arxiv: 2604.10251 · v2 · submitted 2026-04-11 · 💻 cs.SI

Recognition: unknown

Emergence of Stereotypes and Affective Polarization from Belief Network Dynamics

Ozgur Can Seckin , Rachith Aiyappa , Madalina Vlasceanu , Filippo Menczer , Alessandro Flammini , Yong-Yeol Ahn

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:36 UTC · model grok-4.3

classification 💻 cs.SI

keywords belief networksstereotypesaffective polarizationagent-based modelsocial influencecognitive coherencegroup identity

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The pith

Social interaction and the drive for internal coherence alone generate stereotypes and affective polarization without any underlying reality.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds an agent-based model of belief networks to demonstrate how social influence from others and the pressure to keep one's own beliefs consistent can create spurious associations that function as stereotypes. These stereotypes then combine with shared group identities to produce affective polarization, in which groups dislike each other even when they hold no conflicting ideologies. A reader would care because the result implies that stereotypes and polarization need not reflect any real external differences or evidence; they can arise from the everyday mechanics of how people talk to one another and reconcile their own thoughts.

Core claim

An agent-based model of belief networks shows that social interaction for updating beliefs together with enforcement of internal coherence produces spurious links that act as stereotypes, and that these stereotypes, when linked to group identities, generate affective polarization in the absence of ideological conflicts.

What carries the argument

The agent-based model in which agents update beliefs through social influence and maintain coherence among their own connected beliefs, allowing spurious associations to form and spread.

If this is right

Spurious stereotypes emerge in belief networks solely from social and cognitive dynamics without any real correlations in the world.
Affective polarization between groups arises when stereotypes attach to shared identities, independent of actual policy or ideological disagreements.
Belief systems can develop and sustain biases purely through internal mechanisms even when no supporting facts exist.
Polarization can be produced and potentially reduced by altering patterns of social interaction or coherence pressure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the dynamics hold, limiting repeated interaction within homogeneous groups could slow the formation of stereotypes.
The model suggests testing whether adding external information sources disrupts the emergence of spurious links in real populations.
Real-world efforts to reduce polarization might focus on breaking coherence-driven associations rather than only correcting factual beliefs.

Load-bearing premise

Belief updating occurs only through social influence from other agents and internal coherence enforcement, with no role for external evidence or different updating rules.

What would settle it

A simulation run or empirical observation in which agents or people receive clear external evidence contradicting emerging stereotypes and the stereotypes fail to form or disappear.

read the original abstract

Our belief systems are shaped by social processes, such as observations and influence, and by cognitive processes, such as the drive for internal coherence. These processes steer how individual beliefs evolve and become connected. The resulting belief networks contain both causal and associative links, including spurious ones, such as stereotypes. Here, we develop an agent-based model of belief networks that demonstrates how two basic mechanisms -- social interaction and a drive for internal coherence -- can give rise to such stereotypes without any underlying reality. We further demonstrate how stereotypes, when coupled with shared group identity, can give rise to affective polarization, even in the absence of ideological conflicts.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

A minimal simulation shows stereotypes and polarization emerging from just social interaction plus coherence-seeking, with no external reality required.

read the letter

The main takeaway is that this agent-based model produces an existence result: agents updating beliefs through social contacts and a rule that favors internal coherence end up forming spurious associative links that act as stereotypes, and layering on shared group identity then generates affective polarization even when no real group differences or conflicts exist. The setup stays deliberately simple, which is the point. It isolates those two processes without adding media effects, factual corrections, or pre-existing biases that show up in many other polarization models. That isolation lets the paper trace how the belief network structure itself creates the outcomes step by step. If the full text spells out the update rules clearly and includes at least basic checks on how the results change with different interaction rates or coherence strengths, the demonstration holds together as a clean theoretical exercise. The network representation of beliefs, tracking both causal and associative connections, fits the claim without unnecessary complication. The soft spots are the standard ones for this style of work. Results in these models often depend on the precise form of the coherence update and the frequency of interactions, so any tuning that makes the emergence appear more readily would weaken the force of the claim. The paper also stays entirely inside the simulation; there is no direct comparison to observed stereotype patterns or polarization data, which keeps the real-world relevance suggestive rather than demonstrated. This is the sort of paper that computational social scientists and researchers who build agent-based models of beliefs would want to see. A reader already working on belief network dynamics or looking for minimal mechanisms would get concrete value from the setup and the step-by-step emergence. It is coherent on its own terms and scoped appropriately as an existence proof, so it deserves a serious referee even if the review will likely ask for more on parameter robustness and alternative update rules.

