The research addresses social networks of agents, where the agents learn about the state of nature not only from private signals (i.e., signals only available to the agents receiving them), but from neighboring agents too. The agents are rational and cooperative, and in forming their beliefs about the state of nature, they process all the information that is available to them. The research aims at finding how information and misinformation can diffuse over networks of agents. The objectives are to use the new theory to design better engineering systems and to influence biological systems in ways so that beneficial outcomes are attained.
The goals of the research are to understand the processes of belief evolution about the state of nature in time and/or space in networks of agents and in a wide variety of settings. The knowledge of the agents is expressed by their beliefs about the state of nature and is quantified by posterior probability distributions. Unlike in the majority of known studies where the agents want to get point estimates about the unknown state of nature, the agents in the addressed problems strive for obtaining complete beliefs about the unknown states as measured by posterior distributions. The state of nature can be static or dynamic and the information acquired from neighbors about it can be of continuous or discrete nature. For information processing, the agents use the Bayes' rule. Endowed with Bayesian reasoning, the agents carry out optimal information processing, and thereby it is expected that they beat the performance of agents that use competing methodologies.
