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Cited by 603Opinion Dynamics and Social InfluenceComplex Network Analysis TechniquesMulti-Agent Systems and Negotiation

Models of Social Influence: Towards the Next Frontiers

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Flache, Andreas, Mäs, Michael, Feliciani, Thomas, Chattoe-Brown, Edmund, Deffuant, Guillaume, Huet, Sylvie, Lorenz, Jan·2017·Journal of Artificial Societies and Social Simulation
DOI 10.18564/jasss.3521
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Abstract

In, Robert Axelrod wondered in a highly influential paper "If people tend to become more alike in their beliefs, attitudes, and behavior when they interact, why do not all such differences eventually disappear?"Axelrod's question highlighted an ongoing quest for formal theoretical answers joined by researchers from a wide range of disciplines.Numerous models have been developed to understand why and under what conditions diversity in beliefs, attitudes and behavior can co-exist with the fact that very o en in interactions, social influence reduces differences between people.Reviewing three prominent approaches, we discuss the theoretical ingredients that researchers added to classic models of social influence as well as their implications.Then, we propose two main frontiers for future research.First, there is urgent need for more theoretical work comparing, relating and integrating alternative models.Second, the field suffers from a strong imbalance between a proliferation of theoretical studies and a dearth of empirical work.More empirical work is needed testing and underpinning micro-level assumptions about social influence as well as macro-level predictions.In conclusion, we discuss major roadblocks that need to be overcome to achieve progress on each frontier.We also propose that a new generation of empirically-based computational social influence models can make unique contributions for understanding key societal challenges, like the possible effects of social media on societal polarization.

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  • This all-to-all interaction is not close to social interactions in the real world.

    “While in line with classic setup used in ABM simulation (Flache et al., 2017; Lorenz et al., 2021), this is not close to social interactions in the real world.”

    From Simulating Opinion Dynamics with Networks of LLM-based Agents (2024) · cites this paper as Flache et al., 2017

  • One key advantage of using ABMs is that they allow incorporating explicit assumptions about cognitive process in opinion updating.

    “One key advantage of using ABMs is that they allow incorporating explicit assumptions about cognitive process in opinion updating (Flache et al., 2017; Lorenz et al., 2021; Chuang and Rogers, 2023).”

    From Simulating Opinion Dynamics with Networks of LLM-based Agents (2024) · cites this paper as Flache et al., 2017

  • Repulsive interactions combined with homophily lead to a decrease of polarization.

    “Another class of models describe polarization by introducing repulsive interactions, in which users reject opinions that differ from their own [24-26], but this mechanism combined with homophily leads to a decrease of polarization [27, 28].”

    From Modeling echo chambers and polarization dynamics in social networks (2020) · cites this paper as [26]

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Authors
Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., & Lorenz, J.
Affiliations
  • University of Groningen (NL)
  • Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
  • Constructor University (DE)
ORCID
Venue
Journal of Artificial Societies and Social Simulation
Publisher
Journal of Artificial Societies and Social Simulation
Published
2017
Volume
20
Issue
4
Pages
31
ISSN
1460-7425 (ISSN-L)
DOI
10.18564/jasss.3521
OpenAlex
W2765195368
Cited by
601
References
69
Citations by year
2026: 57 · 2025: 84 · 2024: 77 · 2023: 81 · 2022: 59 · 2021: 112 · 2020: 53 · 2019: 47 · 2018: 25 · 2017: 1 · 2015: 1 · 2014: 1 · 2013: 1
FWCI
17.83
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Artificial Societies and Social Simulation (publishedVersion, cc-by)
  • HAL (submittedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Figshare (submittedVersion)
  • Cemadoc (Cemagref / IRSTEA) (submittedVersion)
  • Leicester Research Archive (University of Leicester) (submittedVersion, other-oa)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Funders
  • Deutsche Forschungsgemeinschaft
  • Rijksuniversiteit Groningen
Topics
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
Keywords
social influence, Axelrod model, model integration, social media, social polarization, agent-based modeling

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