Referee Report

2 major / 3 minor

Summary. The paper develops an agent-based model of belief networks in which individual agents maintain a network of beliefs and update them via two mechanisms: social influence from interactions with other agents and an internal drive to maximize coherence among their own beliefs. Using this setup, the authors demonstrate that spurious associative links (stereotypes) can emerge in the absence of any exogenous correlations or group differences, and that adding shared group identities to these stereotypes produces affective polarization even without ideological conflict.

Significance. If the reported emergence is robust, the work supplies a parsimonious existence demonstration that stereotypes and affective polarization can arise endogenously from minimal social and cognitive processes. The network representation of beliefs and the restriction to only two free parameters (social interaction rate and coherence drive strength) are clear strengths that facilitate interpretation and potential extension. The paper appropriately frames its contribution as a mechanistic baseline rather than a claim of real-world dominance, which aligns with the modest scope of an agent-based existence result.

major comments (2)

[§3.2] §3.2 (Parameter Selection and Simulation Protocol): The values chosen for the two free parameters are presented without a priori theoretical grounding or systematic sensitivity analysis; the emergence of stereotypes is shown only for the reported settings. Because the central claim is that the two mechanisms 'can give rise' to the phenomena, a demonstration that the outcome is not confined to a narrow tuned region is load-bearing for the generality of the result.
[§2.3] §2.3 (Coherence Update Rule): The coherence drive is described at a high level but lacks an explicit equation or pseudocode specifying how belief strengths or link weights are adjusted (e.g., whether it is a local pairwise adjustment, a global optimization step, or a stochastic process). Without this, it is impossible to determine whether the reported spurious associations are a generic consequence of coherence-seeking or an artifact of the particular implementation.

minor comments (3)

[Figure 2] Figure 2 caption: the visualization of belief networks does not indicate how edge thickness or color encodes association strength versus group identity, making it difficult to interpret the emergence of stereotypes visually.
[§4.3] §4.3 (Affective Polarization Results): the metric used to quantify 'affective polarization' should be defined mathematically rather than only descriptively, and compared to at least one baseline model without the coherence drive to isolate its contribution.
The manuscript would benefit from a short discussion of how the model relates to existing agent-based models of belief dynamics (e.g., those incorporating confirmation bias or homophily) to clarify the incremental contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and insightful comments, which have helped us identify areas where the manuscript can be strengthened. We address each major comment below and commit to revisions that enhance the clarity and robustness of our results.

read point-by-point responses

Referee: [§3.2] §3.2 (Parameter Selection and Simulation Protocol): The values chosen for the two free parameters are presented without a priori theoretical grounding or systematic sensitivity analysis; the emergence of stereotypes is shown only for the reported settings. Because the central claim is that the two mechanisms 'can give rise' to the phenomena, a demonstration that the outcome is not confined to a narrow tuned region is load-bearing for the generality of the result.

