Payoff-based learning explains the decline in cooperation in public goods games

Burton-Chellew, Maxwell N.; Nax, Heinrich H.; West, Stuart A.

doi:10.1098/rspb.2014.2678

Cited by 83 publications

(99 citation statements)

References 44 publications

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“…In all cases, communication was forbidden, and we provided no feedback on earnings or the behavior of groupmates. This design prevents signaling, reciprocity, and learning and therefore minimizes any order effects (51)(52)(53).…”

Section: Resultsmentioning

confidence: 99%

Conditional cooperation and confusion in public-goods experiments

Burton-Chellew

Mouden

West

2016

Proc. Natl. Acad. Sci. U.S.A.

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Economic experiments are often used to study if humans altruistically value the welfare of others. A canonical result from public-good games is that humans vary in how they value the welfare of others, dividing into fair-minded conditional cooperators, who match the cooperation of others, and selfish noncooperators. However, an alternative explanation for the data are that individuals vary in their understanding of how to maximize income, with misunderstanding leading to the appearance of cooperation. We show that (i) individuals divide into the same behavioral types when playing with computers, whom they cannot be concerned with the welfare of; (ii) behavior across games with computers and humans is correlated and can be explained by variation in understanding of how to maximize income; (iii) misunderstanding correlates with higher levels of cooperation; and (iv) standard control questions do not guarantee understanding. These results cast doubt on certain experimental methods and demonstrate that a common assumption in behavioral economics experiments, that choices reveal motivations, will not necessarily hold.altruism | strategy method | inequity aversion | reciprocity | social preferences

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Section: Resultsmentioning

confidence: 99%

Conditional cooperation and confusion in public-goods experiments

Burton-Chellew

Mouden

West

2016

Proc. Natl. Acad. Sci. U.S.A.

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123

140

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“…Avenues for further research include multilateral bargaining experiments in the laboratory, building on the classic bargaining experiments by Tietz and Weber and on more recent non-bargaining experiments in low-information environments such as Bayer et al (2013), Nax et al (2013), Burton-Chellew et al (2015). We are particularly interested in the speed with which convergence occurs, and the conditions under which such simple directional bargaining dynamics apply.…”

Section: Resultsmentioning

confidence: 99%

“…Instead, directionality is born from the fact that players have a tendency to demand more (less) when currently receiving a payoff that matches or exceeds (falls short of) their aspirations. A similar approach has recently been taken by Nax et al (2013), Nax and Perc (2015), Nax and Pradelski (2015), and Burton-Chellew et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Equity dynamics in bargaining without information exchange

Nax

2015

J Evol Econ

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In this paper, completely uncoupled dynamics for n-player bargaining are proposed that mirror key behavioral elements of early bargaining and aspiration adjustment models (Zeuthen 1930, Sauermann & Selten 1962. Individual adjustment dynamics are based on directional learning adjustments, solely driven by histories of own realized payoffs. Bargaining this way, all possible splits have positive probability in the stationary distribution of the process, but players will split the pie almost equally most of the time. The expected waiting time for almost equal splits to be played is quadratic.JEL classifications: C71, C73, C78, D83

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“…3 Meritocracy β guides smoothly from (i) no meritocracy (grouping is random as in [6]) to (ii) full meritocracy (the case of perfect contribution-based grouping as in [5]). Note that many public goods experiments use variants of Andreoni's random (re-)matching implementation (e.g., [6][7][8][9][10][11][12][13][14][15][16]); see [17,18] for reviews.…”

Section: The Modelmentioning

confidence: 99%

“…Note that many public goods experiments use variants of Andreoni's random (re-)matching implementation (e.g., [6][7][8][9][10][11][12][13][14][15][16]); see [17,18] for reviews. Step 3.…”

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confidence: 99%

Contribution-Based Grouping under Noise

Nax

Murphy

Duca

et al. 2017

Games

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Many real-world mechanisms are "noisy" or "fuzzy", that is the institutions in place to implement them operate with non-negligible degrees of imprecision and error. This observation raises the more general question of whether mechanisms that work in theory are also robust to more realistic assumptions such as noise. In this paper, in the context of voluntary contribution games, we focus on a mechanism known as "contribution-based competitive grouping". First, we analyze how the mechanism works under noise and what happens when other assumptions such as population homogeneity are relaxed. Second, we investigate the welfare properties of the mechanism, interpreting noise as a policy instrument, and we use logit dynamic simulations to formulate mechanism design recommendations.Keywords: voluntary contributions; behavioral economics; noise; heterogeneity; mechanism design; welfare; efficiency; equality JEL Classification: C73; D02; D03; D63 MotivationTypically, individual decisions in social-dilemma interactions are not perfectly observable in the real world. Applied mechanism designers should keep this in mind when implementing mechanisms, in particular in the context of interactions that have the strategic nature of voluntary contribution games where imperfect observability is ubiquitous. 1 Real-world institutions are usually "noisy" or "fuzzy", operating with non-negligible degree of imprecision. By contrast, theory investigations of mechanisms for the most part study perfect mechanisms. In this paper, as a first step towards performing robustness checks of mechanisms under relaxed assumptions more generally, we investigate the mechanism of contribution-based competitive grouping (as introduced by [5]): we relax some assumptions and investigate what happens when there is (i) noise, (ii) heterogeneity and (iii) different action spaces.The paper is structured as follows. Next, we introduce our model. In Section 3, we first analyze how the existence of Nash equilibria depends on (iii) the strategy space of the game and on (ii) the underlying population homogeneity/heterogeneity in terms of contribution budgets. Then, we assess their robustness when we allow more and more (i) monitoring noise. In Section 4, we investigate how a social planner, interpreting monitoring noise and/or the other model characteristics as policy instruments, would trade-off efficiency and equality to maximize social welfare. Finally, we use agent-based simulations to study the logit dynamics of the game in order to quantify the effect of noisy monitoring. 1 For other social-dilemma contexts, see, for example, [1,2] for imperfect public monitoring, [3] for noisy prisoners' dilemmas and [4] for team-production games with group-level information. Our results are summarized as follows. In terms of the existence of Nash equilibria, the zerocontribution outcome is always a Nash equilibrium and becomes the unique one under too much noise. High-contribution equilibria exist if three things come together: the rate of return of the underlyin...

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Payoff-based learning explains the decline in cooperation in public goods games

Cited by 83 publications

References 44 publications

Conditional cooperation and confusion in public-goods experiments

Conditional cooperation and confusion in public-goods experiments

Equity dynamics in bargaining without information exchange

Contribution-Based Grouping under Noise

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