon he wrote that ‘I was very much surprised at this so wonderful a spectacle, having never seen any living creature comparable to these for smallness.’ ‘The abilities of those pioneer microscopists were so much greater than has been recognized’ says Ford. He attributes this misconception to the fact that ‘no longer is microscopy properly taught.’
  • Reference1. Ford, B. J. Microsc. Anal. March 2011 (in press).
  • Weiye Loh on 06 Mar 11
     
    The first microscopes were a lot better than they are given credit for.
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

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

256.com/...strong_inference.html

Science Statistics Media Representation Methodolatry Falsifiability

shared by Weiye Loh on 20 Mar 11 - No Cached
  • 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.
  • ...28 more annotations...
  • 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

Commentary: SingTel launches Singapore's first "priority lane" broadband plans | Techgo... - 0 views

www.techgoondu.com/...-priority-lane-broadband-plans

Net Neutrality Capitalism Technology Internet

shared by Weiye Loh on 13 Jun 11 - No Cached
  • Weiye Loh on 13 Jun 11
     
    Until now, all broadband plans here - and in most parts of the world - are sold according to the promised top speeds that are hardly reached in everyday use because of factors like network congestion and wireless coverage. SingTel said as much today when it launched its first-of-its-kind services. Executive vice president of digital consumer, Yuen Kuan Moon, pointed to the many users who have complained that they never got the speeds they paid for, especially when mobile networks are clogged with 145 per cent cellphone penetration in Singapore. SingTel's answer? Give priority to those who pay more, and try to let them know what speeds they will typically get.
Weiye Loh

Fukushima babies and how numbers can lie - Boing Boing - 0 views

www.boingboing.net/...fukushima-babies-and.html

Corporation Data Bias Information Capitalism Reality

shared by Weiye Loh on 28 Jun 11 - No Cached
  • Over at Scientific American, Michael Moyer takes a critical look at an Al Jazeera story about a recent study purporting to show that infant deaths on the American West Coast increased by 35% as a result of fallout from the Fukushima Daiichi nuclear power plant meltdown.
  • At first glance, the story looks credible. And scary. The information comes from a physician, Janette Sherman MD, and epidemiologist Joseph Mangano, who got their data from the Centers for Disease Control and Prevention's Morbidity and Mortality Weekly Reports—a newsletter that frequently helps public health officials spot trends in death and illness.
  • Look closer, though, and the credibility vanishes. For one thing, this isn't a formal scientific study and Sherman and Mangano didn't publish their findings in a peer-reviewed journal, or even on a science blog. Instead, all of this comes from an essay the two wrote for Counter Punch, a political newsletter.
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  • Let's first consider the data that the authors left out of their analysis. It's hard to understand why the authors stopped at these eight cities. Why include Boise but not Tacoma? Or Spokane? Both have about the same size population as Boise, they're closer to Japan, and the CDC includes data from Tacoma and Spokane in the weekly reports.
  • More important, why did the authors choose to use only the four weeks preceding the Fukushima disaster? Here is where we begin to pick up a whiff of data fixing. ... While it certainly is true that there were fewer deaths in the four weeks leading up to Fukushima than there have been in the 10 weeks following, the entire year has seen no overall trend. When I plotted a best-fit line to the data, Excel calculated a very slight decrease in the infant mortality rate. Only by explicitly excluding data from January and February were Sherman and Mangano able to froth up their specious statistical scaremongering.
  • Weiye Loh on 28 Jun 11
     
    When you think about what information be skeptical of, that decision can't begin and end with "corporate interests." Yes, those sources often give you bad information. But bad information comes from other places, too. The Fukushima accident was worse than TEPCO wanted people to believe when it first happened. Radiation isn't healthy for you, and there are people (plant workers, emergency crews, people who lived nearby) who will be dealing with the effects of Fukushima for years to come. But the fact that all of that is true does not mean that we should uncritically accept it when somebody says that radiation from Fukushima is killing babies in the United States. Just because the corporate interests are in the wrong doesn't mean that every claim against them is true.
Weiye Loh

