Social teaching: Being informative vs. being right in sequential decision making
Social teaching: Being informative vs. being right in sequential decision making
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社会教学：在顺序决策中提供信息与正确
DOI：
10.1109/isit.2013.6620697
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Vivek K Goyal
中科院分区：
文献类型：
--
作者：
Joong Bum Rhim;Vivek K Goyal
我们考虑序贯贝叶斯二元假设检验，其中每个个体代理仅通过最小化她自己对贝叶斯风险的感知来做出二元决策。每个代理可用的信息是一个初始信念，一个私人信号，以及所有早期代理的决策;因此，每个代理应该像在社会学习中一样对其信念应用标准的贝叶斯更新。研究了初始信念集对最后一个智能体决策性能的影响。一般情况下，最优初始信念不等于实际先验概率。当私人信号由高斯似然描述时，它们也不是偶然的，而是遵循一种系统模式：当真实的先验概率很小时，较早行动的代理应该表现得好像先验概率大于现实中的概率，反之亦然。我们将此解释为对不太可能的假设持开放态度。这种开放的思想增加了真实假设和决策之间的互信息，但并没有最大化。
We consider sequential Bayesian binary hypothesis testing where each individual agent makes a binary decision motivated only by minimization of her own perception of the Bayes risk. The information available to each agent is an initial belief, a private signal, and decisions of all earlier-acting agents; it is follows that each agent should apply a standard Bayesian update of her belief as in social learning. The effect of the set of initial beliefs on the decision-making performance of the last agent is studied. In general, the optimal initial beliefs are not equal to the actual prior probability. When the private signals are described by Gaussian likelihoods, they also are not haphazard, but rather follow a systematic pattern: The earlier-acting agents should act as if the prior probability is larger than it is in reality when the true prior probability is small, and vice versa. We interpret this as being open-minded toward the unlikely hypothesis. Such open-mindedness increases but does not maximize the mutual information between the true hypothesis and a decision.