CIF: Small: Learning and Herding in Complex Systems

A complex system is often defined as a system composed of interconnected parts whose properties cannot be predicted from the properties of its individual components. The investigator studies systems composed of many interacting agents that are not controlled by designated controlling agents and are self-organized. The agents are autonomous and have only local views of their system. They are endowed with the ability to learn from received signals and they share their knowledge with their neighbors by communicating it with agreed languages and rules. A typical feature of such systems is their tendency to display emergent behavior. An important instance of emergent behavior is the phenomenon of herding. Herding is a process where agents in a group ignore their own signals about the state of nature and follow the actions of their neighbors. The research is on the development of a methodology for understanding the interplay between learning and herding in complex systems. The aim of this work is to study the emergence of herding in complex systems with distributed signal processing. Emergence of herding is defined in a mathematically precise way so that it can be detected in a meaningful way. The agents of the system are rational, i.e., they employ Bayesian learning. The main objective is to understand the emergence of herding in multi-agent systems which is due to diffusion of system knowledge through interactions of received signals, perceived actions of neighboring agents, and learning. Various models of sharing information are studied and scenarios where herding readily arises are identified. Improved methods for efficient diffusion of knowledge in multi-agent systems are developed and ways of preventing adverse herding are sought. At the recommended level of support, the PI will make every attempt to meet the original scope and level of effort of the project.