北京大学助理教授梅文俊博士应邀来我院做学术报告

发布者:深度信息融合研究院发布时间:2021-06-26浏览次数:10

报告主题:Rethinking the Micro-Foundation of Opinion Dynamics: Rich Consequences of an Inconspicuous Change

报 告 人:  梅文俊博士

报告人单位:  北京大学

时  间: 2021年7月6日(星期二)上午09:00 

地  点:  武钢楼1519会议室

专家简介:

梅文俊博士是瑞士联邦理工学院自动控制实验室博士后研究人员。本科毕业于北京大学理论与应用力学专业,博士毕业于加州大学圣巴巴拉分校机械工程专业。20219月起,将任北京大学力学与工程科学系助理教授。现任Journal of Mathematical Sociology期刊的编委。梅博士的研究主要集中在网络动态系统的建模、分析和控制方面,包括社会和经济网络、网络博弈、群体决策和演化博弈。

内容简介:

          Public opinion formation has long been affected by various harmful phenomena like opinion radicalization, echo chambers, and information manipulation. To identify the main mechanisms underlying complex opinion formation processes, researchers have long been exploring simple mechanistic mathematical models. Despite important progress in recent decades, many widely-studied models do not fully explain some important real-world phenomena including, e.g., opinion formation over ordered discrete options, continuous multimodal public opinion distributions, and radicalization of socially marginalized individuals. In this paper, we propose a parsimonious yet predictive model that sheds new light on these important phenomena. Remarkably, this new model is not constructed by introducing additional assumptions or parameters to previous models, but rather by changing the very core mechanism of opinion evolution. Specifically, we modify the widely adopted weighted-averaging mechanism and, inspired by cognitive dissonance theory, propose a novel micro-foundation for opinion dynamics, namely the weighted-median mechanism. This inconspicuous microscopic change leads to rich macroscopic consequences. The new mechanism broadens the applicability of opinion dynamics models to multiple-choice issues with ordered discrete options, as appearing for example in political elections. As shown by quantitative predictability tests on a large-scale online experimental dataset, predictions of opinion shifts by the median-based mechanisms enjoy significantly lower error rates than predictions by the corresponding averaging-based mechanisms. Moreover, comparative numerical studies indicate that, the weighted-median mechanism, despite its simplicity in form, is consistent with empirical observations in many important sociological phenomena. In addition, theoretical analysis shows that the weighted-median model exhibits rich consensus-disagreement phase transitions depending upon subtle yet robust network structures. All these results establish that the weighted-median mechanism is a viable micro-foundation of opinion dynamics.

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