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Strategic Information Disclosure to People with Multiple Alternatives

机译：向具有多种选择的人的战略信息披露

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In this article, we study automated agents that are designed to encourage humans to take some actions over others by strategically disclosing key pieces of information. To this end, we utilize the framework of persuasion games-a branch of game theory that deals with asymmetric interactions where one player (Sender) possesses more information about the world, but it is only the other player (Receiver) who can take an action. In particular, we use an extended persuasion model, where the Sender's information is imperfect and the Receiver has more than two alternative actions available. We design a computational algorithm that, from the Sender's standpoint, calculates the optimal information disclosure rule. The algorithm is parameterized by the Receiver's decision model (i.e., what choice he will make based on the information disclosed by the Sender) and can be retuned accordingly.We then provide an extensive experimental study of the algorithm's performance in interactions with human Receivers. First, we consider a fully rational (in the Bayesian sense) Receiver decision model and experimentally show the efficacy of the resulting Sender's solution in a routing domain. Despite the discrepancy in the Sender's and the Receiver's utilities from each of the Receiver's choices, our Sender agent successfully persuaded human Receivers to select an option more beneficial for the agent. Dropping the Receiver's rationality assumption, we introduce a machine learning procedure that generates a more realistic human Receiver model. We then show its significant benefit to the Sender solution by repeating our routing experiment. To complete our study, we introduce a second (supply-demand) experimental domain and, by contrasting it with the routing domain, obtain general guidelines for a Sender on how to construct a Receiver model.
机译：在本文中，我们研究了自动代理，这些代理旨在通过策略性地公开关键信息来鼓励人们对其他人采取某些行动。为此，我们利用说服游戏的框架-游戏理论的一个分支，处理不对称的互动，其中一个玩家（发送者）拥有关于世界的更多信息，但是只有另一个玩家（接收者）可以采取行动。特别是，我们使用扩展的说服模型，在该模型中，发件人的信息不完善，并且接收方有两个以上的可用替代操作。我们设计了一种计算算法，从发件人的角度出发，可以计算出最佳的信息披露规则。该算法通过接收方的决策模型进行参数化（即他将根据发件人公开的信息做出选择）并可以相应地进行调整，然后我们对该算法在与人类接收方的交互中的性能进行了广泛的实验研究。首先，我们考虑一个完全合理的（在贝叶斯意义上的）接收者决策模型，并通过实验证明所产生的发送者解决方案在路由域中的功效。尽管每个接收方的选择在发送方和接收方的实用程序上存在差异，但我们的发送方代理成功说服了人类接收方选择了对代理更有利的选项。删除接收者的合理性假设后，我们介绍了一种机器学习过程，该过程生成了更现实的人类接收者模型。然后，我们通过重复路由实验来证明它对发件人解决方案的显着好处。为了完成我们的研究，我们引入了第二个（供需）实验域，并通过与路由域进行对比，获得了有关发送者如何构建接收器模型的一般指导。

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