An information cascade is a circumstance where agents make decisions in a sequential fashion by following other agents. Bikhchandani et al., predict that once a cascade starts it continues, even if it is wrong, until agents receive an external input such as public information. In an information cascade, even if an agent has its own personal choice, it is always overridden by observation of previous agents' actions. This could mean agents end up in a situation where they may act without valuing their own information. As information cascades can have serious social consequences, it is important to have a good understanding of what causes them. We present a detailed Bayesian model of the information gained by agents when observing the choices of other agents and their own private information. Compared to prior work, we remove the high impact of the first observed agent's action by incorporating a prior probability distribution over the information of unobserved agents and investigate an alternative model of choice to that considered in prior work: weighted random choice. Our results show that, in contrast to Bikhchandani's results, cascades will not necessarily occur and adding prior agents' information will delay the effects of cascades.

中文翻译：

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贝叶斯信息级联模型

信息级联是代理商通过跟随其他代理商以顺序方式做出决策的情况。Bikhchandani等人预测，一旦级联开始，即使它是错误的，它也会继续，直到代理收到外部输入（例如公共信息）为止。在信息级联中，即使业务代表有自己的个人选择，也总是会因观察先前的业务代表的行为而被覆盖。这可能意味着代理最终会陷入一种情况，即他们可能会在不评估自己的信息的情况下采取行动。由于信息梯级可能会带来严重的社会后果，因此，重要的是要了解导致这些情况的原因。当观察其他代理商的选择和他们自己的私人信息时，我们提出了代理商获得的信息的详细贝叶斯模型。与以前的工作相比，我们通过将先验概率分布合并到未观察到的代理人的信息中，消除了第一个被观察到的代理人行为的高影响，并研究了先前工作中考虑的替代选择模型：加权随机选择。我们的结果表明，与Bikhchandani的结果相反，不一定会发生级联，并且添加现有代理的信息将延迟级联的影响。