Control metaphors in the modelling of economic learning and decision-making behaviour

Abstract

Learning is represented in economic models either as a process of estimating equations which are known to be correct representations of the environment or as a process of sampling from a known probability distribution. It is arguably more natural to represent leaning as the specification of equations or other relationships in conditions where information processing capacity is not sufficiently powerful as to enable agents to identify such global characteristics of the whole information set as the moments of a probability distribution. Techniques drawn from the literature on machine learning and on knowledge-based systems are coming into use as tools for modelling learning in such conditions. In this paper I describe the key differences between the older and newer approaches to the modelling of learning and decision-making in terms of two metaphors: the menu and the agenda. Two agenda-type algorithms—a genetic algorithm and a production system — are applied to a simple economic model to show that they imply quite different descriptions of learning and decision-making. Moreover, the production system finds the optimal behaviour orders of magnitude faster than the genetic algorithm because — it is suggested — it is the better descriptor of actual strategic decision-making behaviour in normally complex economic conditions.