On informational nudging and control of payoff-based learning
On informational nudging and control of payoff-based learning
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基于回报的学习的信息助推和控制
DOI：
10.3182/20130925-2-de-4044.00037
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
D. Work
中科院分区：
文献类型：
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作者：
Robin Guers;Cédric Langbort;D. Work
摘要我们研究了一个模型的信息轻推的上下文中的启发，在交通中的重复游戏。从一个简单的基于收益的学习模型为个人决策者（DM）选择多个选项，我们引入了一个推荐谁提供可能误导的收益信息未选择的选项，以驱动DM的偏好，以达到预期的均衡。推荐者的这种白色谎言可以被看作是塞勒和桑斯坦意义上的信息推动，因此可以说比基于金钱激励的策略在规划方面有一些好处。考虑到我们的简化模型的流体限制，我们表明，推荐可以创建（但不一定是全局稳定）任何结果，他希望使用恒定的谎言策略。我们还确定了一个框架效应，在这个意义上说，谎言的最不利的选择有一个不同的效果相比，谎言的最有利的选择。
Abstract We investigate a model of informational nudging in a context inspired by repeated games in traffic. Starting from a simple payoff–based learning model for an individual decision–maker (DM) choosing among multiple alternatives, we introduce a recommender who provides possibly misleading payoff information for unchosen options, so as to drive the DM's preferences to a desired equilibrium. This kind of white lie on the part of the recommender can be seen as an informational nudge in the sense of Thaler & Sunstein, and may thus arguably present some benefits over monetary incentive–based strategies for the purposes of planning. Considering the fluid limit of our simplified model, we show that the recommender can create (but not necessarily globally stabilize) any outcome he desires using constant lying strategies. We also identify a framing effect, in the sense that lies about the least favorable option has a different effect compared to lies on most favorable option.