Self-serving incentives impair collective decisions by increasing conformity
Autoři:
Sepideh Bazazi aff001; Jorina von Zimmermann aff002; Bahador Bahrami aff003; Daniel Richardson aff002
Působiště autorů:
Independent researcher, London, United Kingdom
aff001; Department of Experimental Psychology, University College London, London, United Kingdom
aff002; Institute of Cognitive Neuroscience, University College London, London, United Kingdom
aff003
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0224725
Souhrn
The average judgment of large numbers of people has been found to be consistently better than the best individual response. But what motivates individuals when they make collective decisions? While it is a popular belief that individual incentives promote out-of-the-box thinking and diverse solutions, the exact role of motivation and reward in collective intelligence remains unclear. Here we examined collective intelligence in an interactive group estimation task where participants were rewarded for their individual or group’s performance. In addition to examining individual versus collective incentive structures, we controlled whether participants could see social information about the others’ responses. We found that knowledge about others’ responses reduced the wisdom of the crowd and, crucially, this effect depended on how people were rewarded. When rewarded for the accuracy of their individual responses, participants converged to the group mean, increasing social conformity, reducing diversity and thereby diminishing their group wisdom. When rewarded for their collective performance, diversity of opinions and the group wisdom increased. We conclude that the intuitive association between individual incentives and individualist opinion needs revising.
Klíčová slova:
Bayesian method – Behavior – Decision making – Experimental design – Intelligence – Motivation – Social influence – Human intelligence
Zdroje
1. Krause J, Ruxton G. D., Krause S (2009) Swarm intelligence in animals and humans. Trends in Ecology and Evolution 25: 28–34. doi: 10.1016/j.tree.2009.06.016 19735961
2. Surowiecki J (2004) The Wisdom of the Crowds: Why the Many are Smarter than the Few: Knopf Doubleday Publishing Group. 336 p.
3. Arrow KJ, Forsythe R, Gorham M, Hahn R, Hanson R, Ledyard J, et al. (2008) The Promise of Prediction Markets. Science 320: 877–878. doi: 10.1126/science.1157679 18487176
4. Wolfers J, Zitzewitz E (2004) Prediction Markets. Journal of Economic Perspectives 18: 107–126.
5. Galton F (1907) Vox populi. Nature 75: 450–451.
6. Page SE (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton: Princeton University Press.
7. King AJ, Cheng L, Starke SD, Myatt JP (2011) Is the true ‘wisdom of the crowd’ to copy successful individuals? Biology Letters 8: 197–200. doi: 10.1098/rsbl.2011.0795 21920956
8. Lorge I, Fox D, Davitz J, Brenner M (1958) A survey of studies contrasting the quality of group performance and individual performance. Psychological Bulletin 55: 337–372. doi: 10.1037/h0042344 13602018
9. Yaniv I, Milyavsky M (2007) Using advice from multiple sources to revise and improve judgments. Organizational Behavior and Human Decision Processes 103: 104–120.
10. Kaplan C, A. (2001) Collective Intelligence: A new approach to stock price forecasting. Proceedings of the 2001 IEEE Systems, Man, and Cybernetics Conference: IEEE.
11. Malone TW, Klein M (2007) Harnessing Collective Intelligence to Address Global Climate Change. Innovations 2: 15–26.
12. Lih A (2009) The Wikipedia Evolution: How a Bunch of Nobodies Created the World’s Greatest Encyclopedia. New York: NY: Hyperion.
13. Janis IL (1972) Victims of Groupthink: a Psychological Study of Foreign-Policy Decisions and Fiascoes.: Boston: Houghton Mifflin.
14. Park J, Konana PC, Gu B, Kumar A, Raghunathan R (2013) Information valuation and confirmation bias in virtual communities: evidence from stock message boards. Information Systems Research 24: 1050–1067.
15. Barsade SG (2002) The Ripple Effect: Emotional Contagion and its Influence on Group Behavior. Administrative Science Quarterly 47: 644–675.