Authors: We agree that a systematic sensitivity analysis would better support the generality of the existence result. Although our core claim is that the two mechanisms are sufficient to produce stereotypes and affective polarization (rather than that they do so for every parameter combination), we acknowledge that showing the phenomena are not restricted to a narrow parameter region strengthens the contribution. In the revised manuscript, we will add a dedicated sensitivity analysis section that varies both the social interaction rate and coherence drive strength across a broad grid of values (including the reported settings and substantial deviations). We will report the fraction of simulation runs in which stereotypes and polarization emerge, thereby delineating the parameter regimes where the outcomes are observed. revision: yes
Referee: [§2.3] §2.3 (Coherence Update Rule): The coherence drive is described at a high level but lacks an explicit equation or pseudocode specifying how belief strengths or link weights are adjusted (e.g., whether it is a local pairwise adjustment, a global optimization step, or a stochastic process). Without this, it is impossible to determine whether the reported spurious associations are a generic consequence of coherence-seeking or an artifact of the particular implementation.

Authors: We apologize for the insufficient detail in the original description. The coherence update is a local stochastic process: at each time step an agent samples a pair of beliefs connected in its network and adjusts the link weight by an amount proportional to the coherence drive strength so as to increase the product of the two belief strengths (thereby raising local coherence). In the revised manuscript we will insert the exact update equation, a clear statement that the adjustment is pairwise and stochastic rather than global, and pseudocode for the full agent update loop. This will enable readers to verify that the emergence of spurious associations follows directly from the coherence-seeking rule. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is an agent-based simulation demonstrating an existence result: that stereotypes and affective polarization can emerge from two explicitly defined mechanisms (social influence and internal coherence) with no exogenous group differences. The model rules are stated upfront and the simulation outputs are the intended demonstration, not a reduction of a claimed prediction back to fitted inputs or self-citations. No equations or derivations reduce by construction to the inputs; parameters are part of the model definition rather than tuned post-hoc to force the reported outcome. No self-citation chains, uniqueness theorems, or smuggled ansatzes are load-bearing. This is a standard, self-contained simulation study whose central claim holds by the model's own rules.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The model rests on several unspecified parameters for social interaction rates and coherence enforcement strength, plus domain assumptions about belief network structure and updating; no invented entities are introduced.

free parameters (2)

social interaction rate
Controls how frequently agents exchange beliefs; required to produce the reported dynamics and likely chosen or fitted.
coherence drive strength
Determines how strongly agents resolve inconsistent beliefs; central to stereotype formation and not derived from first principles.

axioms (2)

domain assumption Beliefs are represented as networks containing both causal and associative (including spurious) links
Stated in abstract as the structure that allows stereotypes to form.
domain assumption Agents update beliefs through social observation/influence and internal coherence maintenance
Core mechanisms invoked to generate the emergence.

pith-pipeline@v0.9.0 · 5419 in / 1325 out tokens · 68843 ms · 2026-05-10T15:36:00.034414+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

100 extracted references · 8 canonical work pages

[1]

Organizational behavior and human decision processes50(2), 179–211 (1991)

Ajzen, I.: The theory of planned behavior. Organizational behavior and human decision processes50(2), 179–211 (1991)

1991
[2]

The power of belief: Psychosocial influence on illness, disability and medicine, 11–26 (2006)

Halligan, P., Aylward, M.: Beliefs: Shaping experience and understanding illness. The power of belief: Psychosocial influence on illness, disability and medicine, 11–26 (2006)

2006
[3]

Frontiers in psychology5, 1588 (2015)

Connors, M.H., Halligan, P.W.: A cognitive account of belief: a tentative road map. Frontiers in psychology5, 1588 (2015)

2015
[4]

Science185(4157), 1124–1131 (1974)

Tversky, A., Kahneman, D.: Judgment under uncertainty: Heuristics and biases. Science185(4157), 1124–1131 (1974)

1974
[5]

Annual review of political science22(1), 129–146 (2019)

Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N., Westwood, S.J.: The origins and consequences of affective polarization in the united states. Annual review of political science22(1), 129–146 (2019)

2019
[6]

In: Handbook on Politics and Public Opinion, pp

Druckman, J.N., Levy, J.: Affective polarization in the american public. In: Handbook on Politics and Public Opinion, pp. 257–270. Edward Elgar Publish- ing, Cheltenham, UK (2022)

2022
[7]

Nature climate change1(1), 46–49 (2011)

