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Models of Social Influence: Towards the Next Frontiers
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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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“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
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“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
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“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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Chai, Y. C., Chee, W. L., Cheong, J. Y., Fong, M. S., & Yap, M. C. (2020)
Agent-based neutral competition in two-community networks
Ishida, K., Oborny, B., & Gastner, M. T. (2021) · Physical review. E · doi:10.1103/physreve.104.024308
Festinger, Schachter, and Back’sSocial Pressures in Informal Groups
Martin, J. L., & Kwon, H. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.011
Smith, S. S., & Sanders, J. M. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.044
Implications of Informant Accuracy Research for Ego Networks
McCarty, C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.013
From Scott L. Feld, “The Focused Organization of Social Ties”
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.026
Mische, A. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.015
From Barry Wellman and Scot Wortley, “Different Strokes from Different Folks”
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.020
Linking Theory and Empirics: A General Framework to Model Opinion Formation ProcessesPreprint
Kozitsin, I. (2021) · Research Square · doi:10.21203/rs.3.rs-1049437/v1
From Ronald S. Burt,Structural Holes
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.028 · Cited by 1
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.059
From Robert Huckfeldt and John Sprague, “Networks in Context”
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.034
From J. Clyde Mitchell, “The Concept and Use of Social Networks”
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.006
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.058
A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teamsPreprint
Pescetelli, N., Reichert, P., & Rutherford, A. (2021) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/67jku
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.002
From Mark S. Granovetter, “The Strength of Weak Ties”
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.018
From Bernice A. Pescosolido, “Beyond Rational Choice”
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.024
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.030
From Elihu Katz and Paul F. Lazarsfeld,Personal Influence
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.004
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.012
From Harrison C. White,Identity and Control
Pescosolido, B. A., & Smith, E. B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.014
Manzo, G. (2022) · Wiley eBooks · doi:10.1002/9781119704492.biblio
Ye, M. (2019) · Springer theses · doi:10.1007/978-3-030-10606-5_1
The Internet: Challenges and Opportunities Faced by Modelers of Social InfluencePreprint
Campoli, B. (2019) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/8fwn7
A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teamsPreprint
Pescetelli, N., & Reichert, P. (2021) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/s67zf
Social influence on Twitter: Examples & Projections
Dagla, M., Bairami, G. P., Nikolaou, F., & Schoinaraki, E. (2022) · Homo Virtualis · doi:10.12681/homvir.30317
Computational Modeling in Various Cognitive Fields
(2023) · Cambridge University Press eBooks · doi:10.1017/9781108755610.027
Adams, J. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.239446
Migrants, Families, and Religion: An Intriguing Research Agenda. Book’s Content and Introduction
Zanfrini, L. (2023) · IOS Press eBooks · doi:10.3233/stal230002
Atiqi, M. A. (2023) · Agent-based social systems · doi:10.1007/978-981-99-1770-9_8
Varshney, G., & Singh, A. P. (2023) · INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS · doi:10.47191/ijmra/v6-i6-19
Social Consolidation, Spatial Clustering, and Opinion PolarizationPreprint
Lee, J.-M. (2023) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/8esa2
Durrheim, K. L., & Schuld, M. (2023) · doi:10.31234/osf.io/n47xe
Fernandes, C. A. D. M. (2023) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.768055223
Rawlings, C. M., Smith, J. A., Moody, J., & McFarland, D. A. (2023) · Cambridge University Press eBooks · doi:10.1017/9781139794985.015
Experimental evidence shows that triadic social balance can be achieved through dyadic interactionsPreprint
Galešić, M., Olsson, H., Pham, T. M., Sorger, J., & Stefan Thurner, A. (2023) · doi:10.31219/osf.io/3bmg7
Reisinger, D., Vogrin, M., Wood, G. M. D. O., Schmickl, T., & Jäger, G. (2023) · Springer proceedings in complexity · doi:10.1007/978-3-031-34920-1_22
The Impact of Online Social Network Algorithms on User Opinion Formation
Chkhartishvili, A. G., & Gubanov, D. (2023) · International Conference Management of Large-Scale System Development (MLSD) · doi:10.1109/mlsd58227.2023.10303932
Liu, S., Guan, X., & Xia, H. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8318-6_18
The Diffusion of Binary Versus Continuous Behavior on Social Networks
Schneider, P. T., Buskens, V., & van de Rijt, A. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/s0882-614520230000040005
Metadata
- 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)
- Venue
- Journal of Artificial Societies and Social Simulation
- Published
- 2017
- Volume
- 20
- Issue
- 4
- Pages
- 31
- ISSN
- 1460-7425 (ISSN-L)
- 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
- FWCI
- 17.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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