Bayesian Learning Without Recall
Bayesian Learning Without Recall
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无回忆的贝叶斯学习
DOI：
10.1109/tsipn.2016.2631943
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发表时间：
2016
影响因子：
3.2
通讯作者：
A. Jadbabaie
中科院分区：
文献类型：
--
作者：
M. Amin Rahimian;A. Jadbabaie
我们分析了网络中的学习和信念形成模型，其中代理遵循贝叶斯规则，但他们不记得过去观察的历史，也无法推理其他代理的信念是如何形成的。他们通过对他们的观察进行理性推理来做到这一点，这些观察包括一系列独立和相同分布的私人信号以及他们相邻代理人在每个时间的行为。连续应用贝叶斯规则到过去观测的整个历史中会导致令人不安的复杂推断：由于缺乏对全球网络结构的了解，私人观测的不可用，以及每个决策之前的第三方交互。这样的困难使得贝叶斯信念更新成为社会学习的一种难以置信的机制。为了解决这些复杂性，我们考虑贝叶斯无召回模型的推理。一方面，该模型为分析社交网络中理性主体的行为提供了一个易于处理的框架。另一方面，该模型也为文献中的各种非贝叶斯更新规则提供了行为基础。我们提出了各种选择的行动空间和效用函数的结构，这样的代理商的影响，并调查在特殊情况下的学习，收敛和共识的属性。
We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents’ beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independent and identically distributed private signals as well as the actions of their neighboring agents at each time. Successive applications of Bayes rule to the entire history of past observations lead to forebodingly complex inferences: due to lack of knowledge about the global network structure, and unavailability of private observations, as well as third party interactions preceding every decision. Such difficulties make Bayesian updating of beliefs an implausible mechanism for social learning. To address these complexities, we consider a Bayesian without Recall model of inference. On the one hand, this model provides a tractable framework for analyzing the behavior of rational agents in social networks. On the other hand, this model also provides a behavioral foundation for the variety of non-Bayesian update rules in the literature. We present the implications of various choices for the structure of the action space and utility functions for such agents and investigate the properties of learning, convergence, and consensus in special cases.
快与慢的思考：跨时间尺度的优化分解
DOI：
10.1109/cdc.2017.8263834
发表时间：
2017
期刊：
56th IEEE Conference on Decision and Control
影响因子：
--
作者：
Goel, Gautam;Chen, Niangjun;Wierman, Adam
通讯作者：
Wierman, Adam