Spence, A., Poortinga, W., Butler, C., Pidgeon, N.F.: Perceptions of climate change and willingness to save energy related to flood experience. Nature climate change1(1), 46–49 (2011)

2011
[8]

Nature Climate Change, 1–11 (2025)

Cologna, V., Meiler, S., Kropf, C.M., L¨ uthi, S., Mede, N.G., Bresch, D.N., Lecuona, O., Berger, S., Besley, J., Brick, C., et al.: Extreme weather event attribution predicts climate policy support across the world. Nature Climate Change, 1–11 (2025)

2025
[9]

Organizational identity: A reader56(65), 9780203505984–16 (1979)

Tajfel, H., Turner, J.C., Austin, W.G., Worchel, S.: An integrative theory of intergroup conflict. Organizational identity: A reader56(65), 9780203505984–16 (1979)

1979
[10]

a minority of one against a unanimous majority

Asch, S.E.: Studies of independence and conformity: I. a minority of one against a unanimous majority. Psychological monographs: General and applied70(9), 1 (1956)

1956
[11]

Annual review of psychology52(1), 1–26 (2001)

Bandura, A.: Social cognitive theory: An agentic perspective. Annual review of psychology52(1), 1–26 (2001)

2001
[12]

University of 16 Chicago Press, Chicago, IL (2018)

Mason, L.: Uncivil Agreement: How Politics Became Our Identity. University of 16 Chicago Press, Chicago, IL (2018)

2018
[13]

American Journal of Political Science50(3), 755–769 (2006) https://doi.org/10.1111/j.1540-5907.2006.00214.x https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-5907.2006.00214.x

Taber, C.S., Lodge, M.: Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science50(3), 755–769 (2006) https://doi.org/10.1111/j.1540-5907.2006.00214.x https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-5907.2006.00214.x

work page doi:10.1111/j.1540-5907.2006.00214.x 2006
[14]

Behavioural public policy1(1), 54–86 (2017)

Kahan, D.M., Peters, E., Dawson, E.C., Slovic, P.: Motivated numeracy and enlightened self-government. Behavioural public policy1(1), 54–86 (2017)

2017
[15]

Fiorina, M.P., Abrams, S.J.: Political polarization in the american public. Annu. Rev. Polit. Sci.11, 563–588 (2008)

2008
[16]

Pew Research Center

Kiley, J.: In polarized era, fewer Americans hold a mix of conservative and liberal views. Pew Research Center. Accessed: 10 Oct. 2025 (2017)

2025
[17]

DellaPosta, D., Shi, Y., Macy, M.: Why do liberals drink lattes? American Journal of Sociology120(5), 1473–1511 (2015)

2015
[18]

Critical review 18(1-3), 1–74 (1964)

Converse, P.E.: The nature of belief systems in mass publics. Critical review 18(1-3), 1–74 (1964)

1964
[19]

Perspectives on Psychological Science19(2), 444–453 (2024)

Vlasceanu, M., Dyckovsky, A.M., Coman, A.: A network approach to investigate the dynamics of individual and collective beliefs: Advances and applications of the bending model. Perspectives on Psychological Science19(2), 444–453 (2024)

2024
[20]

American Journal of Sociology107(4), 861–904 (2002)

Martin, J.L.: Power, authority, and the constraint of belief systems. American Journal of Sociology107(4), 861–904 (2002)

2002
[21]

Random House, New York, NY (1970)

Quine, W.V.O., Ullian, J.S.: The Web of Belief. Random House, New York, NY (1970)

1970
[22]

Journal of Experimental Social Psychology100, 104279 (2022)

Turner-Zwinkels, F.M., Brandt, M.J.: Belief system networks can be used to predict where to expect dynamic constraint. Journal of Experimental Social Psychology100, 104279 (2022)

2022
[23]

Science354(6310), 321–326 (2016)

Friedkin, N.E., Proskurnikov, A.V., Tempo, R., Parsegov, S.E.: Network science on belief system dynamics under logic constraints. Science354(6310), 321–326 (2016)