Is Assange the "world-spirit embodied"? A Hegel scholar reports fro... - 0 views

www.opendemocracy.net/...5%BEi%C5%BEekassange-troxy-gig

wikileaks Philosophy Open Data Open Government

shared by Weiye Loh on 10 Jul 11 - No Cached
  • Although the atmosphere at the Troxy was very genial, and Žižek generally enthusiastic about WikiLeaks (as he was in the London Review of Books article he published about it), there was a distinct tension between the rather standard Enlightenment rhetoric employed by Assange (more facts, a more complete historical record, better educated journalists)  and the significantly more radical conclusions the philosopher was drawing. This is why - whilst it should no doubt be read in a similar light as Žižek’s own remarks on his position during the conversation (I feel now like that Stalinist commentator: the leader has spoken, I provide the deeper meaning) - the ventured analogy nevertheless contains a kernel of truth beyond its bombast: defining the emancipatory significance of phenomena should not be left to the actors alone.
  • in response to Goodman's initial question on the significance of the Iraq war logs, Assange primarily emphasized the concrete revelations WikiLeaks had provided. He mentioned the 400.000 cables leaked, 15.000 previously unreported deaths revealed, a video of an American helicopter mowing down civilians, and so on. In contrast, Žižek went far enough to say that even if WikiLeaks had not revealed a single new thing, it should be considered game-changing. Why? Because of the very way it functions. For the philosopher, our democracies not only have rules regarding what can be revealed, but also rules which regulate the transgression of those first rules (the independent press, NGOs, etc). The contention then is that WikiLeaks operates outside both these sets of rules, and that there is the source of its power.
  • the reply was firmly anchored in the key trope Žižek has championed since his first major work in English: that ideology in today's "post-ideological" world is not dead, but rather more powerful than ever - alive not so much on the level of knowledge but in the ways it structures social reality itself.
  • ...2 more annotations...
  • Žižek points out, the innocence of the accusers is anything but innocent; they decry the violence of WikiLeaks revelations, themselves oblivious to the military, economic, political and social framework of everyday violence that goes unmentioned in public discourse. The violence of leaks is on a formal level, and precisely this is at the root of the Slovene’s exclamation to Assange: “Yes, you are a terrorist, but by God, then what are they?”
  • WikiLeaks should not be seen as merely another chapter in investigative journalism and free flow of information, but a positive, subversive emancipatory force by virtue of the way it operates outside the system of secrets and allowed revelations. What then remains ahead is the hard task of keeping this subversive strength alive.
  • Weiye Loh on 10 Jul 11
     
     in response to Goodman's initial question on the significance of the Iraq war logs, Assange primarily emphasized the concrete revelations WikiLeaks had provided. He mentioned the 400.000 cables leaked, 15.000 previously unreported deaths revealed, a video of an American helicopter mowing down civilians, and so on. In contrast, Žižek went far enough to say that even if WikiLeaks had not revealed a single new thing, it should be considered game-changing. Why? Because of the very way it functions. For the philosopher, our democracies not only have rules regarding what can be revealed, but also rules which regulate the transgression of those first rules (the independent press, NGOs, etc). The contention then is that WikiLeaks operates outside both these sets of rules, and that there is the source of its power.
Weiye Loh

Wk 4 Online censorship & digital access: Mormon Church Attacks Wikileaks - 6 views

WIKILEAK RELEASES SECRET CHURCH DOCUMENTS! The First Link is an article regarding Wikileaks releasing a 'copyrighted' and confidential Church document of the Mormons (also known as the Church of J...

Mormons Scientology Wikileaks Copyright Censorship

started by guanyou chen on 31 Aug 09 1 follow-up, last by Weiye Loh on 31 Aug 09
Jude John

What's so Original in Academic Research? - 26 views

Thanks for your comments. I may have appeared to be contradictory, but what I really meant was that ownership of IP should not be a motivating factor to innovate. I realise that in our capitalistic...

started by Weiye Loh on 21 Aug 09 4 follow-ups, last by Jude John on 25 Aug 09
joanne ye

TJC Stomp Scandal - 34 views

This is a very interesting topic. Thanks, Weiman! From the replies for this topic, I would say two general questions surfaced. Firstly, is STOMP liable for misinformation? Secondly, is it right for...

started by Weiman Kow on 19 Aug 09 3 follow-ups, last by joanne ye on 26 Aug 09
Chen Guo Lim

Anti plagiarism is (un)ethical - 20 views

I think there is a need to investigate the motivation behind using these softwares. Suppose a writer has recently come across an article that seemingly have plagiarised, thus using the software to ...

