Modeling Risky Decision Making by Cellular Automata


This paper proposes a new approach to the problem of decision making under risk (and partly under uncertainty) by a simple cellular automaton which can simulates well- known anomalies of risky choice. This field has been studied mainly by psychologists, economists, management scientists, and more recently neuroscientists so far. Various types of anomalous choice patterns which violate the prediction according to the probability theory and the expected utility theory are called paradoxes, or anomalies. Specifically, descriptive theory of risky choice should model the effects of event-splitting (and event-merging) to violate the first-order stochastic dominance, as well as the ambiguity aversion and the common consequence effect.

Back to Table of Contents