首页    期刊浏览 2024年09月01日 星期日
登录注册

文章基本信息

  • 标题:Learning with Communication Barriers Due to Overconfidence. What a "Model-To-Model Analysis" Can Add to the Understanding of a Problem
  • 本地全文:下载
  • 作者:Juliette Rouchier ; Emily Tanimura
  • 期刊名称:Journal of Artificial Societies and Social Simulation
  • 印刷版ISSN:1460-7425
  • 出版年度:2016
  • 卷号:19
  • 期号:2
  • 页码:1-19
  • DOI:10.18564/jasss.3039
  • 出版社:University of Surrey, Department of Sociology
  • 摘要:In this paper, we describe a process of validation for an already published model, which relies on the M2M paradigm of work. The initial model showed that over-confident agents, which refuse to communicate with agents whose beliefs differ, disturb collective learning within a population. We produce an analytical model based on probabilistic analysis, that enables us to explain better the process at stake in our first model, and demonstrates that this process is indeed converging. To make sure that the convergence time is meaningful for our question (not just for an infinite number of agents living for an infinite time), we use the analytical model to produce very simple simulations and assess that the result holds in finite contexts.
  • 关键词:Collective Learning; Agent-Based Simulation; M2M; Influence Model; Analytical Model; Over-Confidence
国家哲学社会科学文献中心版权所有