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REVIEW 4 major objections 4 minor 63 references

The Wisdom of Intellectually Humble Networks

T0 review · 4 major / 4 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Networks of intellectually humble agents estimate more accurately and polarize less than matched control networks.

desk verdict A clean proof-of-concept simulation showing that one particular parameterization of intellectual humility in DeGroot updating improves collective accuracy, but the effect depends on uncalibrated, unreported slope parameters. read the letter →

arxiv 2502.02015 v2 pith:KHJ7UPVM submitted 2025-02-04 cs.SI

Mohammad Ratul Mahjabin , Raiyan Abdul Baten This is my paper · ORCID
classification cs.SI
keywords intellectualhumilitycollectivewisdomofcrowdssocialnetworksagent-basedmodelingpolarizationbeliefrevisionDeGrootmodel
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks whether a measurable psychological trait—intellectual humility, the willingness to revise one's beliefs when evidence warrants—can make a group's shared beliefs more accurate and less polarized when people influence one another through a social network. Using agent-based simulations calibrated to four real-world estimation datasets, it argues that it can: networks whose agents are modeled as intellectually humble show significantly lower estimation errors and lower political polarization than statistically matched control networks. The advantage is explained by the Diversity Prediction Theorem: humble agents reduce average individual error far more than they reduce predictive diversity, so collective error falls by 12.6%. If correct, the result gives intervention designers a concrete lever—raising intellectual humility—to improve collective judgment in organizations, online platforms, and democratic deliberation.

What carries the argument

The load-bearing mechanism is the modulated self-weight in the DeGroot update rule: each agent $i$ revises its estimate as a weighted average of its own estimate and its neighbors' mean, with weight $$\tilde{\$\alpha$}^{(c)}_i = \$alpha^{0}$_i + $w^{{(c)}}$_1 + $w^{{(c)}}$_2(d_i - \bar{d}_{j\in N_i}) + $w^{{(c)}}$_3 + $w^{{(c)}}$_4 h_i,$$ where $d_i$ is the agent's evidence quality and $h_i$ the fraction of same-party neighbors. Treatment agents use a steeper evidence-quality slope $w_2$ and a flatter homophily slope $w_4$ than controls, which makes them lower their self-weight when peers' evidence is stronger and resist partisan reinforcement. The Diversity Prediction Theorem (collective error squared equals mean individual error squared minus predictive diversity) then turns this into a collective advantage: the individual-error reduction outweighs the small diversity loss.

What would settle it

Re-running the simulation with the treatment slopes $w_2$ and $w_4$ set equal to the control slopes should eliminate the error and polarization differences; if the advantage persists, the result is not attributable to the intellectual-humility parameterization.

Watch

Extended reading notes

Core claim

The paper's central discovery is that encoding intellectual humility in a standard DeGroot belief-updating process yields measurably wiser networks. In the model, each agent's weight on its own estimate is modulated by two IH-driven signals: the perceived quality of evidence from neighbors and the partisan similarity of those neighbors. Agents with IH are given a steeper response to evidence quality (they lower their self-weight more when neighbors have better evidence, and raise it more when neighbors have worse evidence) and a flatter response to homophily (they discount similar-party agreement less). Across 100 replications per network structure, treatment networks show lower estimation error ($\beta = -0.11$, SE $= 0.01$, $P < 0.001$), lower polarization ($\beta = -0.09$, SE $= 0.02$, $P < 0.01$), and higher revision coefficients ($\beta = 0.41$, SE $= 0.004$, $P < 0.001$) than matched controls. Decomposing collective error via the Diversity Prediction Theorem, average individual error squared falls 19.5% while diversity falls only 6.9%, producing a 12.6% net reduction in collective error squared. The pattern is robust across four datasets and four network structures, and persists even when only 10–60% of agents are endowed with IH.

Load-bearing premise

The whole result follows from the hand-chosen differences in how treatment agents respond to evidence quality and partisan similarity; if real intellectually humble people do not actually revise their self-weights with those slopes, the simulated wisdom gain would not occur.

