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Payoff-based learning best explains the rate of decline in cooperation across 237 public-goods games


What motivates human behaviour in social dilemmas? The results of public goods games are commonly interpreted as showing that humans are altruistically motivated to benefit others. However, there is a competing ‘confused learners’ hypothesis: that individuals start the game either uncertain or mistaken (confused) and then learn from experience how to improve their payoff (payoff-based learning). Here we (1) show that these competing hypotheses can be differentiated by how they predict contributions should decline over time; and (2) use metadata from 237 published public goods games to test between these competing hypotheses. We found, as predicted by the confused learners hypothesis, that contributions declined faster when individuals had more influence over their own payoffs. This predicted relationship arises because more influence leads to a greater correlation between contributions and payoffs, facilitating learning. Our results suggest that humans, in general, are not altruistically motivated to benefit others but instead learn to help themselves.

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Fig. 1: The confused learners hypothesis.
Fig. 2: Payoff-based learning in a black box public goods game.
Fig. 3: Influence explains variation in the rate at which contributions change.
Fig. 4: Competing hypotheses.

Data availability

Figures 2 and 3 and Supplementary Figs. 35 have associated raw data available from the Dryad Digital Repository ( There are no restrictions on data availability.

Code availability

The R code for the simulation study is included in the Dryad Digital Repository (


  1. Bshary, R. & Raihani, N. J. Helping in humans and other animals: a fruitful interdisciplinary dialogue. Proc. Biol. Sci. 284, 20170929 (2017).

  2. Apicella, C. L. & Silk, J. B. The evolution of human cooperation. Curr. Biol. 29, R447–R450 (2019).

    CAS  PubMed  Google Scholar 

  3. Miller, G. Social distancing prevents infections, but it can have unintended consequences. Science (2020).

  4. Wynes, S. & Nicholas, K. A. The climate mitigation gap: education and government recommendations miss the most effective individual actions. Environ. Res. Lett. 12, 074024 (2017).

  5. Ledyard, J. in Handbook of Experimental Economics (eds Kagel, J. H. & Roth, A. E.) 253–279 (Princeton Univ. Press, 1995).

  6. Zelmer, J. Linear public goods experiments: a meta-analysis. Exp. Econ. 6, 299–310 (2003).

    Google Scholar 

  7. Chaudhuri, A. Sustaining cooperation in laboratory public goods experiments: a selective survey of the literature. Exp. Econ. 14, 47–83 (2011).

    Google Scholar 

  8. Arifovic, J. & Ledyard, J. Individual evolutionary learning, other-regarding preferences, and the voluntary contributions mechanism. J. Public Econ. 96, 808–823 (2012).

    Google Scholar 

  9. Fehr, E. & Schmidt, K. M. A theory of fairness, competition, and cooperation. Q. J. Econ. 114, 817–868 (1999).

    Google Scholar 

  10. Fehr, E. & Fischbacher, U. The nature of human altruism. Nature 425, 785–791 (2003).

    CAS  PubMed  Google Scholar 

  11. Camerer, C. F. & Fehr, E. When does ‘Economic Man’ dominate social behavior? Science 311, 47–52 (2006).

    CAS  PubMed  Google Scholar 

  12. Camerer, C. F. Experimental, cultural, and neural evidence of deliberate prosociality. Trends Cogn. Sci. 17, 106–108 (2013).

    PubMed  Google Scholar 

  13. Gachter, S., Kolle, F. & Quercia, S. Reciprocity and the tragedies of maintaining and providing the commons. Nat. Hum. Behav. 1, 650–656 (2017).

    PubMed  PubMed Central  Google Scholar 

  14. Fehr, E. & Schurtenberger, I. Normative foundations of human cooperation. Nat. Hum. Behav. 2, 458–468 (2018).

    PubMed  Google Scholar 

  15. Weber, T. O., Weisel, O. & Gächter, S. Dispositional free riders do not free ride on punishment. Nat. Commun. 9, 2390 (2018).

    PubMed  PubMed Central  Google Scholar 

  16. Fischbacher, U., Gachter, S. & Fehr, E. Are people conditionally cooperative? Evidence from a public goods experiment. Econ. Lett. 71, 397–404 (2001).

    Google Scholar 

  17. Fischbacher, U. & Gachter, S. Social preferences, beliefs, and the dynamics of free riding in public goods experiments. Am. Econ. Rev. 100, 541–556 (2010).

    Google Scholar 

  18. Thoni, C. & Volk, S. Conditional cooperation: review and refinement. Econ. Lett. 171, 37–40 (2018).

    Google Scholar 

  19. Andreoni, J. Cooperation in public-goods experiments—kindness or confusion. Am. Econ. Rev. 85, 891–904 (1995).

    Google Scholar 

  20. Palfrey, T. R. & Prisbrey, J. E. Altruism, reputation and noise in linear public goods experiments. J. Public Econ. 61, 409–427 (1996).

    Google Scholar 

  21. Palfrey, T. R. & Prisbrey, J. E. Anomalous behavior in public goods experiments: how much and why? Am. Econ. Rev. 87, 829–846 (1997).

