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Scientists these days tend to keep up a polite fiction that all science is equal. Except for the work of the misguided opponent whose arguments we happen to be refuting at the time, we speak as though every scientist's field and methods of study are as good as every other scientist's and perhaps a little better. This keeps us all cordial when it comes to recommending each other for government grants.

Why should there be such rapid advances in some fields and not in others? I think the usual explanations that we tend to think of - such as the tractability of the subject, or the quality or education of the men drawn into it, or the size of research contracts - are important but inadequate. I have begun to believe that the primary factor in scientific advance is an intellectual one. These rapidly moving fields are fields where a particular method of doing scientific research is systematically used and taught, an accumulative method of inductive inference that is so effective that I think it should be given the name of "strong inference." I believe it is important to examine this method, its use and history and rationale, and to see whether other groups and individuals might learn to adopt it profitably in their own scientific and intellectual work. In its separate elements, strong inference is just the simple and old-fashioned method of inductive inference that goes back to Francis Bacon. The steps are familiar to every college student and are practiced, off and on, by every scientist. The difference comes in their systematic application. Strong inference consists of applying the following steps to every problem in science, formally and explicitly and regularly: Devising alternative hypotheses; Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly is possible, exclude one or more of the hypotheses; Carrying out the experiment so as to get a clean result; Recycling the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain, and so on.

On any new problem, of course, inductive inference is not as simple and certain as deduction, because it involves reaching out into the unknown. Steps 1 and 2 require intellectual inventions, which must be cleverly chosen so that hypothesis, experiment, outcome, and exclusion will be related in a rigorous syllogism; and the question of how to generate such inventions is one which has been extensively discussed elsewhere (2, 3). What the formal schema reminds us to do is to try to make these inventions, to take the next step, to proceed to the next fork, without dawdling or getting tied up in irrelevancies.

The authors suggest that there are two publics for science communication, one that is liberal, educated and with a number of resources at its disposals; the other with less predictable and less-formed opinions. The authors explored empirically (via a survey of 108 Colorado citizens) the responses of liberal and educated people to scientific jargon by exposing them to two “treatments”: jargon-laden vs lay terminology news articles. The results found that scientists were considered the most credible sources in the specific area of environmental science (94.3% agreed), followed by activists (61.1%). The least credible were industry representatives, clergy and celebrities. (Remember, this is among liberal educated people.) Interestingly, the use of jargon per se did not increase acceptance of the news source or of the content of the story. So the presence of scientific expertise is important, not so the presence of actual scientific details in the story.

There is no complete account of the scientific method, and again one can choose certain methods rather than others, depending on what one is trying to accomplish (a choice that is itself informed by one’s values). And of course the Duhem-Quine thesis shows that there is no straightforward way to falsify scientific theories (contra Popper). If there were supernatural causes that interact with (or override) the causes being studied by science, but are themselves undiscoverable, this would lead to false conclusions and bad predictions. Which means that the truth is discoverable empirically only if such supernatural causes are not active. Science cannot answer the question of whether such factors are present, which raises the question of whether we ought to proceed as if they were not (i.e., methodological naturalism).

It’s undeniable that people sometimes act in a way that benefits others, but it may seem that they always get something in return — at the very least, the satisfaction of having their desire to help fulfilled.

Contemporary discussions of altruism quickly turn to evolutionary explanations. Reciprocal altruism and kin selection are the two main theories. According to reciprocal altruism, evolution favors organisms that sacrifice their good for others in order to gain a favor in return. Kin selection — the famous “selfish gene” theory popularized by Richard Dawkins — says that an individual who behaves altruistically towards others who share its genes will tend to reproduce those genes. Organisms may be altruistic; genes are selfish. The feeling that loving your children more than yourself is hard-wired lends plausibility to the theory of kin selection.

The defect of reciprocal altruism is clear. If a person acts to benefit another in the expectation that the favor will be returned, the natural response is: “That’s not altruism!”  Pure altruism, we think, requires a person to sacrifice for another without consideration of personal gain. Doing good for another person because something’s in it for the do-er is the very opposite of what we have in mind. Kin selection does better by allowing that organisms may genuinely sacrifice their interests for another, but it fails to explain why they sometimes do so for those with whom they share no genes

The most comprehensive inquiry was the Independent Climate Change Email Review led by Sir Muir Russell, commissioned by UEA to examine the behaviour of the CRU scientists (but not the scientific validity of their work). It published its final report in July 2010

It focused on what the CRU scientists did, not what they said, investigating the evidence for and against each allegation. It interviewed CRU and UEA staff, and took 111 submissions including one from CRU itself. And it also did something the media completely failed to do: it attempted to put the actions of CRU scientists into context.

The Review went back to primary sources to see if CRU really was hiding or falsifying their data. It considered how much CRU’s actions influenced the IPCC’s conclusions about temperatures during the past millennium. It commissioned a paper by Dr Richard Horton, editor of The Lancet, on the context of scientific peer review. And it asked IPCC Review Editors how much influence individuals could wield on writing groups.

