Computational intelligence determines effective rationality

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Abstract

Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are “bounded rational”. However, it is very difficult to quantify “bounded rationality”, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers’ decision-making procedures. This paper takes a computational point of view to Rubinstein’s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power — we refer to this as the computational intelligence determines effective rationality (CIDER) theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to (computationally) reason about economic systems when the full rationality assumption is relaxed.

Edward P. K. Tsang is a professor in computer science at University of Essex. He is also the deputy director of Centre for Computational Finance and Economic Agents (CCFEA) and a founding member of the Centre for Computational Intelligence at University of Essex.
He has broad interest in business application of artificial intelligence, including, computational finance, computational economics, and constraint satisfaction. Main techniques used included heuristic search, optimization, and evolutionary computation.