the psychology of design as explanation

Since I posted the link to his blog, Baron just wrote about Cardinal Schönborn’s anti-evolution Op-Ed piece. I agree absolutely that people should learn about the psychology of judgment and probability for these sorts of questions, where it’s really hard to understand that random processes can generate things that seem not so random.

I’m still thinking about how the psychology of judgment plays in to the analysis below. I have a feeling that people’s intuitions are usually too hospitable for explanations based on intention. E.g.: People are poor, therefore someone is trying to make them poor. Organizations (corportations, governments) do things, therefore someone (say, at the top) ordered them to do these things. Natural disasters happen, therefore someone is wishing them upon us. Etc., etc. I’m still not sure how a bayesian dissection of whether “looks intentful” implies “is intentful” shows us whether such an “intent-seeking” bias (hey, I have to call it something) is correct or erroneous. Hopefully more to come.

Also: there was an older posting by Tabarrok on MR arguing that theism makes ID quite reasonable, and atheism makes evolution quite reasonable. This would be the effect of the dominance of the P(H) prior, I believe.

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another blog: cog psych and political/social stuff

By cognitive psychologist Jon Baron

When is it time to stop accruing links to yet more blogs? Blogging makes no sense whatsoever.

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a bayesian analysis of intelligent design

UPDATE: just wrote a revision of this.


Pick an organism. Two propositions, H and E, each may be either true or false about it.

H: the organism was designed by an intelligent creator.
E: the organism looks like it was designed by an intelligent creator.

Most of what I know about ID is from seeing a talk by Michael Behe (may 2005). He had to major lines of argument: (1) it is implausible that an evolutionary process could produce life that looks as if it was intelligently designed. (2) Since it looks like it was intelligently designed, it was. He really emphasized the E component of the argument.

Justifications for E: Lots of organisms look like they were intelligently designed. They have complex and intricate mechanisms involving coordination among many components. Sometimes they look like things humans would design: for example, bacteria locomotion devices sometimes bear uncanny resemblance to human-designed motors or propellers.

Behe was really into showing all these quotes from pro-evolution authors like Dawkins who note this fact: many forms of life appear to us as if they were designed. Consider one of those organisms where E is true. This organism looks as if it was designed.

However, does that mean it actually was designed? That’s a different proposition, the difference between H and E. Since I distrust human intuition on matters of intention ascription (we do it too often), I’d rather look towards a rational framework.

What is the plausibility that this ID-looking orgnanism actually was designed? That’s asking to evaluate P(H|E). Bayes rule tells us how to find P(H|E): the plausibility of a hypothesis H, given the truth of a proposition E (evidence).

Bayes rule derived:

P(H|E) P(E)  =  P(E|H) P(H)

P(H|E)  =  P(E|H) P(H)  =  likelihood * prior
           -----------     --------------------
              P(E)         marginal likelihood

P(H|E): if the organism looks like it was ID’d, the plausibility it actually was. (the core ID argument)
P(E|H): if the organism was ID’d, the plausibility it looks ID’d.

P(E|H) at first seems odd: certainly, if a creator intelligently designed an organism, doesn’t that mean we’d be able to tell? Well, not necessarily: what if a designer makes decisions we cannot understand, or we can’t divine the intelligence in the design of an organism? If that is likely to be the case, then P(E|H) decreases, and H|E becomes less likely.

P(H) is a pretty nasty prior: forgetting the evidence of whether it looks designed, what’s the chance an organism was intelligently designed? That question seems to hinge on prior beliefs in the existence and activity of a creator. It’s not up to debate. If you are already certain God exists, it may be reasonable to entertain the notion that organisms were intelligently designed. If you are less certain God exists, you may believe P(H) to be lower.

P(E) denotes the likelihood to find an organism that looks like it was intelligently designed. Though P(H|E) denotes the plausibility H is true given E is true, to evaluate it we have to look at the probability E could be true independently. The standard way to do this is to expand P(E).

P(H|E)  =  P(E|H) P(H)
           ---------------------
           P(E|H) P(H)  +  P(E|~H) P(~H)

E|~H: if the organism was not ID’d (e.g. it evolved), the plausibility it looks ID’d.

Some evolutionary theorists argue P(E|~H) can be quite high. e.g. Dawkins’ “Blind Watchmaker”: Nature can create impressively complex and purposeful looking life through random chance and natural selection. Behe’s presentation seemed to unfairly argue down E|~H by only considering gradualist Darwinist explanations of evolution. It seems implausible that one-at-a-time tiny mutations could produce big complex systems like the eye or the immune system. That is, it’s too hard to get out of local minima. However, to examine E|~H you need to look at all alternatives to ID. Complexity theory explanations might note that great complexity and order can emerge out of randomness; thus, the formation of complex systems through evolution is more plausible than our intuitions might tell us. Or exaptation: old adaptations might be put to new uses.

