悲观陷阱与算法干预
Pessimism Traps and Algorithmic Interventions
AI总结：
本文通过信息级联模型解释悲观陷阱，提出一种算法干预，可引导群体从错误级联转向正确级联并在补贴停止后维持，并进行了理论与实证研究。
摘要：
在本文中，我们将关于悲观陷阱的哲学文献与信息级联联系起来，后者是一种源自经济学和数学文献的正式模型。悲观陷阱是一种社会模式，在这种模式中，社区中的个体在不确定的情况下，尽管持有个人信念，却开始模仿他人的次优行动。这很好地映射到信息级联的概念上，信息级联涉及一系列智能体在两个备选方案之间做出决策，每个智能体拥有关于较优方案的私有信号以及他人行动的公开历史。经济学文献中的关键结果表明，在许多情境下，信息级联以概率1发生，并且根据信号的强度，群体可能非常容易且迅速地陷入错误的级联。一旦形成，在没有外部扰动的情况下，级联无法被打破——因此，我们推导出一种干预措施，可用于将群体从错误的级联推向正确的级联，并且重要的是，在补贴停止后仍能维持该级联。我们从理论和实证两方面对此进行研究。
英文摘要：
In this paper, we relate the philosophical literature on pessimism traps to information cascades, a formal model derived from the economics and mathematics literature. A pessimism trap is a social pattern in which individuals in a community, in situations of uncertainty, begin to copy the sub-optimal actions of others, despite their individual beliefs. This maps nicely onto the concept of an information cascade, which involves a sequence of agents making a decision between two alternatives, with a private signal of the superior alternative and a public history of others' actions. Key results from the economics literature show that information cascades occur with probability one in many contexts, and depending on the strength of the signal, populations can fall into the incorrect cascade very easily and quickly. Once formed, in the absence of external perturbation, a cascade cannot be broken -- therefore, we derive an intervention that can be used to nudge a population from an incorrect to a correct cascade and, importantly, maintain the cascade once the subsidy is discontinued. We study this both theoretically and empirically.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
