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?
What are the key algorithms for learning optimal behavior of economic agents?
learning;reference request;social psychology;game theory;agent based modeling
Take a look at POMDP's - partially observable markov decision processes.If you have a value function (income) that is known for agents in various states, and you are trying to identify the optimal policy then Bellman's equation which is at the heart of POMDP's will help you identify this policy.These tools are part of a class of re-inforcement learning algorithms (in fact, they are used quite often for robotics). So they map very neatly to the framework you have identified (agents, a reward function, and a state/action space)Another angle of attack would be using genetic algorithms in your optimization procedure. 