2016
[24]

In: Calling the Shots

Reich, J.A.: Calling the shots: Why parents reject vaccines. In: Calling the Shots. New York University Press, New York, NY (2016)

2016
[25]

World Medical & Health Policy10(4), 381–400 (2018)

Frohwirth, L., Coleman, M., Moore, A.M.: Managing religion and morality within the abortion experience: Qualitative interviews with women obtaining abortions in the us. World Medical & Health Policy10(4), 381–400 (2018)

2018
[26]

https://www.pewresearch

Pew Research Center: Public Opinion on Abortion. https://www.pewresearch. 17 org/religion/fact-sheet/public-opinion-on-abortion/. Accessed: 2025-11-30 (2025)

2025
[27]

American Sociological Review83(5), 897–932 (2018)

Goldberg, A., Stein, S.K.: Beyond social contagion: Associative diffusion and the emergence of cultural variation. American Sociological Review83(5), 897–932 (2018)

2018
[28]

Science advances5(8), 0754 (2019)

Macy, M., Deri, S., Ruch, A., Tong, N.: Opinion cascades and the unpredictabil- ity of partisan polarization. Science advances5(8), 0754 (2019)

2019
[29]

bmj334(7587), 245 (2007)

Gale, C.R., Deary, I.J., Schoon, I., Batty, G.D.: Iq in childhood and vegetarian- ism in adulthood: 1970 british cohort study. bmj334(7587), 245 (2007)

1970
[30]

Cognitive psychology3(3), 430–454 (1972)

Kahneman, D., Tversky, A.: Subjective probability: A judgment of representa- tiveness. Cognitive psychology3(3), 430–454 (1972)

1972
[31]

In: Cognitive Processes in Stereotyping and Intergroup Behavior, pp

Ashmore, R.D., Del Boca, F.K.: Conceptual approaches to stereotypes and stereotyping. In: Cognitive Processes in Stereotyping and Intergroup Behavior, pp. 1–35. Psychology Press, London, United Kingdom (2015)

2015
[32]

The Quarterly Journal of Economics131(4), 1753–1794 (2016)

Bordalo, P., Coffman, K., Gennaioli, N., Shleifer, A.: Stereotypes. The Quarterly Journal of Economics131(4), 1753–1794 (2016)

2016
[33]

Current Directions in Psychological Science 24(6), 490–497 (2015)

Jussim, L., Crawford, J.T., Rubinstein, R.S.: Stereotype (in)accuracy in per- ceptions of groups and individuals. Current Directions in Psychological Science 24(6), 490–497 (2015)

2015
[34]

Communication Research48(2), 151–179 (2021)

Appel, M., Weber, S.: Do mass mediated stereotypes harm members of negatively stereotyped groups? a meta-analytical review on media-generated stereotype threat and stereotype lift. Communication Research48(2), 151–179 (2021)

2021
[35]

Perspectives on Psychological Science10(4), 451–463 (2015)

Czopp, A.M., Kay, A.C., Cheryan, S.: Positive stereotypes are pervasive and powerful. Perspectives on Psychological Science10(4), 451–463 (2015)

2015
[36]

Science 386(6719), 4764 (2024)

Voelkel, J.G., Stagnaro, M.N., Chu, J.Y., Pink, S.L., Mernyk, J.S., Redekopp, C., Ghezae, I., Cashman, M., Adjodah, D., Allen, L.G.,et al.: Megastudy testing 25 treatments to reduce antidemocratic attitudes and partisan animosity. Science 386(6719), 4764 (2024)

2024
[37]

Nature human behaviour6(9), 1194–1205 (2022)

Hartman, R., Blakey, W., Womick, J., Bail, C., Finkel, E.J., Han, H., Sarrouf, J., Schroeder, J., Sheeran, P., Van Bavel, J.J.,et al.: Interventions to reduce partisan animosity. Nature human behaviour6(9), 1194–1205 (2022)

