CIF: Small: Learning and herding in complex systems

复杂系统通常被定义为由相互连接的部分组成的系统，其属性不能从其单个组件的属性来预测。研究者研究由许多相互作用的agent组成的系统，这些agent不受指定的控制agent的控制，并且是自组织的。代理是自治的，并且只有其系统的局部视图。它们被赋予了从接收到的信号中学习的能力，它们通过用商定的语言和规则与邻居交流来分享它们的知识。这种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现象。羊群是一个过程，在这个过程中，一个群体中的个体忽略了他们自己关于自然状态的信号，而跟随他们邻居的行动。这项研究是关于发展一种方法来理解复杂系统中学习和羊群之间的相互作用。本研究的目的是研究具有分布式信号处理的复杂系统中羊群现象的出现。羊群的出现是用精确的数学方法来定义的，这样就可以用一种有意义的方式来检测。系统的代理是理性的，也就是说，他们使用贝叶斯学习。主要目标是理解多智能体系统中羊群现象的出现，这是由于系统知识通过接收信号、感知邻近智能体的行为和学习的相互作用而扩散。研究了各种共享信息的模型，并确定了容易出现羊群现象的场景。研究了多智能体系统中知识有效扩散的改进方法，并寻求了防止逆向羊群的方法。在推荐的支持水平下，项目负责人将尽一切努力满足项目最初的范围和工作水平。

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.