Editorial extensions

If this is right

  • If the model is right, raising intellectual humility in a group—for example through training or interface design—should lower the group's estimation error without requiring any individual to be smarter.
  • The mechanism predicts that interventions should target the balance of belief revision: less accurate members revise more, more accurate members stick; a higher revision coefficient is the observable signature of a wise network.
  • Because results hold across egalitarian, power-law, small-world, and star networks, the benefit is structural—it does not depend on a particular contact pattern.
  • The robustness to $f$ as low as 10% suggests that even a minority of intellectually humble members can shift a networked group's collective accuracy.
  • In politically charged estimation tasks, reduced cross-party pairwise distance implies IH can dampen polarization of factual beliefs, not just improve accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An empirical test follows directly: measure the self-weight modulation slopes $w_2$ and $w_4$ in human groups experimentally (e.g., via estimation tasks with evidence prompts before and after social information), and check whether high-IH individuals indeed show steeper $w_2$ and flatter $w_4$; the paper's predicted collective benefit is contingent on those slopes being real.
  • The model treats evidence quality $d$ as a synthetic score correlated with initial accuracy; a natural extension would replace it with actual message content in text-based discussion data to see whether the IH advantage survives realistic evidence distributions.
  • The diversity loss of 6.9% hints at a trade-off frontier: interventions that push humility too far could erode the independence that makes crowds wise; the model could be used to find an optimal humility level before diversity loss overtakes individual-error gains.
  • Because the treatment is defined by parameter changes rather than measured IH, the paper should be read as a proof of concept for the mechanism's plausibility, not as evidence that IH itself is causally responsible in real humans—though the robustness across datasets is suggestive.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes an agent-based model of belief updating on social networks, extending the DeGroot averaging rule with self-weights that are modulated by two factors: the difference between an agent's evidence quality and that of its neighbors, and the fraction of same-party neighbors. The treatment condition is designed to represent intellectual humility by making the evidence-quality slope steeper and the homophily slope flatter than in control networks. Using matched treatment/control networks initialized from four published experimental datasets, the authors report that IH-enhanced networks exhibit significantly lower estimation errors, lower polarization, higher Revision Coefficients, and improved collective error as decomposed by the Diversity Prediction Theorem. They further report robustness across four network structures and four datasets, and state that the qualitative results survive even when only a fraction of agents are treated. The paper frames the contribution as a proof of concept connecting intellectual humility to collective wisdom.

Significance. If the central result were well grounded, the paper would provide a useful proof of concept that intellectual humility can improve collective accuracy and reduce polarization in social networks, with potential implications for intervention design. The study has notable strengths: it calibrates baseline agent properties (initial estimates, self-weights, party labels) to multiple real datasets; it uses a matched treatment/control design with random node assignment; it reports mixed-effects models with confidence intervals; and it provides publicly available code and data. However, the current significance is limited by a load-bearing gap: the IH treatment is defined by hand-set parameters in Eq. (5) that are never reported or varied, and the synthetic evidence-quality score is generated to correlate with initial accuracy. The reported effects are therefore conditional on untested assumptions. The paper is a coherent simulation study, but its empirical grounding is more modest than the phrase 'data-calibrated simulations' suggests.

major comments (4)
  1. [Capturing IH in Self-weight Parameters, Eq. (5)] The treatment condition is operationalized by choosing steeper evidence-quality slopes and flatter homophily slopes, yet the manuscript never reports the numerical values of w1-w4 used in any simulation, nor does the robustness section (Table 2) vary them. Because the reported effect sizes (e.g., beta = -0.11, SE = 0.01 for error; beta = 0.41, SE = 0.004 for the Revision Coefficient) are functions of these slopes, the results are conditional on unstated hand-set choices. Please report the full parameterization, justify the values from prior empirical work on intellectual humility (or explain why they are not needed), and provide sensitivity analyses showing that the results hold over a defensible range of w1-w4.
  2. [Effects of Evidence Quality Evaluation, Eq. (3)] The synthetic objective evidence-quality score d is generated to correlate with initial estimation accuracy plus Gaussian noise, but the paper does not state the correlation strength or the noise level. Because treatment agents in Eq. (5) raise their self-weight when d exceeds the neighbor average and lower it otherwise, and because d is correlated with accuracy, the treatment mechanically causes accurate agents to persist and inaccurate agents to revise more. This alone would produce a higher Revision Coefficient and lower collective error, independent of any psychological process associated with intellectual humility. Please quantify the d-accuracy relationship (e.g., report the correlation and variance of the noise) and run sensitivity or placebo tests where d is uncorrelated with accuracy or the correlation is varied. Without these, the main simulation result may be an artifact of this generative assumption.
  3. [Abstract and Results] The phrase 'data-calibrated simulations' overstates the empirical grounding. Baseline estimates, self-weights, and network structures are sampled from real datasets, but the IH treatment parameters (w1-w4) and the evidence-quality score d are entirely synthetic and are not calibrated to any empirical measures of intellectual humility. Either calibrate these parameters to relevant data (for instance, from studies that measure IH alongside estimation revisions in social-influence experiments) or revise the abstract and framing to state explicitly that the model is a proof of concept with synthetic treatment parameters.
  4. [Results, The Moderating Role of Revision Coefficients] The paper interprets the elevated Revision Coefficient as evidence of a mechanism through which intellectual humility improves collective wisdom. However, because the treatment parameterization in Eq. (5) directly assigns higher self-weights to more accurate agents (via the d-accuracy correlation) and lower self-weights to less accurate agents, the increase in the Revision Coefficient is a designed feature of the treatment rather than an emergent property. To support the mechanism claim, the authors should demonstrate that the Revision Coefficient increases even when the error reduction is accounted for (for example, by comparing conditions that vary the revision dynamics independently of accuracy), or temper the claim to a descriptive observation.
minor comments (4)
  1. [Robustness] The sentence 'Simulations show that the main results remain consistent even when f is as low as 10%, with statistical significance emerging for f between 20% and 60%' is ambiguous; if results are consistent at 10%, it is unclear why statistical significance only emerges at higher f, and the notion of 'consistent' should be defined explicitly.
  2. [References] In the reference list, the author name 'V orobej, M.' should be corrected to 'Vorobej, M.'.
  3. [Figure 1 caption] The caption of Figure 1 could state the number of replications and the network structure used for the main results, since these details currently appear only in the main text.
  4. [Table 1] The column header 'Alpha' is ambiguous; consider renaming it to indicate whether baseline self-weights are available in the dataset, and add a footnote explaining the column.