    Google Scholar 

  22. Houser, D. & Kurzban, R. Revisiting kindness and confusion in public goods experiments. Am. Econ. Rev. 92, 1062–1069 (2002).

    Google Scholar 

  23. Cooper, D. J. & Stockman, C. K. Fairness and learning: an experimental examination. Games Econ. Behav. 41, 26–45 (2002).

    Google Scholar 

  24. Janssen, M. A. & Ahn, T. K. Learning, signaling, and social preferences in public-good games. Ecol. Soc. 11, 21 (2006).

  25. Burton-Chellew, M. N. & West, S. A. Prosocial preferences do not explain human cooperation in public-goods games. Proc. Natl Acad. Sci. USA 110, 216–221 (2013).

    CAS  PubMed  Google Scholar 

  26. Burton-Chellew, M. N., Nax, H. H. & West, S. A. Payoff-based learning explains the decline in cooperation in public goods games. Proc. Biol. Sci. 282, 20142678 (2015).

  27. Burton-Chellew, M. N., El Mouden, C. & West, S. A. Conditional cooperation and confusion in public-goods experiments. Proc. Natl Acad. Sci. USA 113, 1291–1296 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Andreozzi, L., Ploner, M. & Saral, A. S. The stability of conditional cooperation: beliefs alone cannot explain the decline of cooperation in social dilemmas. Sci. Rep. 10, 13610 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Shapiro, D. A. The role of utility interdependence in public good experiments. Int. J. Game Theory 38, 81–106 (2009).

    Google Scholar 

  30. Ferraro, P. J. & Vossler, C. A. The source and significance of confusion in public goods experiments. B.E. J. Econ. Anal. Policy 10, 53 (2010).

    Google Scholar 

  31. Bayer, R. C., Renner, E. & Sausgruber, R. Confusion and learning in the voluntary contributions game. Exp. Econ. 16, 478–496 (2013).

    Google Scholar 

  32. Kummerli, R., Burton-Chellew, M. N., Ross-Gillespie, A. & West, S. A. Resistance to extreme strategies, rather than prosocial preferences, can explain human cooperation in public goods games. Proc. Natl Acad. Sci. USA 107, 10125–10130 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Saijo, T. & Nakamura, H. The spite dilemma in voluntary contribution mechanism experiments. J. Confl. Resolut. 39, 535–560 (1995).

    Google Scholar 

  34. Brunton, D., Hasan, R. & Mestelman, S. The ‘spite’ dilemma: spite or no spite, is there a dilemma? Econ. Lett. 71, 405–412 (2001).

    Google Scholar 

  35. Cox, C. A. & Stoddard, B. Strategic thinking in public goods games with teams. J. Public Econ. 161, 31–43 (2018).

    Google Scholar 

  36. Gachter, S. in Psychology and Economics: a Promising New Cross-disciplinary Field (eds Frey, B. S. & Stuzter, A.) 19–50 (MIT Press, 2007).

  37. Bowles, S. Policies designed for self-interested citizens may undermine ‘the moral sentiments’: evidence from economic experiments. Science 320, 1605–1609 (2008).

    CAS  PubMed  Google Scholar 

  38. Bowles, S. & Hwang, S. H. Social preferences and public economics: mechanism design when social preferences depend on incentives. J. Public Econ. 92, 1811–1820 (2008).

    Google Scholar 

  39. Miller, J. H. & Andreoni, J. Can evolutionary dynamics explain free riding in experiments? Econ. Lett. 36, 9–15 (1991).

    Google Scholar 

  40. Nash, J. F. Equilibrium points in N-person games. Proc. Natl Acad. Sci. USA 36, 48–49 (1950).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Trivers, R. L. in Cooperation in Primates and Humans: Mechanisms and Evolution (eds Kappeler, P. M. & van Schaik, C. P.) 67–83 (Springer-Verlag, 2006).

  42. Burton-Chellew, M. N., El Mouden, C. & West, S. A. Evidence for strategic cooperation in humans. Proc. Biol. Sci. 284, 20170689 (2017).

    PubMed  PubMed Central  Google Scholar 

  43. Reuben, E. & Suetens, S. Revisiting strategic versus non-strategic cooperation. Exp. Econ. 15, 24–43 (2012).

    Google Scholar 

  44. Bigoni, M. & Suetens, S. Feedback and dynamics in public good experiments. J. Econ. Behav. Organ. 82, 86–95 (2012).

    Google Scholar 

  45. Fiala, L. & Suetens, S. Transparency and cooperation in repeated dilemma games: a meta study. Exp. Econ. 20, 755–771 (2017).

    PubMed  PubMed Central  Google Scholar 

  46. Cartwright, E. J. & Lovett, D. Conditional cooperation and the marginal per capita return in public good games. Games 5, 234–256 (2014).

    Google Scholar 

  47. Simmons, J. P., Nelson, L. D. & Simonsohn, U. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22, 1359–1366 (2011).

    PubMed  Google Scholar 

  48. Head, M. L., Holman, L., Lanfear, R., Kahn, A. T. & Jennions, M. D. The extent and consequences of P-hacking in science. PLoS Biol. https://dx.doi.org10.1371/journal.pbio.1002106 (2015).

