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

Gideon Amir, Itai Arieli, Galit Ashkenazi-Golan and Ron Peretz

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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 (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 as its denominator and like the DeGroot dynamics it is Markovian and stationary. We show that in contrast to standard DeGroot dynamics, 1/ -DeGroot dynamics is highly robust both to the presence of adversarial agents and to certain types of misspecifications.

JEL-codes: C63 D83 D85 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2025-01-31
References: View complete reference list from CitEc
Citations:

Published in Journal of Economic Theory, 31, January, 2025, 223. ISSN: 0022-0531

Downloads: (external link)
https://researchonline.lse.ac.uk/id/eprint/126309/ Open access version. (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:126309

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().