Distributed Decision Making by Categorically-Thinking Agents

This paper considers group decision making by imperfect agents that only know quantized prior probabilities for use in Bayesian likelihood ratio tests. Global decisions are made by information fusion of local decisions, but information sharing among agents before local decision making is forbidden. The quantization scheme of the agents is investigated so as to achieve the minimum mean Bayes risk; optimal quantizers are designed by a novel extension to the Lloyd-Max algorithm. Diversity in the individual agents’ quantizers leads to optimal performance.