Warning: file_put_contents(/opt/frankenphp/design.onmedianet.com/storage/proxy/cache/663ede08f0bfb975a0d96a0863d14768.html): Failed to open stream: No space left on device in /opt/frankenphp/design.onmedianet.com/app/src/Arsae/CacheManager.php on line 36

Warning: http_response_code(): Cannot set response code - headers already sent (output started at /opt/frankenphp/design.onmedianet.com/app/src/Arsae/CacheManager.php:36) in /opt/frankenphp/design.onmedianet.com/app/src/Models/Response.php on line 17

Warning: Cannot modify header information - headers already sent by (output started at /opt/frankenphp/design.onmedianet.com/app/src/Arsae/CacheManager.php:36) in /opt/frankenphp/design.onmedianet.com/app/src/Models/Response.php on line 20
The Strengths and Weaknesses of Crowds to Address Global Problems - Stephen B. Broomell, Clintin P. Davis-Stober, 2024

Restricted access

Research article

First published online July 10, 2023

Request permissions

The Strengths and Weaknesses of Crowds to Address Global Problems

Stephen B. Broomell https://orcid.org/0000-0001-6983-2591 [email protected] and Clintin P. Davis-StoberView all authors and affiliations

Volume 19, Issue 2

https://doi.org/10.1177/17456916231179152

Abstract

Global climate change, the COVID-19 pandemic, and the spread of misinformation on social media are just a handful of highly consequential problems affecting society. We argue that the rough contours of many societal problems can be framed within a “wisdom of crowds” perspective. Such a framing allows researchers to recast complex problems within a simple conceptual framework and leverage known results on crowd wisdom. To this end, we present a simple “toy” model of the strengths and weaknesses of crowd wisdom that easily maps to many societal problems. Our model treats the judgments of individuals as random draws from a distribution intended to represent a heterogeneous population. We use a weighted mean of these individuals to represent the crowd’s collective judgment. Using this setup, we show that subgroups have the potential to produce substantively different judgments and we investigate their effect on a crowd’s ability to generate accurate judgments about societal problems. We argue that future work on societal problems can benefit from more sophisticated, domain-specific theory and models based on the wisdom of crowds.

Get full access to this article

View all access and purchase options for this article.

Transparency

Action Editor: Mirta Galesic

Editor: Interim Editorial Panel

References

Atanasov P., Rescober P., Stone E., Swift S. A., Servan-Schreiber E., Tetlock P., Ungar L., Mellers B. (2017). Distilling the wisdom of crowds: Prediction markets vs. prediction polls. Management Science, 63(3), 691–706.

Babcock M., Cox R. A. V., Kumar S. (2019). Diffusion of pro-and anti-false information tweets: The Black Panther movie case. Computational and Mathematical Organization Theory, 25(1), 72–84.

Bak-Coleman J. B., Alfano M., Barfuss W., Bergstrom C. T., Centeno M. A., Couzin I. D., Donges J. F., Galesic M., Gersick A. S., Jacquet J., Kao A. B., Moran R. E., Romanczuk P., Rubenstein D. I., Tombak K. J., Van Bavel J. J., Weber E. U. (2021). Stewardship of global collective behavior. Proceedings of the National Academy of Sciences, USA, 118(27), Article e2025764118. https://doi.org/10.1073/pnas.2025764118

Bergstrom C. T., Bak-Coleman J. B. (2019). Gerrymandering in social networks. Nature, 573, 40–41.

Bikhchandani S. (2000). Herd behavior in financial markets: A review (Working Paper WP/00/48.) IMF. https://www.imf.org/external/pubs/ft/wp/2000/wp0048.pdf

Broomell S. B. (2020). Global–local incompatibility: The misperception of reliability in judgment regarding global variables. Cognitive Science, 44(4), Article e12831. https://doi.org/10.1111/cogs.12831

Broomell S. B., Chapman G. B. (2021). Looking beyond cognition for risky decision making: Covid-19, the environment, and behavior. Journal of Applied Research in Memory and Cognition, 10, 512–516.

Broomell S. B., Kane P. B. (2021). Perceiving a pandemic: Global–local incompatibility and covid-19 superspreading events. Decision, 8(4), 227–236.

Brunswik E. (1956). Perception and the representative design of psychological experiments. University of California Press.

Budescu D. V., Chen E. (2015). Identifying expertise to extract the wisdom of crowds. Management Science, 61(2), 267–280.

Condorcet M. (1785). Essay on the application of analysis to the probability of majority decisions. Imprimerie Royale.

Daniel K., Hirshleifer D., Teoh S. H. (2002). Investor psychology in capital markets: Evidence and policy implications. Journal of Monetary Economics, 49(1), 139–209.

Davis-Stober C. P., Budescu D. V., Broomell S. B., Dana J. (2015). The composition of optimally wise crowds. Decision Analysis, 12(3), 130–143.

Davis-Stober C. P., Budescu D. V., Dana J., Broomell S. B. (2014). When is a crowd wise? Decision, 1(2), 79–101.

De Oliveira S., Nisbett R. E. (2018). Demographically diverse crowds are typically not much wiser than homogeneous crowds. Proceedings of the National Academy of Sciences, USA, 115(9), 2066–2071.

Epp D. A. (2017). Public policy and the wisdom of crowds. Cognitive Systems Research, 43, 53–61.

Estes W. K. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53(2), 134–140.

