Monthly Archives: October 2007

Bay Area Bayesians Unite!

Robin Hanson has his fellow GMU economists to talk to, but I’m not associated with a university and I live way out in the boondocks: the echoing emptiness of, er, Silicon Valley.

Overcoming Bias gets over 2000 visitors per day.  Surely some of you are from the Bay Area.  Would you be interested in a Bay Area meetup of Overcoming Bias readers?

Polls after the jump.
If you’re interested at all, please vote in at least the closest-city poll.
Polls will be processed for a best-compromise value, not a binding modal result.
If I get at least 30 responses, I’ll start looking into meetup locations.

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Dan Kahneman Puzzles With Us

I have never tried very hard, but I am in a way surprised by the ambivalence about it that you encounter in organizations. My sense is that by and large there isn’t a huge wish to improve decision-making – there is a lot of talk about doing so, but it is a topic that is considered dangerous by the people in the organization and by the leadership of the organization. I’ll give you a couple of examples. I taught a seminar to the top executives of a very large corporation that I cannot name and asked them, would you invest one percent of your annual profits into improving your decision-making? They looked at me as if I was crazy; it was too much.

That is from an Edge interview.  We see a related disinterest in better decisions via decision markets.  Hat Tip Hamish Barney.

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Why Are Individual IQ Differences OK?

Idang Alibi of Abuja, Nigeria writes on the James Watson affair:

A few days ago, the Nobel Laureate, Dr. James Watson, made a remark that is now generating worldwide uproar, especially among blacks.  He said what to me looks like a self-evident truth.  He told The Sunday Times of London in an interview that in his humble opinion, black people are less intelligent than the White people…

An intriguing opening.  Is Idang Alibi about to take a position on the real heart of the uproar?

I do not know what constitutes intelligence.  I leave that to our so-called scholars.  But I do know that in terms of organising society for the benefit of the people living in it, we blacks have not shown any intelligence in that direction at all.  I am so ashamed of this and sometimes feel that I ought to have belonged to another race…

Darn, it’s just a lecture on personal and national responsibility.  Of course, for African nationals, taking responsibility for their country’s problems is the most productive attitude regardless.  But it doesn’t engage with the controversies that got Watson fired.

Later in the article came this:

As I write this, I do so with great pains in my heart because I know that God has given intelligence in equal measure to all his children irrespective of the colour of their skin.

This intrigued me for two reasons:  First, I’m always on the lookout for yet another case of theology making a falsifiable experimental prediction.  And second, the prediction follows obviously if God is just, but what does skin colour have to do with it at all?

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If Not Data, What?

My colleague Russ Roberts writes

Over the years, I have become increasingly skeptical of the power of statistical techniques to measure causation in complex systems.  … I happen to believe that concealed handguns do deter crime and allowing concealed handguns is a good thing. And you can claim that the evidence that shows I’m right is "good" statistical analysis. The other side disagrees. They claim it’s "bad" statistical analysis. Who’s right? I have no idea. But what’s clear to me is that my belief in the virtues of allowing concealed hand guns has little to do with the empirical evidence. And I would argue that the opponents are really in the same boat. They just don’t like guns and they’ve dressed up their prejudices in fancy statistical analysis. …

If Russ relies little on data to draw his conclusions, then on what does he rely?  Perhaps he relies on theoretical arguments.  But can’t we say the same thing about theory, that we mainly just search for theory arguments to support preconceived conclusions?  If so, what is left, if we rely on neither data nor theory? 

Try saying this out loud: "Neither the data nor theory I’ve come across much explain why I believe this conclusion, relative to my random whim, inherited personality, and early culture and indoctrination, and I have no good reasons to think these are much correlated with truth."  That does not seem a conclusion worth retaining.  If this is really your situation, you should move to a nearly intermediate position of uncertainty.   Either you should believe that truth-correlated data or theory has substantially influenced your belief, or you should retain only a very weak belief. 

HT to Arnold Kling.

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No One Knows What Science Doesn’t Know

At a family party some years ago, one of my uncles remarked on how little science really knows.  For example, we still have no idea how gravity works – why things fall down.

"Actually, we do know how gravity works," I said.  (My father, a Ph.D. physicist, was also present; but he wasn’t even touching this one.)

"We do?" said my uncle.

"Yes," I said, "Gravity is the curvature of spacetime."  At this point I had still swallowed Feynman’s line about being able to explain physics to one’s grandmother, so I continued:  "You could say that the Earth goes around the Sun in a straight line.  Imagine a graph that shows both space and time, so that a straight line shows steady movement and a curved line shows acceleration.  Then curve the graph paper itself.  When you try to draw a straight line on the curved paper, you’ll get what looks like acceleration -"

"I never heard about anything like that," said my uncle.