Circularity Check

2 steps flagged · score 6.0 of 10

The main accuracy and revision-coefficient gains are installed by the treatment parameterization in Eq. (5): IH is defined as producing exactly the weight dynamics that are then reported as evidence of IH's benefits.

  1. self definitional [Theoretical Modeling and Simulation / Capturing IH in Self-weight Parameters, Eqs. (3)-(5); Results / The Moderating Role of Revision Coefficients]
    "we assign each agent a synthetic objective evidence quality score, d, correlating with their initial estimation accuracy with added Gaussian noise ... w(c)2 is steeper for treatment networks than control networks to capture the superior evidence evaluation capabilities of the treatment agents ... The treatment agents, however, can better discern evidence qualities and resist partisan homophily biases. This results in a steeper slope of the treatment curve in Figure 1(D), increasing the Revision Coefficient and pulling the group estimations closer to the truth."

    In Eq. (5), the treatment self-weight is a linear function of (d_i - average neighbor d) with a steeper positive slope w2. Because d is generated to correlate with initial accuracy, treatment agents who are initially less accurate mechanically receive lower self-weights, while more accurate agents receive higher self-weights. The DeGroot update then makes low-accuracy agents revise more and high-accuracy agents revise less. The Revision Coefficient (Eq. 8) is exactly the partial correlation between initial error and revision, so this construction forces the reported positive revision-coefficient increase and the resulting error reduction.

  2. self definitional [Theoretical Modeling and Simulation / Capturing IH in Self-weight Parameters, Eq. (5); Results: Estimation Errors and Polarization Reduce in Intellectually Humble Networks]
    "w(c)4 is less steep for treatment networks than control networks to capture the superior ability of the treatment agents to ignore partisan bias ... The treatment networks show significantly lower polarization than the control networks (β = −0.09, SE = 0.02, P < 0.01 ...)"

    Treatment agents are defined as having a flatter negative response to the same-party fraction h_i, i.e., they are constructed to resist partisan homophily. Polarization is measured as the average cross-party distance after the DeGroot updates. Since the treatment directly weakens the mechanism that pulls agents toward same-party neighbors, the observed reduction in polarization is a consequence of the input parameterization rather than an independent test of whether intellectual humility reduces polarization in real populations.

full rationale

The paper is a transparent proof-of-concept simulation, and it explicitly acknowledges that the calibration datasets contain no evidence-quality ratings: 'The users in our calibration datasets never gave any evidence supporting their estimates.' The key circularity is definitional: intellectual humility is encoded in Eq. (5) as steeper evidence-quality weighting (w2) and flatter homophily weighting (w4), while the synthetic evidence-quality score d is generated to correlate with initial accuracy. Under this construction, less accurate treatment agents are forced to lower their self-weights and more accurate agents to raise theirs, which by the DeGroot update mechanically raises the Revision Coefficient and lowers collective error. The reported beta coefficients, the 12.6% lower collective error, and the moderation story in Figure 1(D) are therefore largely restatements of the treatment definition rather than independent empirical discoveries. The model does retain some independent content: the Diversity Prediction Theorem decomposition, robustness across four datasets and four network structures, and the fraction-f threshold results are nontrivial computations conditional on the chosen parameterization. But because the defining slopes w1...w4 and the d-accuracy correlation are never estimated from intellectual-humility data, varied, or reported, the central claim that IH 'can foster' collective wisdom is not independently supported beyond the assumption that IH produces exactly this weight-adjustment pattern. This warrants a partial-circularity score of 6, not higher, because the paper is internally coherent as a proof of concept and does not rely on self-citation chains to establish its formalism.

