第188讲

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Cognitive Uncertainty, GPT, and Contribution in Public Goods Game

一、讲座题目：Cognitive Uncertainty, GPT, and Contribution in Public Goods Game

二、讲座时间：2024年1月9日（星期二）14:00-15:30

三、讲座地点：best365网页版登录大学城校区教学楼B210室

四、主讲人：包  特 新加坡南洋理工大学经济系长聘副教授         

   主持人：宋 晖 best365官网登录入口经济系系主任，讲师

五、主讲人简介：

包特，新加坡南洋理工大学经济系长聘副教授、博士项目负责人、南洋理工大学金融计算科技研究中心兼职研究员。主要研究领域为行为金融，计算经济学和金融科技。其研究成果发表于Management Science, Economic Journal, European Economic Review, Experimental Economics, Journal of Economic Behavior and Organization, Journal of Economic Dynamics and Control等国际期刊和《管理世界》《管理科学学报》《经济研究》《世界经济》等国内期刊。担任Singapore Economic Review副主编和Electronic Markets金融科技主题特刊编辑。曾任2023 Asian Meeting of Econometric Society本地组委会联合主席，2023国际计算经济学会年会（Society for Computational Economics）和IEEE ICDCS FiDeFix Workshop学术委员会委员。2018—2021年担任国际计算经济学会（Society for Computational Economics）顾问委员会委员，2023年起任中国计算机学会计算经济学小组执行委员。

六、主讲内容简介：

This paper establishes a connection between cognitive noise (Enke and Graeber, 2023) and the level of contribution in the public goods game. Our experimental results demonstrate that a cooperative advice can assist individual in either gaining a better understanding of their true social preference, or translating their true social preferences into contribution actions that maximize their utility as the game repeats. Further, we argue that cognitive noise complements, rather than replaces, taste-based social preference to explain the contribution decision. Our correlational data supports the notion that cognitive uncertainty is positively correlated with contribution in the public goods game at the aggregate level, or cognitive uncertainty lead people to behave as if they are more cooperative. However, there is heterogeneity, where cognitive noise is negatively correlated with the contribution level of some participants at an economically significant extent. These findings suggest the significance of only considering contribution decisions that exceed a certain cognitive certainty threshold in a public goods game if they are to be taken at face value. We also find that advice from the Generative Pre-trained Transformer (hereafter referred to as "GPT"） reduces cognitive uncertainty for all participants, though the impact of the advice does not seem to depend on whether or not the participants are informed the advice was made by GPT.

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2024年1月4日

初审|常诚

复审|宋晖

终审|张志明