Turnitin plagiarism

started by Weiye Loh on 21 Aug 09 4 follow-ups, last by Chen Guo Lim on 26 Aug 09
Weiye Loh

CancerGuide: The Median Isn't the Message - 0 views

cancerguide.org/median_not_msg.html

Statistics Science Cancer

shared by Weiye Loh on 08 Aug 10 - Cached
  • Statistics recognizes different measures of an "average," or central tendency. The mean is our usual concept of an overall average - add up the items and divide them by the number of sharers
  • The median, a different measure of central tendency, is the half-way point.
  • A politician in power might say with pride, "The mean income of our citizens is $15,000 per year." The leader of the opposition might retort, "But half our citizens make less than $10,000 per year." Both are right, but neither cites a statistic with impassive objectivity. The first invokes a mean, the second a median. (Means are higher than medians in such cases because one millionaire may outweigh hundreds of poor people in setting a mean; but he can balance only one mendicant in calculating a median).
  • ...7 more annotations...
  • The larger issue that creates a common distrust or contempt for statistics is more troubling. Many people make an unfortunate and invalid separation between heart and mind, or feeling and intellect. In some contemporary traditions, abetted by attitudes stereotypically centered on Southern California, feelings are exalted as more "real" and the only proper basis for action - if it feels good, do it - while intellect gets short shrift as a hang-up of outmoded elitism. Statistics, in this absurd dichotomy, often become the symbol of the enemy. As Hilaire Belloc wrote, "Statistics are the triumph of the quantitative method, and the quantitative method is the victory of sterility and death."
  • This is a personal story of statistics, properly interpreted, as profoundly nurturant and life-giving. It declares holy war on the downgrading of intellect by telling a small story about the utility of dry, academic knowledge about science. Heart and head are focal points of one body, one personality.
  • We still carry the historical baggage of a Platonic heritage that seeks sharp essences and definite boundaries. (Thus we hope to find an unambiguous "beginning of life" or "definition of death," although nature often comes to us as irreducible continua.) This Platonic heritage, with its emphasis in clear distinctions and separated immutable entities, leads us to view statistical measures of central tendency wrongly, indeed opposite to the appropriate interpretation in our actual world of variation, shadings, and continua. In short, we view means and medians as the hard "realities," and the variation that permits their calculation as a set of transient and imperfect measurements of this hidden essence. If the median is the reality and variation around the median just a device for its calculation, the "I will probably be dead in eight months" may pass as a reasonable interpretation.
  • But all evolutionary biologists know that variation itself is nature's only irreducible essence. Variation is the hard reality, not a set of imperfect measures for a central tendency. Means and medians are the abstractions. Therefore, I looked at the mesothelioma statistics quite differently - and not only because I am an optimist who tends to see the doughnut instead of the hole, but primarily because I know that variation itself is the reality. I had to place myself amidst the variation. When I learned about the eight-month median, my first intellectual reaction was: fine, half the people will live longer; now what are my chances of being in that half. I read for a furious and nervous hour and concluded, with relief: damned good. I possessed every one of the characteristics conferring a probability of longer life: I was young; my disease had been recognized in a relatively early stage; I would receive the nation's best medical treatment; I had the world to live for; I knew how to read the data properly and not despair.
  • Another technical point then added even more solace. I immediately recognized that the distribution of variation about the eight-month median would almost surely be what statisticians call "right skewed." (In a symmetrical distribution, the profile of variation to the left of the central tendency is a mirror image of variation to the right. In skewed distributions, variation to one side of the central tendency is more stretched out - left skewed if extended to the left, right skewed if stretched out to the right.) The distribution of variation had to be right skewed, I reasoned. After all, the left of the distribution contains an irrevocable lower boundary of zero (since mesothelioma can only be identified at death or before). Thus, there isn't much room for the distribution's lower (or left) half - it must be scrunched up between zero and eight months. But the upper (or right) half can extend out for years and years, even if nobody ultimately survives. The distribution must be right skewed, and I needed to know how long the extended tail ran - for I had already concluded that my favorable profile made me a good candidate for that part of the curve.
  • The distribution was indeed, strongly right skewed, with a long tail (however small) that extended for several years above the eight month median. I saw no reason why I shouldn't be in that small tail, and I breathed a very long sigh of relief. My technical knowledge had helped. I had read the graph correctly. I had asked the right question and found the answers. I had obtained, in all probability, the most precious of all possible gifts in the circumstances - substantial time.
  • One final point about statistical distributions. They apply only to a prescribed set of circumstances - in this case to survival with mesothelioma under conventional modes of treatment. If circumstances change, the distribution may alter. I was placed on an experimental protocol of treatment and, if fortune holds, will be in the first cohort of a new distribution with high median and a right tail extending to death by natural causes at advanced old age.
  • Weiye Loh on 08 Aug 10
     
    The Median Isn't the Message by Stephen Jay Gould
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