Characterizing Non-Myopic Information Cascades in Bayesian Learning
Characterizing Non-Myopic Information Cascades in Bayesian Learning
复制标题
贝叶斯学习中非短视信息级联的特征
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Anastasopoulos
中科院分区：
文献类型：
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作者：
Ilai Bistritz;A. Anastasopoulos
我们考虑这样一种环境：许多参与者需要决定是否购买某种产品（或追随一种潮流）。产品有好坏之分，但参与者并不知晓其真实价值。相反，每个参与者都有自己关于产品质量的私人信息。每个参与者都能观察到其他参与者之前的行为，并由此推断产品质量。一个参与者只能购买一次该产品。与现有的信息级联文献不同，在这项研究中，参与者有不止一次的行动机会。在每一轮中，从所有参与者中随机均匀地选取一名参与者，他可以决定购买或不购买。他的效用是总预期贴现奖励，因此短视策略可能不是最佳反应。我们通过一个维度的不动点方程来刻画具有非短视策略的结构化完美贝叶斯均衡（PBE），该方程的维度仅随参与者数量呈多项式增长。基于这一特征，我们研究了信息级联，并表明对于大量参与者而言，信息级联很有可能发生。此外，在级联发生之前，系统中只有一小部分总信息被揭示出来。
We consider an environment where many players need to decide whether to buy a certain product (or adopt a trend) or not. The product is either good or bad, but its true value is not known to the players. Instead, each player has his own private information on the quality of the product. Each player can observe the previous actions of other players and deduce the quality of the product. A player can only buy the product once. In contrast to the existing literature on informational cascades, in this work players get more than one opportunity to act. In each turn, a player is chosen uniformly at random from all players and can decide to buy or not to buy. His utility is the total expected discounted reward, and thus myopic strategies may not be best responses. We provide a characterization of structured perfect Bayesian equilibria (PBE) with non-myopic strategies through a fixed-point equation of dimensionality that grows only polynomially with the number of players. Based on this characterization we study informational cascades and show that they happen with high probability for a large number of players. Furthermore, only a small portion of the total information in the system is revealed before a cascade occurs.