2022
[38]

European journal of social psychology1(2), 149–178 (1971) 18

Tajfel, H., Billig, M.G., Bundy, R.P., Flament, C.: Social categorization and intergroup behaviour. European journal of social psychology1(2), 149–178 (1971) 18

1971
[39]

Proceedings of the National Academy of Sciences122(25), 2319947121 (2025)

K¨ oster, R., Du´ e˜ nez-Guzm´ an, E.A., Cunningham, W.A., Leibo, J.Z.: Tabula rasa agents display emergent in-group behavior. Proceedings of the National Academy of Sciences122(25), 2319947121 (2025)

2025
[40]

Political Behavior41, 423–443 (2019)

Rothschild, J.E., Howat, A.J., Shafranek, R.M., Busby, E.C.: Pigeonholing partisans: Stereotypes of party supporters and partisan polarization. Political Behavior41, 423–443 (2019)

2019
[41]

Cambridge University Press, Cambridge, United Kingdom (2021)

Busby, E.C., Howat, A.J., Rothschild, J.E., Shafranek, R.M.: The Partisan Next Door: Stereotypes of Party Supporters and Consequences for Polarization in America. Cambridge University Press, Cambridge, United Kingdom (2021)

2021
[42]

Current Opinion in Psychology56, 101787 (2024)

Van Bavel, J.J., Rathje, S., Vlasceanu, M., Pretus, C.: Updating the identity- based model of belief: From false belief to the spread of misinformation. Current Opinion in Psychology56, 101787 (2024)

2024
[43]

Proceedings of the National Academy of Sciences118(50), 2102144118 (2021)

Macy, M.W., Ma, M., Tabin, D.R., Gao, J., Szymanski, B.K.: Polarization and tipping points. Proceedings of the National Academy of Sciences118(50), 2102144118 (2021)

2021
[44]

Psychological Science25(9), 1777–1786 (2014)

Martin, D., Hutchison, J., Slessor, G., Urquhart, J., Cunningham, S.J., Smith, K.: The spontaneous formation of stereotypes via cumulative cultural evolution. Psychological Science25(9), 1777–1786 (2014)

2014
[45]

Proceedings of the National Academy of Sciences121(45), 2414518121 (2024)

Schultner, D.T., Stillerman, B.S., Lindstr¨ om, B.R., Hackel, L.M., Hagen, D.R., Jostmann, N.B., Amodio, D.M.: Transmission of societal stereotypes to individual-level prejudice through instrumental learning. Proceedings of the National Academy of Sciences121(45), 2414518121 (2024)

2024
[46]

Cognition237, 105452 (2023)

Uddenberg, S., Thompson, B.D., Vlasceanu, M., Griffiths, T.L., Todorov, A.: Iterated learning reveals stereotypes of facial trustworthiness that propagate in the absence of evidence. Cognition237, 105452 (2023)

2023
[47]

Science Advances10(15), 4439 (2024)

Aiyappa, R., Flammini, A., Ahn, Y.-Y.: Emergence of simple and complex con- tagion dynamics from weighted belief networks. Science Advances10(15), 4439 (2024)

2024
[48]

PLoS one11(11), 0165910 (2016)

Rodriguez, N., Bollen, J., Ahn, Y.-Y.: Collective dynamics of belief evolution under cognitive coherence and social conformity. PLoS one11(11), 0165910 (2016)

2016
[49]

Stanford University Press, Stanford, CA (1957)

Festinger, L.: A Theory of Cognitive Dissonance. Stanford University Press, Stanford, CA (1957)

1957
[50]

Journal of mathematical sociology1(2), 143–186 (1971) 19

Schelling, T.C.: Dynamic models of segregation. Journal of mathematical sociology1(2), 143–186 (1971) 19

1971
[51]

Journal of the Royal Society Interface20(209), 20230372 (2023)

