Distributed Bayesian Decision Making: Early Experiments

Decision-making under uncertainty is a natural part of everyday life of every human being. In societal science, various aspects of decision-making were studied, mostly in the area of psychology. In technical science, the process was formalized using probability theory yielding so called Bayesian theory of decision making (Berger, 1985). However, one of the key assumptions of this theory is that the decision-maker is the only entity that intentionally influences the system. This assumption is certainly violated in more complicated systems, such as human society or distributed control. Recently, a serie of papers attempts to offer an extension of the Bayesian theory for many decision-makers (Kárný and Guy, 2004), i.e. decentralized stochastic control. Since there are no proofs of optimality of the proposed Bayesian distributed decision making in the literature, we study this approach via experimental simulation studies. In this paper we present the first experimental results of the approach.