Assumptions & free parameters 7 free parameters · 5 assumptions · 1 invented entities

The central claim rests on a chain of modeling choices: the DeGroot update rule, the psychological premise that IH improves evidence evaluation and homophily resistance, and the synthetic evidence quality score. The w parameters and the synthetic score are not empirically calibrated, so the treatment effect is largely induced by construction.

free parameters (7)
  • w1 (intercept for evidence-quality modulation) = Not reported in text
    Chosen by hand to shift baseline self-weights in each condition; the value is not given in the paper.
  • w2 (evidence-quality slope) = Treatment steeper than control, exact values not reported
    This slope controls how strongly agents reduce self-weight when peers' evidence quality is higher; the treatment advantage in evidence evaluation is encoded directly in this parameter.
  • w3 (intercept for homophily modulation) = Not reported in text
    Chosen by hand to shift baseline self-weights based on peer party alignment; value not reported.
  • w4 (homophily slope) = Less negative for treatment, exact values not reported
    This slope controls how strongly agents reduce self-weight when peers share their party; the treatment's resistance to partisan bias is encoded directly in this parameter.
  • Evidence quality score d (Gaussian noise) = Correlated with initial accuracy, noise magnitude not specified
    Synthetic score assigned to each agent; the assumed correlation with initial accuracy is an input that drives the evidence-quality modulation.
  • Synthetic baseline self-weight alpha0 = Stochastic linear relation from Burton et al. (2021)
    Used to generate baseline self-weights in the three datasets that do not provide them; this is an imposed stochastic relationship, not measured data.
  • Network generation parameters (m=2, p=0.5, k=4) = Fixed values from cited network models
    Barabasi-Albert, Watts-Strogatz, and regular graph parameters are set to standard values; robustness is shown across these choices.
assumptions (5)
  • domain assumption DeGroot linear averaging belief update rule (Eq. 1)
    The entire simulation rests on the assumption that people update beliefs as a weighted average of their own estimate and their neighbors' average estimate.
  • domain assumption Intellectually humble individuals evaluate evidence more accurately and resist partisan homophily more effectively
    This psychological premise, drawn from the cited IH literature, is encoded directly into the treatment parameters w2 and w4.
  • ad hoc to paper Synthetic evidence quality d correlates with initial estimation accuracy
    The paper asserts this stochastic linear relation to generate evidence quality scores, but provides no empirical calibration. It is a load-bearing modeling choice.
  • standard math Diversity Prediction Theorem (Eq. 9)
    The collective error decomposition is a mathematical identity used to interpret the simulation results.
  • domain assumption Self-weights clipped to [0, 1]
    The paper clips modulated self-weights to stay in [0,1], a property observed in the calibration datasets.
invented entities (1)
  • Synthetic objective evidence quality score d
    purpose: Provides each agent with a signal about the quality of its own and peers' evidence, which drives the self-weight modulation in Eq. 3.
    No empirical measurement of evidence quality exists in the calibration datasets; the score is generated by an assumed stochastic relation with initial accuracy.

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Cite this review

Pith. "Pith review of The Wisdom of Intellectually Humble Networks." pith.science (2026). https://pith.science/paper/KHJ7UPVM

@misc{pith2026250202015,
  author       = {Pith},
  title        = {Pith review of: The Wisdom of Intellectually Humble Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KHJ7UPVM}},
  note         = {Machine review of arXiv:2502.02015}
}
read the original abstract

People's collectively shared beliefs can have significant social implications, including on democratic processes and policies. Unfortunately, as people interact with peers to form and update their beliefs, various cognitive and social biases can hinder their collective wisdom. In this paper, we probe whether and how the psychological construct of intellectual humility can modulate collective wisdom in a networked interaction setting. Through agent-based modeling and data-calibrated simulations, we provide a proof of concept demonstrating that intellectual humility can foster more accurate estimations while mitigating polarization in social networks. We investigate the mechanisms behind the performance improvements and confirm robustness across task settings and network structures. Our work can guide intervention designs to capitalize on the promises of intellectual humility in boosting collective wisdom in social networks.

Figures

Figures reproduced from arXiv: 2502.02015 by the authors.

Figure 1
Figure 1. (A) Normalized estimation errors and (B) Polarization (average pairwise distance) significantly reduce in treatment networks, while (C) Revision Coefficients increase. (D) The partial correlation between ˜ei,q and △x˜i,q (adjusted for si,q) improves in the treatment condition. Shaded regions and whiskers denote 95% C.I. *** P < 0.001, ** P < 0.01. Notations for the Diversity Prediction Theorem. The Di￾versity Predic… view at source ↗

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Works this paper leans on

63 extracted references · 61 canonical work pages

  1. [1]

    write newline

    " write newline " cite write " FUNCTION editor.postfix editor num.names #1 > "( )" "( )" if FUNCTION editor.trans.postfix editor num.names #1 > "( )" "( )" if FUNCTION trans.postfix translator num.names #1 > "( )" "( )" if FUNCTION authors.editors.reflist.apa5 'field := 'dot := field num.names 'numnames := numnames 'format.num.names := format.num.names na...

