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I'm playing around with social learning of near-optimal behavioral rules on a set of agents. The idea is roughly that given an income process (or technology process, depending on the question) an optimal nonlinear, intertemporal policy rule exists. Assume this rule can be approximated closely by a linear function. Agents would like to learn this policy rule, and a first pass is to have them learn the rule simply by experimentation. "In autarky," i.e. without any information exchange with other agents, an agent would try a rule for some time, use some metric to determine how well it does against other rules he/she has tried, and perhaps reassess, perhaps try an entirely different rule via experimentation. This agent only observes his/her own history.

A second pass is to allow the agent access to all other agents' histories. Presumably this would speed up learning. A third pass might be to put these agents on an information network of some sort.

I've been perusing literature on social learning, but am not entirely sure the frameworks I am looking at are exactly what I want. Many of them appear to be Bayesian learning about a hidden state of nature, for which everyone has a private signal. I'm actively reviewing literature right now, but as I do, does anyone have any thoughts/suggestions?

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You might look into the AI literature. –  Jason B Oct 31 '11 at 20:23
    
Agree on the AI. Bayesian networks and genetic algorithms come into this as well. –  Turukawa Oct 31 '11 at 21:54
    
Any particular references? –  Nate Nov 1 '11 at 3:48
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migrated to cogsci.stackexchange.com by Jason B May 1 '12 at 12:59

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