Bounded rationality and search over small-world models

AbstractThe ideal Bayesian agent reasons from a global probability model. Real agents must use simplified models which they know to be adequate only in restricted circumtances. Very little formal theory has been developed to help fallibly rational agents manage the process of constructing and revising small world models. The goal of this paper is to present a theoretical framework for analyzing model management approaches and to illustrate the approach by analyzing a particular class of model management strategies. For a probability forecasting problem, a search process over small-world models is analyzed as an approximation to a larger-world model which the agent cannot explicitly enumerate or compute. Conditions are given under which the sequence of small-world forecasts converges to those produced by the larger-world model