  49. Henrich, J. et al. ‘Economic man’ in cross-cultural perspective: behavioral experiments in 15 small-scale societies. Behav. Brain Sci. (2005).

  50. Henrich, J., Heine, S. J. & Norenzayan, A. Most people are not WEIRD. Nature 466, 29–29 (2010).

    CAS  PubMed  Google Scholar 

  51. Kocher, M. G., Martinsson, P. & Visser, M. Does stake size matter for cooperation and punishment? Econ. Lett. 99, 508–511 (2008).

    Google Scholar 

  52. Karagozoglu, E. & Urhan, U. B. The effect of stake size in experimental bargaining and distribution games: a survey. Group Decis. Negot. 26, 285–325 (2017).

    Google Scholar 

  53. Larney, A., Rotella, A. & Barclay, P. Stake size effects in ultimatum game and dictator game offers: a meta-analysis. Organ. Behav. Hum. Decis. Process. 151, 61–72 (2019).

    Google Scholar 

  54. Plott, C. R. & Zeiler, K. The willingness to pay-willingness to accept gap, the ‘endowment effect,’ subject misconceptions, and experimental procedures for eliciting valuations. Am. Econ. Rev. 95, 530–545 (2005).

    Google Scholar 

  55. Chou, E., McConnell, M., Nagel, R. & Plott, C. R. The control of game form recognition in experiments: understanding dominant strategy failures in a simple two person ‘guessing’ game. Exp. Econ. 12, 159–179 (2009).

    Google Scholar 

  56. Gachter, S. & Thoni, C. Social learning and voluntary cooperation among like-minded people. J. Eur. Econ. Assoc. 3, 303–314 (2005).

    Google Scholar 

  57. Gunnthorsdottir, A., Houser, D. & McCabe, K. Disposition, history and contributions in public goods experiments. J. Econ. Behav. Organ. 62, 304–315 (2007).

    Google Scholar 

  58. Gunnthorsdottir, A., Vragov, R., Seifert, S. & McCabe, K. Near-efficient equilibria in contribution-based competitive grouping. J. Public Econ. 94, 987–994 (2010).

    Google Scholar 

  59. Nax, H. H., Murphy, R. O., Duca, S. & Helbing, D. Contribution-based grouping under noise. Games 8, 50 (2017).

    Google Scholar 

  60. Nax, H. H., Murphy, R. O. & Helbing, D. in Social Dilemmas, Institutions, and the Evolution of Cooperation (eds Ben Jann, B. & Przepiorka, W.) 447–469 (de Gruyter Oldenbourg, 2017).

  61. McAuliffe, W. H. B., Burton-Chellew, M. N. & McCullough, M. E. Cooperation and learning in unfamiliar situations. Curr. Dir. Psychol. Sci. 28, 436–440 (2019).

    Google Scholar 

  62. Rand, D. G. et al. Social heuristics shape intuitive cooperation. Nat. Commun. 5, 3677 (2014).

  63. Bear, A. & Rand, D. G. Intuition, deliberation, and the evolution of cooperation. Proc. Natl Acad. Sci. USA 113, 936–941 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. R: a language and environment for statistical computing. Version 3.6 (R Foundation for Statistical Computing, 2017).

  65. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2009).

  66. Greiner, B. Subject pool recruitment procedures: organizing experiments with ORSEE. J. Econ. Sci. Assoc. 1, 114–125 (2015).

    Google Scholar 

  67. Fischbacher, U. z-Tree: Zurich toolbox for ready-made economic experiments. Exp. Econ. 10, 171–178 (2007).

    Google Scholar 

  68. Eckel, C. & Grossman, P. J. in Handbook of Experimental Economics Results Vol. 1 (eds Plott, C. R. & Smith, V. L.) Ch. 57 (North-Holland, 2008).

  69. Balliet, D., Li, N. P., Macfarlan, S. J. & Van Vugt, M. Sex differences in cooperation: a meta-analytic review of social dilemmas. Psychol. Bull. 137, 881–909 (2011).

    PubMed  Google Scholar 

  70. Garson, G. D. (ed.) Hierarchical Linear Modeling: Guide and Applications (SAGE, 2013).

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Funding provided by Calleva Research Centre for Evolution and Human Sciences, Magdalen College, Oxford (M.N.B.-C. and S.A.W.), ERC advanced grant 834164 (S.A.W.) and the University of Lausanne, Switzerland (M.N.B.-C.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Thanks to Z. Griffiths for help with data collection, L. Lehmann for discussions and P. Barclay for reviewing the manuscript.

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M.N.B.-C. and S.A.W. conceived and designed the research. M.N.B.-C. collected the data and performed the analyses. M.N.B.-C. and S.A.W. wrote the article.

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Correspondence to Maxwell N. Burton-Chellew.

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Burton-Chellew, M.N., West, S.A. Payoff-based learning best explains the rate of decline in cooperation across 237 public-goods games. Nat Hum Behav 5, 1330–1338 (2021).

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