摘要 We study learning in a setting where 智能體s receive

本頁只刊出中文翻譯與中文說明；英文原文請見下方原文連結。

原文連結

• SFI 頁面

論文資訊

• 類型：已發表論文
• 日期：2010

摘要

We study learning in a setting where 智能體s receive independent noisy signals about the true value of a variable and then communicate in a 網絡. They naively update belief's by repeatedly taking weighted averages of neighbors' opinions. We show that all opinions in a large society converge to the truth if and only if the influence of the most influential 智能體 vanishes as the society grows. We also identify obstructions to this, including prominent groups, and provide structural conditions on the 網絡 ensuring efficient learning. Whether 智能體s converge to the truth is unrelated to how quickly consensus is approached

※ 此為已發表論文，全文需透過期刊付費取得