  2. [2]

    , Noriega-Campero, A

    almaatouq2020adaptive APACrefauthors Almaatouq, A. , Noriega-Campero, A. , Alotaibi, A. , Krafft, P. , Moussaid, M. \ Pentland, A. APACrefauthors \ 2020 . Adaptive social networks promote the wisdom of crowds Adaptive social networks promote the wisdom of crowds . Proceedings of the National Academy of Sciences 117 21 11379--11386

    work page 2020

  3. [3]

    , Rahimian, M A

    almaatouq2022distribution APACrefauthors Almaatouq, A. , Rahimian, M A. , Burton, J W. \ Alhajri, A. APACrefauthors \ 2022 . The distribution of initial estimates moderates the effect of social influence on the wisdom of the crowd The distribution of initial estimates moderates the effect of social influence on the wisdom of the crowd . Scientific Reports...

    work page 2022

  4. [4]

    \ Albert, R

    barabasi1999emergence APACrefauthors Barab \'a si, A L. \ Albert, R. APACrefauthors \ 1999 . Emergence of scaling in random networks Emergence of scaling in random networks . Science 286 5439 509--512

    work page 1999

  5. [5]

    APACrefauthors \ 2002

    bartels2002beyond APACrefauthors Bartels, L M. APACrefauthors \ 2002 . Beyond the running tally: Partisan bias in political perceptions Beyond the running tally: Partisan bias in political perceptions . Political Behavior 24 117--150

    work page 2002

  6. [6]

    , Brackbill, D

    becker2017network APACrefauthors Becker, J. , Brackbill, D. \ Centola, D. APACrefauthors \ 2017 . Network dynamics of social influence in the wisdom of crowds Network dynamics of social influence in the wisdom of crowds . Proceedings of the National Academy of Sciences 114 26 E5070--E5076

    work page 2017

  7. [7]

    , Porter, E

    becker2019wisdom APACrefauthors Becker, J. , Porter, E. \ Centola, D. APACrefauthors \ 2019 . The wisdom of partisan crowds The wisdom of partisan crowds . Proceedings of the National Academy of Sciences 116 22 10717--10722

    work page 2019

  8. [8]

    , Costello, T H

    bowes2021looking APACrefauthors Bowes, S M. , Costello, T H. , Ma, W. \ Lilienfeld, S O. APACrefauthors \ 2021 . Looking under the tinfoil hat: Clarifying the personological and psychopathological correlates of conspiracy beliefs Looking under the tinfoil hat: Clarifying the personological and psychopathological correlates of conspiracy beliefs . Journal ...

    work page 2021

Show all 63 references
  1. [9]

    , Gerber, A S

    bullock2013partisan APACrefauthors Bullock, J G. , Gerber, A S. , Hill, S J. \ Huber, G A. APACrefauthors \ 2013 May . Partisan Bias in Factual Beliefs about Politics Partisan bias in factual beliefs about politics \ Working Paper\ \ 19080 . Massachusetts Avenue Cambridge, MA ...

    2013 doi

  2. [10]

    , Almaatouq, A

    burton2021rewiring APACrefauthors Burton, J W. , Almaatouq, A. , Rahimian, M A. \ Hahn, U. APACrefauthors \ 2021 . Rewiring the wisdom of the crowd Rewiring the wisdom of the crowd . Proceedings of the Annual Meeting of the Cognitive Science Society 43 . Retrieved from https:/...

    2021

  3. [11]

    APACrefauthors \ 2016

    church2016doxastic APACrefauthors Church, I M. APACrefauthors \ 2016 . The doxastic account of intellectual humility The doxastic account of intellectual humility . Logos & Episteme 7 4 413--433

    2016

  4. [12]

    APACrefauthors \ 1974

    degroot1974reaching APACrefauthors DeGroot, M H. APACrefauthors \ 1974 . Reaching a consensus Reaching a consensus . Journal of the American Statistical Association 69 345 118--121

    1974

  5. [13]

    APACrefauthors \ 2022

    esteve2022homophily APACrefauthors Esteve-Del-Valle, M. APACrefauthors \ 2022 . Homophily and polarization in T witter political networks: A cross-country analysis Homophily and polarization in T witter political networks: A cross-country analysis . Media and Communication 10 2 81--92

    2022

  6. [14]

    \ Pilditch, T

    franken2021 APACrefauthors Fr\" a nken, J P. \ Pilditch, T. APACrefauthors \ 2021 . Cascades Across Networks Are Sufficient for the Formation of Echo Chambers: An Agent-Based Model Cascades across networks are sufficient for the formation of echo chambers: An agent-based model...

    2021 doi

  7. [15]

    APACrefauthors \ 1907

    galton1907vox APACrefauthors Galton, F. APACrefauthors \ 1907 . Vox Populi (The Wisdom of Crowds) Vox populi (the wisdom of crowds) . Nature 75 7 450--451

    1907

  8. [16]

    APACrefauthors \ 2012

    gawronski2012back APACrefauthors Gawronski, B. APACrefauthors \ 2012 . Back to the future of dissonance theory: Cognitive consistency as a core motive Back to the future of dissonance theory: Cognitive consistency as a core motive . Social Cognition 30 6 652--668

    2012

  9. [17]

    \ Jackson, M O

    golub2012homophily APACrefauthors Golub, B. \ Jackson, M O. APACrefauthors \ 2012 . How homophily affects the speed of learning and best-response dynamics How homophily affects the speed of learning and best-response dynamics . The Quarterly Journal of Economics 127 3 1287--1338

    2012

  10. [18]

    \ Newell, J P

    gounaridis2024social APACrefauthors Gounaridis, D. \ Newell, J P. APACrefauthors \ 2024 . The social anatomy of climate change denial in the U nited S tates The social anatomy of climate change denial in the U nited S tates . Scientific Reports 14 1 2097

    2024

  11. [19]

    , Becker, J

    guilbeault2018social APACrefauthors Guilbeault, D. , Becker, J. \ Centola, D. APACrefauthors \ 2018 . Social learning and partisan bias in the interpretation of climate trends Social learning and partisan bias in the interpretation of climate trends . Proceedings of the Nation...

