Conditional cooperation can hinder network reciprocity

Semmann, Dirk

doi:10.1073/pnas.1210294109

Cited by 16 publications

(6 citation statements)

References 29 publications

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“…However, cooperative outcomes have been rarely observed in experiments on many static networks 56 57 58 59 60 . A possible explanation for this inconsistency is that most theoretical studies have considered payoff-based updating rules, whereas the people in the experiments did not adopt these rules 61 62 63 . In particular, a behavioral rule called “moody conditional cooperation” was observed in several spatial PD game experiments, where moody conditional cooperators make decisions based on their own previous action and the actions of their neighbors (specifically, they cooperate more when they themselves cooperated in the previous round and more of their neighbors cooperated) 61 63 .…”

Section: Discussionmentioning

confidence: 99%

The dynamics of human behavior in the public goods game with institutional incentives

Dong

Zhang

Tao

2016

Sci Rep

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The empirical research on the public goods game (PGG) indicates that both institutional rewards and institutional punishment can curb free-riding and that the punishment effect is stronger than the reward effect. Self-regarding models that are based on Nash equilibrium (NE) strategies or evolutionary game dynamics correctly predict which incentives are best at promoting cooperation, but individuals do not play these rational strategies overall. The goal of our study is to investigate the dynamics of human decision making in the repeated PGG with institutional incentives. We consider that an individual’s contribution is affected by four factors, which are self-interest, the behavior of others, the reaction to rewards, and the reaction to punishment. We find that people on average do not react to rewards and punishment, and that self-interest and the behavior of others sufficiently explain the dynamics of human behavior. Further analysis suggests that institutional incentives promote cooperation by affecting the self-regarding preference and that the other-regarding preference seems to be independent of incentive schemes. Because individuals do not change their behavioral patterns even if they were not rewarded or punished, the mere potential to punish defectors and reward cooperators can lead to considerable increases in the level of cooperation.

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Section: Discussionmentioning

confidence: 99%

The dynamics of human behavior in the public goods game with institutional incentives

Dong

Zhang

Tao

2016

Sci Rep

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“…In this respect, it is worth noting that our model assumes the dynamics of the master and the agents' strategy updates. As has been shown for the case of the Prisoner's Dilemma [24,25,64,65], it can perfectly be the case that the way people behave in this kind of situation is not that we are assuming. Therefore, following the usual physics approach to a problem, specifically designed experiments where the dynamics of actual human subjects could be observed are in order to assess the extent of the validity of our work.…”

Section: Discussionmentioning

confidence: 92%

Crowd Computing as a Cooperation Problem: An Evolutionary Approach

et al. 2012

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Cooperation is one of the socio-economic issues that has received more attention from the physics community. The problem has been mostly considered by studying games such as the Prisoner's Dilemma or the Public Goods Game. Here, we take a step forward by studying cooperation in the context of crowd computing. We introduce a model loosely based on Principal-agent theory in which people (workers) contribute to the solution of a distributed problem by computing answers and reporting to the problem proposer (master). To go beyond classical approaches involving the concept of Nash equilibrium, we work on an evolutionary framework in which both the master and the workers update their behavior through reinforcement learning. Using a Markov chain approach, we show theoretically that under certain--not very restrictive-conditions, the master can ensure the reliability of the answer resulting of the process. Then, we study the model by numerical simulations, finding that convergence, meaning that the system reaches a point in which it always produces reliable answers, may in general be much faster than the upper bounds given by the theoretical calculation. We also discuss the effects of the master's level of tolerance to defectors, about which the theory does not provide information. The discussion shows that the system works even with very large tolerances. We conclude with a discussion of our results and possible directions to carry this research further.

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“…As several authors did in the last decade, [19][20][21][22][23] we question network reciprocity as a mechanism supporting cooperation in socio-economic networks. In particular, we question the rationale behind the imitation process of strategy update.…”

Section: Introductionmentioning

confidence: 97%

Rethinking network reciprocity over social ties: local interactions make direct reciprocity possible and pave the rational way to cooperation

Dercole,

Della Rossa,

Piccardi

2018

Preprint

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Since Nowak & May's (1992) influential paper, network reciprocity-the fact that local interactions allow unconditional cooperators to self-organize into clusters-was proposed as the simplest mechanism for the evolution of cooperation in biological and socio-economic systems. It has been confirmed in several theoretical models and shown to predict the fixation of cooperation under a simple rule: the benefit of a single altruistic act must outweigh the cost of cooperating with all neighbors. The experimental evidence among humans is however controversial. The reason is that, while models assume that individuals update strategy by imitating better performing neighbors, experiments showed that humans are more prone to reciprocate cooperation than to compare payoffs. Motivated by the empirical results, we rethink network reciprocity as a rational form of direct reciprocity on networks, indeed made possible by the locality of interactions. Imitating a better performing neighbor is irrational in networks far from all-to-all, as it can well cause a payoff loss. Instead, we base strategy update on a model prediction among two options: conditional-reciprocal-cooperation and unconditional defection in a networked prisoner's dilemma. We show that both reciprocity and a multi-step predictive horizon are necessary to sustain cooperation, as well as sufficient for its fixation in any network, provided the game benefit-to-cost ratio is larger than a measure of network's connectivity. We hence rediscover the same simple rule-experimentally validated-underpinned however with a different evolutionary mechanism.

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Conditional cooperation can hinder network reciprocity

Cited by 16 publications

References 29 publications

The dynamics of human behavior in the public goods game with institutional incentives

The dynamics of human behavior in the public goods game with institutional incentives

Crowd Computing as a Cooperation Problem: An Evolutionary Approach

Rethinking network reciprocity over social ties: local interactions make direct reciprocity possible and pave the rational way to cooperation

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