Is it legitimate to cite one’s intuitions as evidence in a philosophical argument?

appeals to intuitions are ubiquitous in philosophy. What are intuitions? Well, that’s part of the controversy, but most philosophers view them as intellectual “seemings.” George Bealer, perhaps the most prominent defender of intuitions-as-evidence, writes, “For you to have an intuition that A is just for it to seem to you that A… Of course, this kind of seeming is intellectual, not sensory or introspective (or imaginative).”2 Other philosophers have characterized them as “noninferential belief due neither to perception nor introspection”3 or alternatively as “applications of our ordinary capacities for judgment.”4

Philosophers may not agree on what, exactly, intuition is, but that doesn’t stop them from using it. “Intuitions often play the role that observation does in science – they are data that must be explained, confirmers or the falsifiers of theories,” Brian Talbot says.5 Typically, the way this works is that a philosopher challenges a theory by applying it to a real or hypothetical case and showing that it yields a result which offends his intuitions (and, he presumes, his readers’ as well).

I predict that you will find this review informative. If you do, you will congratulate my foresight. If you don’t, you’ll forget I was wrong.

My playful intro summarizes the main thesis of Gardner’s excellent book, Future Babble: Why Expert Predictions Fail – and Why We Believe Them Anyway.

In Future Babble, the research area explored is the validity of expert predictions, and the primary researcher examined is Philip Tetlock. In the early 1980s, Tetlock set out to better understand the accuracy of predictions made by experts by conducting a methodologically sound large-scale experiment.

The commenter starts with some ad hominems, asserting that my post is biased and emotional. They provide no evidence or argument to support this assertion. And of course they don’t even attempt to counter any of the arguments I laid out. They then follow up with an argument from authority – he can link to a PhD creationist – so there.

The article that the commenter links to is by Henry M. Morris, founder for the Institute for Creation Research (ICR) – a young-earth creationist organization. Morris was (he died in 2006 following a stroke) a PhD – in civil engineering. This point is irrelevant to his actual arguments. I bring it up only to put the commenter’s argument from authority into perspective. No disrespect to engineers – but they are not biologists. They have no expertise relevant to the question of evolution – no more than my MD. So let’s stick to the arguments themselves.

The article by Morris is an overview of so-called Creation Science, of which Morris was a major architect. The arguments he presents are all old creationist canards, long deconstructed by scientists. In fact I address many of them in my original refutation. Creationists generally are not very original – they recycle old arguments endlessly, regardless of how many times they have been destroyed.

So if syphilis causes AIDS, and not HIV, where is the evidence? As microbiologist and epidemiolist Tara Smith points out in her excellent blog, Margulis offers none. Instead, she says to the credulous and uncritical interviewer: The idea that penicillin kills the cause of the disease is nuts. If you treat the painless chancre in the first few days of infection, you may stop the bacterium before the symbiosis develops, but if you really get syphilis, all you can do is live with the spirochete. The spirochete lives permanently as a symbiont in the patient. The infection cannot be killed because it becomes part of the patient’s genome and protein synthesis biochemistry. After syphilis establishes this symbiotic relationship with a person, it becomes dependent on human cells and is undetectable by any testing. Great. Just what we need: an untestable hypothesis promoted by assertion and reputation, not something concrete that scientists could test (although most specialists in microbiology would say the evidence is clear that the HIV retrovirus, and not the spirochaete bacterium Treponema pallidum, is the true cause of AIDS).

Has she never actually LOOKED at the hundreds of peer-reviewed scientific papers documenting the structure of the HIV virus, and the clear documentation of that virus in patients that suffer and die from AIDS? Or the fact that patients treated with anti-retrovirals manage to suppress their AIDS symptoms? Or the disaster in South Africa, when the government became active AIDS deniers, spread misinformation and myths about AIDS, and the infection rate shot up? Not even the hard-core AIDS deniers like Peter Duesberg deny that the HIV virus exists!

she slips outside the realm of science entirely, and becomes a full-fledged AIDS denier. My jaw just dropped when I read the following: There is a vast body of literature on syphilis spanning from the 1500s until after World War II, when the disease was supposedly cured by penicillin. It’s in our paper “Resurgence of the Great Imitator.” Our claim is that there’s no evidence that HIV is an infectious virus, or even an entity at all. There’s no scientific paper that proves that the HIV virus causes AIDS. Kary Mullis said in an interview that he went looking for a reference substantiating that HIV causes AIDS and discovered, “There is no such document.”

It is both misguided and dangerous to push unobserved risks further into the statistical tails of the probability distribution of outcomes and allow these high-impact, low-probability "tail risks" to disappear from policymakers' fields of observation.

Such environments eventually experience massive blowups, catching everyone off-guard and undoing years of stability or, in some cases, ending up far worse than they were in their initial volatile state. Indeed, the longer it takes for the blowup to occur, the worse the resulting harm in both economic and political systems.

Seeking to restrict variability seems to be good policy (who does not prefer stability to chaos?), so it is with very good intentions that policymakers unwittingly increase the risk of major blowups. And it is the same misperception of the properties of natural systems that led to both the economic crisis of 2007-8 and the current turmoil in the Arab world. The policy implications are identical: to make systems robust, all risks must be visible and out in the open -- fluctuat nec mergitur (it fluctuates but does not sink) goes the Latin saying.