    2018

  12. [20]

    , Mellers, B A

    gurccay2015power APACrefauthors G \"u r c ay, B. , Mellers, B A. \ Baron, J. APACrefauthors \ 2015 . The power of social influence on estimation accuracy The power of social influence on estimation accuracy . Journal of Behavioral Decision Making 28 3 250--261

    2015

  13. [21]

    , Hansen, J U

    hahn2020truth APACrefauthors Hahn, U. , Hansen, J U. \ Olsson, E J. APACrefauthors \ 2020 . Truth tracking performance of social networks: How connectivity and clustering can make groups less competent Truth tracking performance of social networks: How connectivity and cluster...

    2020

  14. [22]

    , von Sydow, M

    hahn2019communication APACrefauthors Hahn, U. , von Sydow, M. \ Merdes, C. APACrefauthors \ 2019 . How communication can make voters choose less well How communication can make voters choose less well . Topics in Cognitive Science 11 1 194--206

    2019

  15. [23]

    , Maynard, J L

    homer2013complex APACrefauthors Homer-Dixon, T. , Maynard, J L. , Mildenberger, M. , Milkoreit, M. , Mock, S J. , Quilley, S. Thagard, P. APACrefauthors \ 2013 . A complex systems approach to the study of ideology: Cognitive-affective structures and the dynamics of belief syst...

    2013

  16. [24]

    , Hoyle, R H

    hopkin2014intellectual APACrefauthors Hopkin, C R. , Hoyle, R H. \ Toner, K. APACrefauthors \ 2014 . Intellectual humility and reactions to opinions about religious beliefs Intellectual humility and reactions to opinions about religious beliefs . Journal of Psychology and Theo...

    2014

  17. [25]

    , Kim, H r

    jayles2017social APACrefauthors Jayles, B. , Kim, H r. , Escobedo, R. , Cezera, S. , Blanchet, A. , Kameda, T. Theraulaz, G. APACrefauthors \ 2017 . How social information can improve estimation accuracy in human groups How social information can improve estimation accuracy in...

    2017

  18. [26]

    APACrefauthors \ 2013

    kahan2013ideology APACrefauthors Kahan, D M. APACrefauthors \ 2013 . Ideology, motivated reasoning, and cognitive reflection Ideology, motivated reasoning, and cognitive reflection . Judgment and Decision Making 8 4 407--424

    2013

  19. [27]

    \ Cohrs, J C

    knochelmann2024effects APACrefauthors Kn \"o chelmann, L. \ Cohrs, J C. APACrefauthors \ 2024 . Effects of intellectual humility in the context of affective polarization: Approaching and avoiding others in controversial political discussions Effects of intellectual humility in...

    2024

  20. [28]

    , Lichtenstein, S

    koriat1980reasons APACrefauthors Koriat, A. , Lichtenstein, S. \ Fischhoff, B. APACrefauthors \ 1980 . Reasons for confidence Reasons for confidence . Journal of Experimental Psychology: Human Learning and Memory 6 2 107

    1980

  21. [29]

    APACrefauthors \ 2017

    krumrei2017intellectual APACrefauthors Krumrei-Mancuso, E J. APACrefauthors \ 2017 . Intellectual humility and prosocial values: Direct and mediated effects Intellectual humility and prosocial values: Direct and mediated effects . The Journal of Positive Psychology 12 1 13--28

    2017

  22. [30]

    \ Newman, B

    krumrei2020intellectual APACrefauthors Krumrei-Mancuso, E J. \ Newman, B. APACrefauthors \ 2020 . Intellectual humility in the sociopolitical domain Intellectual humility in the sociopolitical domain . Self and Identity 19 8 989--1016

    2020

  23. [31]

    \ Newman, B

    krumrei2021sociopolitical APACrefauthors Krumrei-Mancuso, E J. \ Newman, B. APACrefauthors \ 2021 . Sociopolitical intellectual humility as a predictor of political attitudes and behavioral intentions Sociopolitical intellectual humility as a predictor of political attitudes a...

    2021

  24. [32]

    , P \"a rnamets, P

    krumrei2024toward APACrefauthors Krumrei-Mancuso, E J. , P \"a rnamets, P. , Bland, S. , Astola, M. , Cichocka, A. , de Ridder, J. others APACrefauthors \ 2024 . Toward an understanding of collective intellectual humility Toward an understanding of collective intellectual humi...