16. Mackay C (1852) Extraordinary Popular Delusions and the Madness of Crowds. London, UK.
17. Asch SE (1955) Opinions and Social Pressure. Scientific American 193: 31–35.
18. Christakis N, Fowler J (2009) Connected: The Amazing Power of Social Networks and How They Shape Our Lives: Little, Brown and Company.
19. Raafat R. M., Charter N, Frith C (2009) Herding in humans. Trends in Cognitive Sciences 13: 420–428. doi: 10.1016/j.tics.2009.08.002 19748818
20. Farrell S (2011) Social influence benefits the wisdom of individuals in the crowd. Proceedings of the National Academy of Sciences 108: E625.
21. Lichtendahl KC, Grushka-Cockayne Y, Pfeifer PE (2013) The wisdom of competitive crowds. Darden Business School Working Paper No 1926330.
22. Pfeifer PE (2015) The promise of pick-the-winners contests for producing crowd probability forecasts. Theory and Decision: 1–24.
23. Pfeifer PE, Grushka-Cockayne Y, Lichtendahl KC (2014) The promise of prediction contests. The American Statistician 68: 264–270.
24. Hong L, Page SE, Riolo M (2012) Incentives, information, and emergent collective accuracy. Managerial and Decision Economics 33: 323–334.
25. Mann RP, Helbing D (2017) Optimal incentives for collective intelligence. Proceedings of the National Academy of Sciences published ahead of print May 1, 2017.
26. Sumpter DJT (2006) The principles of collective animal behaviour. Philosophical Transactions of the Royal Society of London: Series B 361: 5–22.
27. Smith A (1759) The Theory of Moral Sentiments: London: A. Millar.
28. Gale D, Kariv S (2003) Bayesian Learning in Social Networks. Games and Economic Behavior 45: 329–346.
29. von Zimmermann J, Richardson D (in prep) The Hive—https://thehive.sc/welcome.
30. Kruschke JK (2010) Bayesian data analysis. Wiley Interdisciplinary Reviews: Cognitive Science 1: 658–676. doi: 10.1002/wcs.72 26271651
31. Wagenmakers EJ, Wetzels R, Borsboom D, van der Maas HLJ (2011) Why psychologists must change the way they analyze their data: The case of psi: The case of psi: Comment on Bem. Journal of Personality and Social Psychology 100: 426–432. doi: 10.1037/a0022790 21280965
32. R-Core-Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.
33. Stan-Development-Team (2016) rstanarm: Bayesian applied regression modeling via Stan. R package version 2.13.1. http://mc-stan.org/.
34. Makowski (2018) The psycho Package: an Efficient and Publishing-Oriented Workflow for Psychological Science. Journal of Open Source Software 3: 470.
35. 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 108: 9020–9025.
36. Bentley RA, Brock WA, Caiado CCS, O'Brien MJ (2016) Evaluating reproductive decisions as discrete choices under social influence. Philosophical Transactions of The Royal Society B: Biological Sciences 371: 20150154.
37. Danchin E, Giraldeau L-A, Valone T. J., Wagner R. H. (2004) Public Information: From Nosy Neighbors to Cultural Evolution. Science 305: 487–491. doi: 10.1126/science.1098254 15273386
38. Granovskiy B, Gold JM, Sumpter DJT, Goldstone RL (2015) Integration of Social Information by Human Groups. Topics in Cognitive Science 7: 469–493. doi: 10.1111/tops.12150 26189568
39. Wisdom TN, Song X, Goldstone RL (2013) Social Learning Strategies in Networked Groups. Cognitive Science 37: 1383–1425. doi: 10.1111/cogs.12052 23845020
40. Bahrami B, Olsen K, Latham P, Roepstorff A, Rees G, Frith C (2010) Optimally interacting minds. Science 329: 1081–1085. doi: 10.1126/science.1185718 20798320
41. Dong W, Lepri B, Pentland A. Modeling the Co-evolution of Behaviors and Social Relationships Using Mobile Phone Data; 2011; Beijing, China. pp. 134–143.
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