Fiedler K. (2000). Beware of samples! A cognitive-ecological sampling approach to judgment biases. Psychological Review, 107(4), 659–676.

Galesic M., Olsson H., Rieskamp J. (2018). A sampling model of social judgment. Psychological Review, 125(3), 363–390.

Goel S., Anderson A., Hofman J., Watts D. J. (2016). The structural virality of online diffusion. Management Science, 62(1), 180–196.

Goldstein D. G., McAfee R. P., Suri S. (2014). The wisdom of smaller, smarter crowds. In Proceedings of the fifteenth ACM conference on Economics and computation (pp. 471–488). Association for Computing Machinery.

Hong L., Page S. E. (2004). Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proceedings of the National Academy of Sciences, USA, 101(46), 16385–16389.

Huang S., Broomell S. B., Golman R. (2022). A hypothesis test algorithm for determining when weighting individual judgments reliably improves collective accuracy or just adds noise. Decision. Advance online publication. https://doi.org/10.1037/dec0000187

IPPC Working Group I. (2021). Climate change 2021: The physical science basis (Vol. 2). Cambridge University Press.

Kao A. B., Berdahl A. M., Hartnett A. T., Lutz M. J., Bak-Coleman J. B., Ioannou C. C., Giam X., Couzin I. D. (2018). Counteracting estimation bias and social influence to improve the wisdom of crowds. Journal of the Royal Society Interface, 15(141), Article 20180130. https://doi.org/10.1098/rsif.2018.0130

Kao A. B., Couzin I. D. (2014). Decision accuracy in complex environments is often maximized by small group sizes. Proceedings of the Royal Society B: Biological Sciences, 281(1784), Article 20133305. https://doi.org/10.1098/rspb.2013.3305

Kao A. B., Couzin I. D. (2019). Modular structure within groups causes information loss but can improve decision accuracy. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1774), Article 20180378. https://doi.org/10.1098/rstb.2018.0378

Kupferschmidt K. (2020, May 19). Why do some covid-19 patients infect many others, whereas most don’t spread the virus at all. Science. https://www.science.org/content/article/why-do-some-covid-19-patients-infect-many-others-whereas-most-don-t-spread-virus-all#:~:text=Some%20people%20shed%20far%20more,than%20others%20when%20they%20talk. 10.

Lamberson P., Page S. E. (2012). Optimal forecasting groups. Management Science, 58(4), 805–810.

Lee T. M., Markowitz E. M., Howe P. D., Ko C.-Y., Leiserowitz A. A. (2015). Predictors of public climate change awareness and risk perception around the world. Nature Climate Change, 5(11), 1014–1020.

Li Y., Johnson E. J., Zaval L. (2011). Local warming: Daily temperature change influences belief in global warming. Psychological Science, 22(4), 454–459.

Lindsey R., Dahlman L. (2023). Climate change: Global temperature. https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature

Lorenz J., Rauhut H., Schweitzer F., Helbing D. (2011). How social influence can undermine the wisdom of crowd effect. Proceedings of the National Academy of Sciences, USA, 108(22), 9020–9025.

Merkle E. C., Saw G., Davis-Stober C. (2020). Beating the average forecast: Regularization based on forecaster attributes. Journal of Mathematical Psychology, 98, Article 102419. https://doi.org/10.1016/j.jmp.2020.102419

Noar S. M., Austin L. (2020). (Mis)communicating about covid-19: Insights from health and crisis communication. Health Communication, 35(14), 1735–1739.

Page S. (2017). The diversity bonus. Princeton University Press.

Satopää V. A., Salikhov M., Tetlock P. E., Mellers B. (2021). Bias, information, noise: The bin model of forecasting. Management Science, 67(12), 7599–7618.

Simon H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138.

Stewart A. J., Mosleh M., Diakonova M., Arechar A. A., Rand D. G., Plotkin J. B. (2019). Information gerrymandering and undemocratic decisions. Nature, 573(7772), 117–121.

Sugerman E. R., Li Y., Johnson E. J. (2021). Local warming is real: A meta-analysis of the effect of recent temperature on climate change beliefs. Current Opinion in Behavioral Sciences, 42, 121–126.

Surowiecki J. (2005). The wisdom of crowds. Anchor.

Tucker J. A., Guess A., Barberá P., Vaccari C., Siegel A., Sanovich S., Stukal D., Nyhan B. (2018, March 19). Social media, political polarization, and political disinformation: A review of the scientific literature. SSRN. http://dx.doi.org/10.2139/ssrn.3144139

Ungar L., Mellers B., Satopää V., Tetlock P., Baron J. (2012). The good judgment project: A large scale test of different methods of combining expert predictions (Papers from the 2012 AAAI Fall Symposium). Association for the Advancement of Artificial Intelligence. https://aaai.org/papers/05570-the-good-judgment-project-a-large-scale-test-of-different-methods-of-combining-expert-predictions/

Weber E. U. (2016). What shapes perceptions of climate change? New research since 2010. Wiley Interdisciplinary Reviews: Climate Change, 7(1), 125–134.

Whitmarsh L., Capstick S. (2018). Perceptions of climate change. In Clayton S., Manning C. (Eds.), Psychology and climate change (pp. 13–33). Elsevier.

Zaval L., Keenan E. A., Johnson E. J., Weber E. U. (2014). How warm days increase belief in global warming. Nature Climate Change, 4(2), 143–147.

View full text|Download PDF

You currently have no access to this content. Visit the access options page to authenticate.