On Common Beliefs

Abstract

This thesis discusses how probabilistic multi-agent common belief can be defined. It also attempts to provide insight into what constitutes belief. Further, it proposes an algorithm to measure the strength of such common belief by redefining common belief as a bayesian network in form of a directed cyclic graph, and discusses common belief in terms of computational complexity and the depth of belief.