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Dennett’s Special Pleading

Daniel Dennett has a review out in Artificial Intelligence of Douglas Hofstadter’s 2007 book I am a Strange Loop, and Marvin Minsky’s 2006 book The Emotion Machine.  Dennett admits that he is biased for these authors, that they mainly rehash their famous books of twenty years ago, and that they fail to meet the standards of all related academic disciplines, and so are rejected by those academics.  But, well, he just likes them anyway: 

I am much too close, personally and intellectually, to Doug Hofstadter and Marvin Minsky to write a proper academic review of these wonderful books … I travel in several quite different academic circles and I find that each gang has its particular way of not taking these thinkers seriously. The neuroscientists deplore the absence of rigorous experiments and the refusal of both Hofstadter and Minsky to canvass the relevant experimental literature thoroughly and explicitly. Where are the data? The philosophers of mind, at the other extreme, find few formal arguments and a frustratingly cavalier refusal by the authors to define their terms at the outset. Where are the proofs?

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Double Illusion of Transparency

Followup to:  Explainers Shoot High, Illusion of Transparency

My first true foray into Bayes For Everyone was writing An Intuitive Explanation of Bayesian Reasoning, still one of my most popular works.  This is the Intuitive Explanation’s origin story.

In December of 2002, I’d been sermonizing in a habitual IRC channels about what seemed to me like a very straightforward idea:  How words, like all other useful forms of thought, are secretly a disguised form of Bayesian inference.  I thought I was explaining clearly, and yet there was one fellow, it seemed, who didn’t get it.  This worried me, because this was someone who’d been very enthusiastic about my Bayesian sermons up to that point.  He’d gone around telling people that Bayes was "the secret of the universe", a phrase I’d been known to use.

So I went into a private IRC conversation to clear up the sticking point.

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Why I’m Betting on the Red Sox

One of the most pervasive beliefs among sports fans is a belief in "streaks".  I cannot tell you the number of times I have heard sports commentators this week tell us that the Rockies have won 21 of their last 22 games.  And this alone is the reason that I’m betting against the Rockies.

The "hot hand bias" was first documented in a fascinating paper by Tom Gilovich, Robert Vallone and Amos Tversky.  That original paper (available here) is a wonderful read, showing that the widespread belief among basketball fans of a strong "hot hand" is simply false.  That is, today’s streak doesn’t predict tomorrow’s behavior.  I love teaching this paper to my MBA students, simply because they don’t believe it.  The hot hand fallacy is a wake-up to how pervasive bias can be.  A nice example of how sports can yield very convincing teaching metaphors.

A subsequent literature has developed showing that many (most?) of the sports statistics that ESPN loves to share with us, are simply useless as inputs for forecasting the future.  It seems that our brains are a bit too willing to try to find order, even in a world where chaos reigns.  This leads me to believe that most baseball fans are a bit too optimistic that the Rockies’ streak will persist.

Some will protest that subsequent research has found evidence of streakiness in specific sports.  I agree.  But this is beside the point: it is essentially an observation about sports.  What is more relevant here (and no-one has convincingly refuted) is that sports fans tend to believe that streakiness is even stronger

Believe it or not, there is now an entire blog devoted to the hot hand and streakiness in sports – read more here.  Or if you are interested in the performance of streaky baseball teams in the post-season, read this analysis at

(OK, there is one more reason I’m betting on the Red Sox: I went to graduate school in Beantown, and learned to love baseball at Fenway.)

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InTrade Nominee Advice

For the top four US presidential candidates from each party, gives these chances of nomination, becoming president, and the implied conditional chance of winning if nominated:


Al Gore’s conditional chances seem way out of line to me.  And Ron Paul’s conditional chances are suspiciously large compared to the other Republicans.

Added: What good are estimates that could be better?  I know of no other source of daily-updated estimates with even a similar accuracy.  And here anyone can be paid to fix any errors they find.

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Explainers Shoot High. Aim Low!

Followup to:  Illusion of Transparency: Why No One Understands You, Expecting Short Inferential Distances

A few years ago, an eminent scientist once told me how he’d written an explanation of his field aimed at a much lower technical level than usual.  He had thought it would be useful to academics outside the field, or even reporters.  This ended up being one of his most popular papers within his field, cited more often than anything else he’d written.

The lesson was not that his fellow scientists were stupid, but that we tend to enormously underestimate the effort required to properly explain things.

He told me this, because I’d just told him about my experience publishing "An Intuitive Explanation of Bayesian Reasoning".  This is still one of my most popular, most blogged, and most appreciated works today.  I regularly get fan mail from formerly confused undergraduates taking statistics classes, and journalists, and professors from outside fields.  In short, I successfully hit the audience the eminent scientist had thought he was aiming for.

I’d thought I was aiming for elementary school.

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