Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
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 p...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347154/ https://www.ncbi.nlm.nih.gov/pubmed/32645069 http://dx.doi.org/10.1371/journal.pone.0234875 |
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author | Sarkar, Soumajyoti Shakarian, Paulo Sanchez, Danielle Armenta, Mika Lakkaraju, Kiran |
author_facet | Sarkar, Soumajyoti Shakarian, Paulo Sanchez, Danielle Armenta, Mika Lakkaraju, Kiran |
author_sort | Sarkar, Soumajyoti |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7347154 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-73471542020-07-20 Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making Sarkar, Soumajyoti Shakarian, Paulo Sanchez, Danielle Armenta, Mika Lakkaraju, Kiran PLoS One Research Article 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. Public Library of Science 2020-07-09 /pmc/articles/PMC7347154/ /pubmed/32645069 http://dx.doi.org/10.1371/journal.pone.0234875 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Sarkar, Soumajyoti Shakarian, Paulo Sanchez, Danielle Armenta, Mika Lakkaraju, Kiran Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making |
title | Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making |
title_full | Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making |
title_fullStr | Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making |
title_full_unstemmed | Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making |
title_short | Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making |
title_sort | use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347154/ https://www.ncbi.nlm.nih.gov/pubmed/32645069 http://dx.doi.org/10.1371/journal.pone.0234875 |
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