    2024

  25. [33]

    \ Rouse, S V

    krumrei2016development APACrefauthors Krumrei-Mancuso, E J. \ Rouse, S V. APACrefauthors \ 2016 . The development and validation of the comprehensive intellectual humility scale The development and validation of the comprehensive intellectual humility scale . Journal of Person...

    2016

  26. [34]

    , Weinstock, M

    kuhn1994well APACrefauthors Kuhn, D. , Weinstock, M. \ Flaton, R. APACrefauthors \ 1994 . How well do jurors reason? C ompetence dimensions of individual variation in a juror reasoning task How well do jurors reason? C ompetence dimensions of individual variation in a juror re...

    1994

  27. [35]

    APACrefauthors \ 1990

    kunda1990case APACrefauthors Kunda, Z. APACrefauthors \ 1990 . The case for motivated reasoning The case for motivated reasoning . Psychological Bulletin 108 3 480

    1990

  28. [36]

    APACrefauthors \ 2018

    leary2018psychology APACrefauthors Leary, M R. APACrefauthors \ 2018 . The psychology of intellectual humility The psychology of intellectual humility . John Templeton Foundation 3

    2018

  29. [37]

    APACrefauthors \ 2022

    leary2022intellectual APACrefauthors Leary, M R. APACrefauthors \ 2022 . Intellectual humility as a route to more accurate knowledge, better decisions, and less conflict Intellectual humility as a route to more accurate knowledge, better decisions, and less conflict . American...

    2022

  30. [38]

    , Diebels, K J

    leary2017cognitive APACrefauthors Leary, M R. , Diebels, K J. , Davisson, E K. , Jongman-Sereno, K P. , Isherwood, J C. , Raimi, K T. Hoyle, R H. APACrefauthors \ 2017 . Cognitive and interpersonal features of intellectual humility Cognitive and interpersonal features of intel...

    2017

  31. [39]

    , Rauhut, H

    lorenz2011social APACrefauthors Lorenz, J. , Rauhut, H. , Schweitzer, F. \ Helbing, D. APACrefauthors \ 2011 . How social influence can undermine the wisdom of crowd effect How social influence can undermine the wisdom of crowd effect . Proceedings of the National Academy of S...

    2011

  32. [40]

    \ de Polavieja, G G

    madirolas2015improving APACrefauthors Madirolas, G. \ de Polavieja, G G. APACrefauthors \ 2015 . Improving collective estimations using resistance to social influence Improving collective estimations using resistance to social influence . PLoS Computational Biology 11 11 e1004594

    2015

  33. [41]

    , Bailey, R M

    madsen2018large APACrefauthors Madsen, J K. , Bailey, R M. \ Pilditch, T D. APACrefauthors \ 2018 . Large networks of rational agents form persistent echo chambers Large networks of rational agents form persistent echo chambers . Scientific Reports 8 1 12391

    2018

  34. [42]

    , Smith-Lovin, L

    mcpherson2001birds APACrefauthors McPherson, M. , Smith-Lovin, L. \ Cook, J M. APACrefauthors \ 2001 . Birds of a Feather: Homophily in Social Networks Birds of a feather: Homophily in social networks . Annual Review of Sociology 27 1 415--444

    2001

  35. [43]

    , Aral, S

    muchnik2013social APACrefauthors Muchnik, L. , Aral, S. \ Taylor, S J. APACrefauthors \ 2013 . Social influence bias: A randomized experiment Social influence bias: A randomized experiment . Science 341 6146 647--651

    2013

  36. [44]

    APACrefauthors \ 2014

    nash2014curious APACrefauthors Nash, U W. APACrefauthors \ 2014 . The curious anomaly of skewed judgment distributions and systematic error in the wisdom of crowds The curious anomaly of skewed judgment distributions and systematic error in the wisdom of crowds . PloS One 9 11 e112386

    2014

  37. [45]

    APACrefauthors \ 2013

    olsson2013bayesian APACrefauthors Olsson, E J. APACrefauthors \ 2013 . A Bayesian Simulation Model of Group Deliberation and Polarization A bayesian simulation model of group deliberation and polarization . F. Zenker\ ( ), Bayesian A rgumentation: The practical side of probabi...

    2013

  38. [46]

    APACrefauthors \ 2008

    page2008difference APACrefauthors Page, S E. APACrefauthors \ 2008 . The difference: How the power of diversity creates better groups, firms, schools, and societies. The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton, NJ,...

    2008

  39. [47]

    \ Rand, D G

    pennycook2019fighting APACrefauthors Pennycook, G. \ Rand, D G. APACrefauthors \ 2019 . Fighting misinformation on social media using crowdsourced judgments of news source quality Fighting misinformation on social media using crowdsourced judgments of news source quality . Pro...

