CIF: Small: Learning and herding in complex systems

CIF：小型：复杂系统中的学习和放牧

• 批准号: 1018323
• 负责人: Petar Djuric
• 金额: $ 47.02万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018323&HistoricalAwards=false
• 关键词: CIF; Small; Learning; herding; complex; CIF; Small; Learning; herding; complex

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.

复杂的系统通常定义为由互连部分组成的系统，其属性无法从其各个组件的属性中预测。研究者的研究系统由许多不受指定控制剂控制的相互作用剂组成，并且是自组织的。代理人是自主的，只有对其系统的本地观点。 他们具有从收到的信号中学习的能力，并通过与邻居与商定的语言和规则进行交流，与邻居分享他们的知识。 这种系统的典型特征是它们倾向于显示出紧急行为。新兴行为的一个重要实例是放牧的现象。放牧是一个小组中的特工忽略自己关于自然状态的信号的过程，并遵循邻居的行动。这项研究是关于理解复杂系统学习与放牧之间相互作用的方法的发展。这项工作的目的是研究具有分布式信号处理的复杂系统中放牧的出现。放牧的出现以数学精确的方式定义，以便以有意义的方式检测到它。 系统的代理是理性的，即他们采用贝叶斯学习。主要目的是了解多代理系统中放牧的出现，这是由于系统知识通过接收信号的相互作用，邻近代理的感知行动和学习而引起的。研究了各种共享信息的模型，并确定了很容易出现放牧的场景。开发了改进的方法，以有效地扩散知识在多代理系统中，并寻求防止不良放牧的方法。在建议的支持水平上，PI将尽一切尝试满足项目的原始范围和努力水平。