CIF: Small: Belief Evolutions in Networks of Bayesian Agents

CIF：小：贝叶斯代理网络的信念演变

• 批准号: 1320626
• 负责人: Petar Djuric
• 金额: $ 39.31万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320626&HistoricalAwards=false
• 关键词: CIF; Small; Belief; Evolutions; Networks

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.

该研究涉及代理的社交网络，其中代理不仅从私人信号（即，信号仅对接收它们的代理可用），但是也来自相邻代理。代理人是理性的和合作的，在形成他们对自然状态的信念时，他们处理所有可用的信息。该研究旨在发现信息和错误信息如何在代理网络中传播。目标是使用新的理论来设计更好的工程系统，并影响生物系统的方式，使有益的结果是achieved.The研究的目标是理解的信念进化过程中的自然状态在时间和/或空间的网络代理和各种设置。代理人的知识是由他们对自然状态的信念来表达的，并由后验概率分布来量化。与大多数已知的研究不同，在大多数已知的研究中，代理人希望得到关于未知自然状态的点估计，代理人在所解决的问题中努力获得关于未知状态的完整信念，如后验分布所测量的。自然状态可以是静态的或动态的，从邻居那里获得的关于它的信息可以是连续的或离散的。对于信息处理，代理使用贝叶斯规则。赋予贝叶斯推理，代理进行最佳的信息处理，从而预计，他们击败使用竞争方法的代理的性能。