Rational basis of social learning process underlying collective intelligence
Rational basis of social learning process underlying collective intelligence
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集体智慧背后的社会学习过程的理性基础
DOI：
10.11225/cs.2022.032
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
亀田 達也
中科院分区：
文献类型：
--
作者：
内藤 碧;亀田 達也
社会学习过程在我们社会中集体智慧的出现中起着关键作用。最近开发的具有认知建模的计算框架使我们能够从数学上跟踪人们如何联合收割机将个人经历和社会信息结合起来。在这篇文章中，我们首先介绍了社会学习过程的几种变化，以及社会学习对每个人都有益的情况。接下来，我们概述了一个博弈论的困境，产生于谁构成一个群体的个人之间的相互依赖性。正如社会困境中的“公地悲剧”一样，理性的自利个体可以通过社会学习利用他人的探索性发现，同时在信息搜索中表现为搭便车。我们回顾了个人群体如何克服这一挑战，实现集体智慧。最后，我们展示了理性个体的大集体如何在互联网上传播不准确的信息，并导致社会的不可预测性，例如虚假信息的扩散或信息级联。我们讨论了两种可能的方法来应对这种无意中的适应不良的问题，在一个大规模的社会：轻推和算法备份。有许多作品揭示了各种推动技术如何减轻人群的疯狂。虽然这些努力肯定是有帮助的，但我们认为，基于对人类决策算法更深入理解的干预可能会为防止虚假信息在我们社会中的传播提供更根本的帮助。
The social learning process plays a key role in the emergence of collective intelligence in our society. The recent development of computational frameworks with cognitive modeling has enabled us to mathematically track how people combine their personal experiences and social information. In this article, we first present several variations of social learning processes and the situations in which social learning can be beneficial for each individual. Next, we outline a game-theoretic dilemma that arises from the interdependence between individuals who constitutes a group. As in the “tragedy of the commons” in social dilemmas, rational self-interested individuals could exploit others’ exploratory findings through social learning while behaving as a free-rider in information search. We review how groups of individuals can overcome this challenge and achieve collective intelligence. Finally, we demonstrate how large collectives of rational individuals may spread inaccurate information on the Internet and cause unpredictability in society, such as the diffusion of false information or information cascade. We discuss two possible ways to counter such unintended maladaptive problems in a large-scale society: nudges and algorithmic backups. There have been many works that shed light on how various nudge techniques can mitigate the madness of crowds. Although these efforts are certainly helpful, we argue that interventions based on deeper understanding about algorithms of human decision making may provide more fundamental aid to prevent the spread of false information in our society.