CIF: Small: Learning and herding in complex systems

复杂系统通常被定义为由相互关联的部分组成的系统，其属性无法从其单个组件的属性中预测。研究者研究由许多相互作用的代理人组成的系统，这些代理人不受指定的控制代理人的控制，并且是自组织的。代理是自治的，并且只有其系统的本地视图。 它们被赋予了从接收到的信号中学习的能力，并通过用商定的语言和规则与邻居交流来分享知识。 这类系统的一个典型特征是它们倾向于显示紧急行为。涌现行为的一个重要例子是羊群效应。羊群行为是一个过程，在这个过程中，一个群体中的代理人忽略了他们自己关于自然状态的信号，并跟随他们邻居的行动。本研究旨在发展一种理解复杂系统中学习和羊群效应之间相互作用的方法，目的是研究具有分布式信号处理的复杂系统中羊群效应的出现。羊群行为的出现是以一种数学上精确的方式定义的，这样就可以以一种有意义的方式来检测。 系统的代理人是理性的，即，他们采用贝叶斯学习。主要目的是了解羊群效应的出现，这是由于系统知识的扩散，通过相互作用的接收信号，感知相邻代理的行动，和学习的多智能体系统。各种模式的共享信息进行了研究，并确定了容易出现羊群效应的情况。改进的方法，有效地传播知识的多智能体系统的开发和防止不利的羊群行为的方法。在建议的支持水平下，PI将尽一切努力满足项目的原始范围和工作水平。

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.