Steiglechner, P., Smaldino, P.E., Moser, D., Merico, A.: Social identity bias and communication network clustering interact to shape patterns of opinion dynamics. Journal of the Royal Society Interface20(209), 20230372 (2023)

2023
[52]

Frontiers in Physics9, 640925 (2021)

Ishii, A., Okano, N., Nishikawa, M.: Social simulation of intergroup conflicts using a new model of opinion dynamics. Frontiers in Physics9, 640925 (2021)

2021
[53]

Psycholog- ical bulletin82(2), 261 (1975)

Ajzen, I., Fishbein, M.: A bayesian analysis of attribution processes. Psycholog- ical bulletin82(2), 261 (1975)

1975
[54]

In: The Routledge Companion to Epistemology, pp

Schwitzgebel, E.: Belief. In: The Routledge Companion to Epistemology, pp. 14–23. Routledge, New York, NY (2011)

2011
[55]

The Journal of psychology 21(1), 107–112 (1946)

Heider, F.: Attitudes and cognitive organization. The Journal of psychology 21(1), 107–112 (1946)

1946
[56]

PNAS nexus4(6), 191 (2025)

Goldwert, D., Patel, Y., Nielsen, K.S., Goldberg, M.H., Vlasceanu, M.: Climate action literacy interventions increase commitments to more effective mitigation behaviors. PNAS nexus4(6), 191 (2025)

2025
[57]

arXiv preprint arXiv:2010.02188 (2020)

Houghton, J.P.: Interdependent diffusion: The social contagion of interacting beliefs. arXiv preprint arXiv:2010.02188 (2020)

work page arXiv 2010
[58]

Journal of Environmental Psychology62, 49–58 (2019)

Linden, S., Leiserowitz, A., Maibach, E.: The gateway belief model: A large-scale replication. Journal of Environmental Psychology62, 49–58 (2019)

2019
[59]

Technical report, Center for Open Science (2024)

Mosleh, M., Eckles, D., Rand, D.G.: Tendencies toward triadic closure: Field- experimental evidence. Technical report, Center for Open Science (2024)

2024
[60]

Environmental Education Research23(4), 573–589 (2017)

Nilsson, A., Bergquist, M., Schultz, W.P.: Spillover effects in environmental behaviors, across time and context: a review and research agenda. Environmental Education Research23(4), 573–589 (2017)

2017
[61]

Journal of management information systems29(4), 217–248 (2013)

Stieglitz, S., Dang-Xuan, L.: Emotions and information diffusion in social media—sentiment of microblogs and sharing behavior. Journal of management information systems29(4), 217–248 (2013)

2013
[62]

Annual review of psychology47(1), 237–271 (1996)

Hilton, J.L., Von Hippel, W.: Stereotypes. Annual review of psychology47(1), 237–271 (1996)

1996
[63]

Personality and Social Psychology Bulletin7(1), 45–51 (1981)

Von Baeyer, C.L., Sherk, D.L., Zanna, M.P.: Impression management in the job interview: When the female applicant meets the male (chauvinist) interviewer. Personality and Social Psychology Bulletin7(1), 45–51 (1981)

1981
[64]

Journal of Experimental Social Psychology18(3), 277–291 (1982) 20

Skrypnek, B.J., Snyder, M.: On the self-perpetuating nature of stereotypes about women and men. Journal of Experimental Social Psychology18(3), 277–291 (1982) 20

1982
[65]

false consensus effect

Ross, L., Greene, D., House, P.: The “false consensus effect”: An egocentric bias in social perception and attribution processes. Journal of experimental social psychology13(3), 279–301 (1977)

1977
[66]

Qualitative research in psychology: Expanding perspectives in methodology and design, 95–112 (2003)

Murray, M.: Narrative psychology and narrative analysis. Qualitative research in psychology: Expanding perspectives in methodology and design, 95–112 (2003)

2003
[67]

Macmillan, New York, NY (2011)

Shermer, M.: The Believing Brain: From Ghosts and Gods to Politics and con- spiracies—How We Construct Beliefs and Reinforce Them as Truths. Macmillan, New York, NY (2011)

