Granular DeGroot dynamics - A model for robust naive learning in social networks
成果类型：
Article
署名作者：
Amir, Gideon; Arieli, Itai; Ashkenazi-Golan, Galit; Peretz, Ron
署名单位：
Bar Ilan University; Technion Israel Institute of Technology; University of London; London School Economics & Political Science; Bar Ilan University
刊物名称：
JOURNAL OF ECONOMIC THEORY
ISSN/ISSBN：
0022-0531
DOI：
10.1016/j.jet.2024.105952
发表日期：
2025
关键词：
摘要：
We study a model of opinion exchange in social networks where a state of the world is realized and every agent receives a zero-mean noisy signal of the realized state. Golub and Jackson (2010) have shown that under DeGroot (1974) dynamics agents reach a consensus that is close to the state of the world when the network is large. The DeGroot dynamics, however, is highly non-robust and the presence of a single adversarial agent that does not adhere to the updating rule can sway the public consensus to any other value. We introduce a variant of DeGroot dynamics that we call 1-DeGroot. 1-DeGroot dynamics approximates standard DeGroot dynamics to the nearest rational number with m as its denominator and like the DeGroot dynamics it is Markovian and stationary. We show that in contrast to standard DeGroot dynamics, 1m-DeGroot dynamics is highly robust both to the presence of adversarial agents and to certain types of misspecifications.