We study the wisdom of the crowd in three sequential decision-making tasks: the Balloon Analogue Risk Task (BART), optimal stopping problems, and bandit problems. We consider a behavior-based approach, using majority decisions to determine crowd behavior and show that this approach performs poorly in the BART and bandit tasks. The key problem is that the crowd becomes progressively more extreme as the decision sequence progresses, because the diversity of opinion that underlies the wisdom of the crowd is lost. We also consider model-based approaches to each task. This involves inferring cognitive models for each individual based on their observed behavior, and using these models to predict what each individual would do in any possible task situation. We show that this approach performs robustly well for all three tasks and has the additional advantage of being able to generalize to new problems for which there are no behavioral data. We discuss potential applications of the model-based approach to real-world sequential decision problems and discuss how our approach contributes to the understanding of collective intelligence.

中文翻译：

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用于顺序决策任务的基于模型的群体智慧

我们在三个连续决策任务中研究人群的智慧：气球模拟风险任务 (BART)、最优停止问题和强盗问题。我们考虑了一种基于行为的方法，使用多数决策来确定人群行为，并表明这种方法在 BART 和老虎机任务中表现不佳。关键问题是，随着决策序列的推进，人群变得越来越极端，因为作为群众智慧基础的意见多样性已经丧失。我们还考虑了每个任务的基于模型的方法。这涉及根据观察到的每个人的行为推断每个人的认知模型，并使用这些模型来预测每个人在任何可能的任务情况下会做什么。我们表明，这种方法对所有三个任务都表现良好，并且具有能够推广到没有行为数据的新问题的额外优势。我们讨论了基于模型的方法在现实世界顺序决策问题上的潜在应用，并讨论了我们的方法如何有助于理解集体智慧。