And of course, there’s the hard-to-debate prior P(~H) again.

For reference, here are all the propositions again:
H: the organism was designed by an intelligent creator
E: the organism looks like it was designed by an intelligent creator
E|H: if the organism was ID’d, the plausibility it looks ID’d.
E|~H: if the organism was not ID’d (e.g. it evolved), the plausibility it looks ID’d.

So, here’s how things line up for and against ID:

belief pro-ID belief reasons anti-ID belief reasons

E|H high ID’d organisms will look ID’d to us low we may not understand a designer’s designs; they may not look familiar or intelligent to us

E|~H low gradualist adaptationism is unlikely to explain complex systems high blind watchmaker, complexity theory, exaptation… an evolutionary process could lead to outcomes that look as if they were designed.

H high prior belief in a creator and that creator’s likelihood to design life low prior disbelief in a creator and that creator’s likelihood to design life

Caveat: I’m confused how to analyze a given organism versus picking one at random. Does that make a difference?

Also, I’m wondering how to determine how much priors matter. When should argumentation over evidence for evolution force you to revise your beliefs about God? Is there a rational way to do this belief revision? If there isn’t, are we all condemned to stick to our prior beliefs?

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Statistical inference and social science

amazing: a blog on statistical inference for social science. Can’t get more hardcore than that. Well, a formal modelling (e.g. mathematical game theory) blog would be quite something too.

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finding some decision science blogs

Decision Science News looks active & useful. The cognitive neuroscience of decision making is such a great topic — I mean, there are studies of the neurobiology of sarcasm!

There are some terrific older posts on the naturally named “Neuroeconomics”. Steve Saletti also has there a post on Bernheim & Rangel’s cue-triggered addiction paper, which I wrote about earlier (while taking taking a neuroeconomics course co-taught by Rangel, so I guess I’m biased!)

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Social economics and rationality

Here’s a fantastic discussion by Alex Tabarrok and Bryan Caplan on social economics research and rationality — and full of great links to current reviews & research.

NB: just realized Tabarrok is one of the authors of Marginal Revolution (already listed on the Links list here), and Bryan Caplan is at the same place, GMU… maybe the world of creative social economists isn’t all that big after all…

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City crisis simulation (e.g. terrorist attack)

WP: Computers simulate terrorist extremes

Los Alamos scientists are running terrorist attack/response simulations. Well, the article title is misleading, they’re not simulating terrorists (which would pose a whole set of interesting questions about scientific knowledge, social construction and security), but rather, the impact on telecomm, health, and infrastructure systems. They’re using the standard justifications for systems simulation: these are big, highly complex, highly interdependent systems that are ill-understood and have had drastic domino-effect collapses before (like the northeast power blackout).

The article also talks about epidemiology simulations (smallpox in this case, following the terrorist scenario again) that take into account the interactions of individual people with each other — very much along the lines of agent-based simulations, and satisfying complexity theory’s arguments about tipping points and emergent effects. [The article doesn't seem to say whether they actually computed at the level of individual agents, or used statistical aggregate approximations.]

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freakonomics blog

Here it is! Still need to read the book. I’m a little bothered by people proclaiming it to be the first application of economic principles to social questions — hasn’t social economics been around for decades? — but the spirit and approach is right.

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Supreme Court justices’ agreement levels

Cool visualization of agreement levels among Supreme Court justices. I like how they’re ordered so that the smallest amount of agreement ends up in the lower-left. Hopefully it’s not deceptive for certain cases: I imagine that summarizing their tendencies to vote certain ways into a one dimensional spectrum would lose important information of other dimensions of agreement or coalitions.

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$ echo {political,social,economic}{cognition,behavior,systems}

The current subtitle is “where {political, social, economic} crosses {cognition, behavior, systems}”. Amusingly enough, this syntax on a unix shell actually gets you the 9 combinations:

~% echo {political,social,economic}{cognition,behavior,systems}
politicalcognition politicalbehavior politicalsystems
socialcognition socialbehavior socialsystems economiccognition
economicbehavior economicsystems

Tossing together groups of words is a good thing, since unexpected phrases suggest unexpected meanings. For example: Scott McCloud’s story machine, where the point is to force yourself to see randomly generated new ideas.

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