    2019

  40. [48]

    , Elnakouri, A

    porter2022predictors APACrefauthors Porter, T. , Elnakouri, A. , Meyers, E A. , Shibayama, T. , Jayawickreme, E. \ Grossmann, I. APACrefauthors \ 2022 . Predictors and consequences of intellectual humility Predictors and consequences of intellectual humility . Nature Reviews P...

    2022

  41. [49]

    \ Schumann, K

    porter2018intellectual APACrefauthors Porter, T. \ Schumann, K. APACrefauthors \ 2018 . Intellectual humility and openness to the opposing view Intellectual humility and openness to the opposing view . Self and Identity 17 2 139--162

    2018

  42. [50]

    , Pate, N

    proma2024exploring APACrefauthors Proma, A M. , Pate, N. , Baten, R A. , Chen, S. , Druckman, J. , Ghoshal, G. \ Hoque, E. APACrefauthors \ 2024 . Exploring the Role of Randomization on Belief Rigidity in Online Social Networks Exploring the role of randomization on belief rig...

    2024 arXiv

  43. [51]

    , Borsboom, D

    scheffer2022belief APACrefauthors Scheffer, M. , Borsboom, D. , Nieuwenhuis, S. \ Westley, F. APACrefauthors \ 2022 . Belief traps: Tackling the inertia of harmful beliefs Belief traps: Tackling the inertia of harmful beliefs . Proceedings of the National Academy of Sciences 1...

    2022

  44. [52]

    , Westley, F

    scheffer2003slow APACrefauthors Scheffer, M. , Westley, F. \ Brock, W. APACrefauthors \ 2003 . Slow response of societies to new problems: Causes and costs Slow response of societies to new problems: Causes and costs . Ecosystems 6 493--502

    2003

  45. [53]

    \ Ayduk, O N

    sgambati2022intellectual APACrefauthors Sgambati, T J. \ Ayduk, O N. APACrefauthors \ 2022 . Is intellectual humility an antidote for our polarized nation? Is intellectual humility an antidote for our polarized nation? American Journal of Health Promotion 36 8 1411--1414

    2022

  46. [54]

    APACrefauthors \ 2023

    smith2023you APACrefauthors Smith, G. APACrefauthors \ 2023 . You Know You're Right: How Intellectual Humility Decreases Political Hostility You know you're right: How intellectual humility decreases political hostility . Political Psychology 44 6 1319--1335

    2023

  47. [55]

    \ Wormald, N C

    steger1999generating APACrefauthors Steger, A. \ Wormald, N C. APACrefauthors \ 1999 . Generating random regular graphs quickly Generating random regular graphs quickly . Combinatorics, Probability and Computing 8 4 377--396

    1999

  48. [56]

    APACrefauthors \ 2006

    sunstein2006infotopia APACrefauthors Sunstein, C R. APACrefauthors \ 2006 . Infotopia: How Many Minds Produce Knowledge Infotopia: How many minds produce knowledge . Oxford University Press

    2006

  49. [57]

    APACrefauthors \ 2004

    surowiecki2004wisdom APACrefauthors Surowiecki, J. APACrefauthors \ 2004 . The Wisdom of Crowds: Why the Many are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations The wisdom of crowds: Why the many are smarter than the few and h...

    2004

  50. [58]

    \ Lodge, M

    taber2006motivated APACrefauthors Taber, C S. \ Lodge, M. APACrefauthors \ 2006 . Motivated skepticism in the evaluation of political beliefs Motivated skepticism in the evaluation of political beliefs . American Journal of Political Science 50 3 755--769

    2006

  51. [59]

    APACrefauthors \ 2011

    vorobej2011distant APACrefauthors Vorobej, M. APACrefauthors \ 2011 . Distant peers Distant peers . Metaphilosophy 42 5 708--722

    2011

  52. [60]

    \ Strogatz, S H

    watts1998collective APACrefauthors Watts, D J. \ Strogatz, S H. APACrefauthors \ 1998 . Collective dynamics of `small-world' networks Collective dynamics of `small-world' networks . Nature 393 6684 440--442

    1998

  53. [61]

    , Battaly, H

    whitcomb2017intellectual APACrefauthors Whitcomb, D. , Battaly, H. , Baehr, J. \ Howard-Snyder, D. APACrefauthors \ 2017 . Intellectual humility Intellectual humility . Philosophy and Phenomenological Research 94 3 509--539

    2017

  54. [62]

    , Krause, J

    wolf2015collective APACrefauthors Wolf, M. , Krause, J. , Carney, P A. , Bogart, A. \ Kurvers, R H. APACrefauthors \ 2015 . Collective intelligence meets medical decision-making: The collective outperforms the best radiologist Collective intelligence meets medical decision-mak...

    2015

  55. [63]

    , Zmigrod, S

    zmigrod2019psychological APACrefauthors Zmigrod, L. , Zmigrod, S. , Rentfrow, P J. \ Robbins, T W. APACrefauthors \ 2019 . The psychological roots of intellectual humility: The role of intelligence and cognitive flexibility The psychological roots of intellectual humility: The...

    2019

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.