社会网络中信念学习的外生控制

Usha Sridhar, Sridhar Mandyam
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引用次数: 0

摘要

社交媒体的新兴增长和移动计算技术的融合为在社交网络中进行个性化、有目的的通信提供了前所未有的机会。利用这种网络的影响范围和影响力,需要更深入地了解说服偏见是如何通过网络中信念学习过程的外生控制在个人和群体层面注入的。嵌入线性控制是一种将说服偏见与沟通暴露联系起来的直接方式。我们在论文中表明,通过对DeGroot信念更新周期应用线性外生控制,可以说服代理以期望的方式改变他们的信念。我们提出了这种控制的理论框架,并举例说明了这一理论,以表明共识是如何被放大甚至破坏的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exogenous control of belief learning in social networks
Emerging growth of social media and convergence of mobile computing technologies offer unprecedented opportunities for targeting personalized purposeful communications into social networks. Leveraging reach and influence of such networks requires deeper understanding of how persuasive bias can be infused at individual and group level through exogenous control of the belief learning process in a network. Embedding linear control lends itself to a direct way to relate persuasive bias to the notion of communication exposure. We show in this paper that by applying linear exogenous control over DeGroot update cycles of beliefs, the agents can be persuaded to shift their beliefs in desired ways. We present the theoretical framework for such control and illustrate the theory with examples to show how consensus can be amplified or even destroyed.
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