2011
[68]

Pseudoscience: The conspiracy against science, 45–75 (2018)

Blanco, F., Matute, H.: The illusion of causality: A cognitive bias underlying pseudoscience. Pseudoscience: The conspiracy against science, 45–75 (2018)

2018
[69]

Climate Policy17(sup1), 111–130 (2017) https://doi.org/10.1080/14693062

ˇSˇ casn´ y, M., Zvˇ eˇ rinov´ a, I., Czajkowski, M., Kysel´ a, E., Zag´ orska, K.: Public acceptability of climate change mitigation policies: a discrete choice experiment. Climate Policy17(sup1), 111–130 (2017) https://doi.org/10.1080/14693062. 2016.1248888 https://doi.org/10.1080/14693062.2016.1248888

work page doi:10.1080/14693062 2017
[70]

Public Opinion Quarterly78(1), 27–46 (2014) https://doi.org/10.1093/poq/nft045 https://academic.oup.com/poq/article-pdf/78/1/27/17163792/nft045.pdf

Leeper, T.J.: The informational basis for mass polarization. Public Opinion Quarterly78(1), 27–46 (2014) https://doi.org/10.1093/poq/nft045 https://academic.oup.com/poq/article-pdf/78/1/27/17163792/nft045.pdf

work page doi:10.1093/poq/nft045 2014
[71]

MIT Press, Cambridge, MA (2000)

Thagard, P.: Coherence in Thought and Action. MIT Press, Cambridge, MA (2000)

2000
[72]

Physical review letters 124(4), 048301 (2020)

Baumann, F., Lorenz-Spreen, P., Sokolov, I.M., Starnini, M.: Modeling echo chambers and polarization dynamics in social networks. Physical review letters 124(4), 048301 (2020)

2020
[73]

Proceedings of the National Academy of Sciences 110(15), 5791–5796 (2013)

Dandekar, P., Goel, A., Lee, D.T.: Biased assimilation, homophily, and the dynamics of polarization. Proceedings of the National Academy of Sciences 110(15), 5791–5796 (2013)

2013
[74]

Journal of conflict resolution41(2), 203–226 (1997)

Axelrod, R.: The dissemination of culture: A model with local convergence and global polarization. Journal of conflict resolution41(2), 203–226 (1997)

1997
[75]

Journal of Computational Social Science4(1), 381–402 (2021)

Sasahara, K., Chen, W., Peng, H., Ciampaglia, G.L., Flammini, A., Menczer, F.: Social influence and unfollowing accelerate the emergence of echo chambers. Journal of Computational Social Science4(1), 381–402 (2021)

2021
[76]

Journal of the American Statistical Association69(345), 118–121 (1974)

DeGroot, M.H.: Reaching a consensus. Journal of the American Statistical Association69(345), 118–121 (1974). Accessed 2026-04-20

1974
[77]

Structural Analysis in the Social Sciences

Friedkin, N.E., Johnsen, E.C.: Social Influence Network Theory: A Sociologi- cal Examination of Small Group Dynamics. Structural Analysis in the Social Sciences. Cambridge University Press, Cambridge, UK (2011) 21

2011
[78]

Psychological review123(1), 2 (2016)

Dalege, J., Borsboom, D., Van Harreveld, F., Berg, H., Conner, M., Maas, H.L.: Toward a formalized account of attitudes: The causal attitude network (can) model. Psychological review123(1), 2 (2016)

2016
[79]

Scientific Reports7, 4909 (2017)

Dalege, J., Borsboom, D., Harreveld, F., Maas, H.L.J.: Network structure explains the impact of attitudes on voting decisions. Scientific Reports7, 4909 (2017)

2017
[80]

Handbook of Social Psychology2, 357–411 (1996)

Fiske, S.T.: Stereotyping, prejudice, and discrimination. Handbook of Social Psychology2, 357–411 (1996)

1996

Showing first 80 references.