Dissonance minimization and conversation in social networks

Anufriev, M; Borissov, K; Pakhnin, M

Dissonance minimization and conversation in social networks

Anufriev, M

Borissov, K Pakhnin, M

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Journal of Economic Behavior and Organization, 2023, 215, pp. 167-191
Issue Date:: 2023-11-01

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Field	Value	Language
dc.contributor.author	Anufriev, M https://orcid.org/0000-0002-1842-6814
dc.contributor.author	Borissov, K
dc.contributor.author	Pakhnin, M
dc.date.accessioned	2024-03-03T23:46:53Z
dc.date.available	2024-03-03T23:46:53Z
dc.date.issued	2023-11-01
dc.identifier.citation	Journal of Economic Behavior and Organization, 2023, 215, pp. 167-191
dc.identifier.issn	0167-2681
dc.identifier.issn	1879-1751
dc.identifier.uri	http://hdl.handle.net/10453/176027
dc.description.abstract	We are examining social learning in networks, where agents aim to minimize cognitive dissonance resulting from disagreement by adjusting their statements in conversations to align with those of their associates, rather than truthfully sharing their beliefs. Our analysis investigates the impact of this adjustment, known as audience tuning, on belief revision, limiting beliefs, consensus conditions, and convergence speed. Our findings demonstrate that audience tuning facilitates extensive belief propagation beyond immediate associates, resulting in faster convergence in most of the societies considered. It also leads to a redistribution of influences on long-run beliefs, favoring agents with lower dissonance sensitivity. We also show that endogenous changes in the network, driven by dissonance minimization, can impede society from reaching a consensus.
dc.language	English
dc.publisher	ELSEVIER
dc.relation	http://purl.org/au-research/grants/arc/DP170100429
dc.relation.ispartof	Journal of Economic Behavior and Organization
dc.relation.isbasedon	10.1016/j.jebo.2023.09.013
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1401 Economic Theory, 1402 Applied Economics, 1403 Econometrics
dc.subject.classification	Economics
dc.subject.classification	3502 Banking, finance and investment
dc.subject.classification	3801 Applied economics
dc.title	Dissonance minimization and conversation in social networks
dc.type	Journal Article
utslib.citation.volume	215
utslib.for	1401 Economic Theory
utslib.for	1402 Applied Economics
utslib.for	1403 Econometrics
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Business
pubs.organisational-group	University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-03T23:46:51Z
pubs.publication-status	Published
pubs.volume	215

Abstract:

We are examining social learning in networks, where agents aim to minimize cognitive dissonance resulting from disagreement by adjusting their statements in conversations to align with those of their associates, rather than truthfully sharing their beliefs. Our analysis investigates the impact of this adjustment, known as audience tuning, on belief revision, limiting beliefs, consensus conditions, and convergence speed. Our findings demonstrate that audience tuning facilitates extensive belief propagation beyond immediate associates, resulting in faster convergence in most of the societies considered. It also leads to a redistribution of influences on long-run beliefs, favoring agents with lower dissonance sensitivity. We also show that endogenous changes in the network, driven by dissonance minimization, can impede society from reaching a consensus.

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http://hdl.handle.net/10453/176027