Granular DeGroot dynamics – a model for robust naive learning in social networks

Amir, Gideon, Arieli, Itai, Ashkenazi-Golan, Galit ORCID: 0000-0003-3896-4131 and Peretz, Ron (2024) Granular DeGroot dynamics – a model for robust naive learning in social networks. Journal of Economic Theory. ISSN 0022-0531 (In Press)

Text (status_quo_bias_dynamics (4)) - Accepted Version Pending embargo until 1 January 2100. Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (804kB)

Abstract

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 [17] have shown that under DeGroot [9] 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 m-DeGroot. 1 m-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, 1 m-DeGroot dynamics is highly robust both to the presence of adversarial agents and to certain types of misspecifications.

Item Type:	Article
Additional Information:	© 2024
Divisions:	Mathematics
Subjects:	Q Science > QA Mathematics H Social Sciences > HB Economic Theory
Date Deposited:	10 Dec 2024 16:09
Last Modified:	12 Dec 2024 04:36
URI:	http://eprints.lse.ac.uk/id/eprint/126309

Actions (login required)

View Item