Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2020
Historique:
received: 26 02 2020
accepted: 03 06 2020
entrez: 10 7 2020
pubmed: 10 7 2020
medline: 15 9 2020
Statut: epublish

Résumé

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the "pattern" by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game decides among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make a similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.

Identifiants

pubmed: 32645069
doi: 10.1371/journal.pone.0234875
pii: PONE-D-20-05599
pmc: PMC7347154
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0234875

Déclaration de conflit d'intérêts

The authors declare no competing interests. The commercial affiliation to Sandia National Laboratories does not alter our adherence to PLOS ONE policies on sharing data and materials.

Références

Proc Natl Acad Sci U S A. 2011 Nov 29;108(48):19193-8
pubmed: 22084103
PLoS One. 2016 Jan 19;11(1):e0146536
pubmed: 26784448
PLoS One. 2013 Oct 01;8(10):e76027
pubmed: 24098422
Science. 2010 Sep 3;329(5996):1194-7
pubmed: 20813952
PLoS One. 2017 Jan 18;12(1):e0169326
pubmed: 28099478
PLoS One. 2018 Sep 20;13(9):e0203958
pubmed: 30235239
PLoS One. 2016 Mar 04;11(3):e0150989
pubmed: 26943909
Proc Natl Acad Sci U S A. 2011 May 31;108(22):9020-5
pubmed: 21576485
Mem Cognit. 1978 Sep;6(5):554-61
pubmed: 24203389
Science. 2013 Aug 9;341(6146):647-51
pubmed: 23929980
PLoS One. 2015 Oct 14;10(10):e0140556
pubmed: 26465749
Science. 2018 Mar 9;359(6380):1094-1096
pubmed: 29590025
Sci Rep. 2012;2:638
pubmed: 22962633
Science. 2006 Feb 10;311(5762):854-6
pubmed: 16469928
PLoS One. 2013 Jun 19;8(6):e66199
pubmed: 23840422
J Exp Soc Psychol. 1997 Jul;33(4):345-66
pubmed: 9247369
Science. 2012 Jul 20;337(6092):337-41
pubmed: 22722253
Proc Natl Acad Sci U S A. 2016 Jul 5;113(27):7316-22
pubmed: 27382145

Auteurs

Soumajyoti Sarkar (S)

School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States of America.

Paulo Shakarian (P)

School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States of America.

Danielle Sanchez (D)

Computer Science and Applications, Sandia National Laboratories, Albuquerque, NM, United States of America.

Mika Armenta (M)

Computer Science and Applications, Sandia National Laboratories, Albuquerque, NM, United States of America.

Kiran Lakkaraju (K)

Computer Science and Applications, Sandia National Laboratories, Albuquerque, NM, United States of America.

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Classifications MeSH