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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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Free information disrupts even Bayesian crowds
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Stable Boundaries of Opinion Dynamics in Heterogeneous Spatial Complex Networks
Bierwirth, M., & Lengler, J. (2026) · Entropy · doi:10.3390/e28040401
Cultural tightness and social cohesion under coevolving beliefs, behaviors, and preferences
Zimmaro, F., Grilli, J., Galešić, M., & Stewart, A. J. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2525139123
Contributions to the modeling, the analysis and the control of networked hybrid dynamical systems
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Navigating complexity : essais on learning and decision making in the networked era
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Mean-field dynamics of attractive resource interaction: From uniform to aggregated states
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Dubovskaya, A. V., O’Sullivan, D. J. P., & Quayle, M. (2026) · PLOS complex systems. · doi:10.1371/journal.pcsy.0000115
Modeling individual self-protective behavior during epidemics
Yu, G., Garee, M. J., Ventresca, M., & Yih, Y. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014252
Wagenblast, T., Warnier, M., & Filatova, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6751340
İlter, H. K. (2026) · Journal of Computational Science · doi:10.1016/j.jocs.2026.102904
Analysis of Opinion Evolution Based on Hegselmann–Krause Model with Historical Opinion
Zhou, Y., & Sun, J. (2026) · Entropy · doi:10.3390/e28050541
Stable Boundaries of Opinion Dynamics in Heterogeneous Spatial Complex Networks
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Broekaert, J., Hancock, T. O., Hafiz, F. M. F., Hess, S., & Torre, D. L. (2026) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2026.102994
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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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