Punishment mechanisms and cooperation in public goods games: Experimental evidence

Peng, Hui-Chun

doi:10.1111/apce.12343

Cited by 4 publications

(1 citation statement)

References 29 publications

(48 reference statements)

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“…In addition, a punishment mechanism [44] is introduced to encourage the two parties to participate in innovation activity: when one party cooperates and the other does not, the noncooperative party pays a fine, P, to the cooperative one.…”

Section: Problem Description and Formulationmentioning

confidence: 99%

Decision‐Making Mechanism of Cooperative Innovation between Clients and Service Providers Based on Evolutionary Game Theory

Zhang

Cao

et al. 2022

Discrete Dynamics in Nature and Society

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With the maturity of the service outsourcing market and the development of business relations, the core of outsourcing is shifting from transactional services to risk sharing and value creation. The client and service provider have an increasing interest in service innovation. Although cooperative innovation between them has many benefits, the two parties do not necessarily establish a cooperative innovation relationship. Regarding this issue, an evolutionary game focusing on client-service provider cooperative innovation behavior is constructed and solved. Based on the results and a corresponding numerical simulation, the decision-making mechanism of the cooperative innovation behavior is studied, and suggestions are provided regarding how to promote cooperative innovation. The results show that the benefits of both the client and service provider when they innovate cooperatively being greater than that when they innovate independently cannot guarantee that the system will certainly evolve to a stable state in which both parties adopt a cooperation strategy. However, as long as a condition in which either party gains more than zero when it innovates independently is established in addition to the preceding condition, the system will certainly evolve to a stable state in which both parties adopt a cooperative strategy. The following measures can be taken to promote client-service provider cooperation: improving the initial probabilities of the two parties choosing the cooperative strategy; increasing the innovation benefit when one party innovates independently; reducing the innovation cost and spillover coefficient when one party innovates independently; increasing the penalty when one party cooperates and the other party does not; decreasing the innovation cost when the two parties both choose the cooperation strategy; increasing the excess benefit when the two parties both cooperate; setting reasonable benefit distribution and cost sharing proportions.

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Section: Problem Description and Formulationmentioning

confidence: 99%

Decision‐Making Mechanism of Cooperative Innovation between Clients and Service Providers Based on Evolutionary Game Theory

Zhang

Cao

et al. 2022

Discrete Dynamics in Nature and Society

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Incomplete punishment networks, heterogeneity, and cooperation in public good experiments

Peng,

Fan

2023

Journal of Behavioral and Experimental Economics

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Computational behavioral models in public goods games with migration between groups

Tomassini

Antonioni

2021

J. Phys. Complex.

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In this study we have simulated numerically two models of linear public goods games where players are equally distributed among a given number of groups. Agents play in their group by using two simple sets of rules, called ‘blind’ and ‘rational’ model, respectively, that are inspired by the observed behavior of human participants in laboratory experiments. In addition, unsatisfied agents have the option of leaving their group and migrating to a new random one through probabilistic choices. Stochasticity, and the introduction of two types of players in the blind model, help simulate the heterogeneous behavior that is often observed in experimental work. Our numerical simulations of the corresponding dynamical systems show that being able to leave a group when unsatisfied favors contribution and avoids free-riding to a good extent in a range of the enhancement factor where defection would prevail without migration. Our numerical simulation presents results that are qualitatively in line with known experimental data when human agents are given the same kind of information about themselves and the other players in the group. This is usually not the case with customary mathematical models based on replicator dynamics or stochastic approaches. As a consequence, models like the ones described here may be useful for understanding experimental results and also for designing new experiments by first running cheap computational simulations instead of doing costly preliminary laboratory work. The downside is that models and their simulation tend to be less general than standard mathematical approaches.

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Punishment mechanisms and cooperation in public goods games: Experimental evidence

Cited by 4 publications

References 29 publications

Decision‐Making Mechanism of Cooperative Innovation between Clients and Service Providers Based on Evolutionary Game Theory

Decision‐Making Mechanism of Cooperative Innovation between Clients and Service Providers Based on Evolutionary Game Theory

Incomplete punishment networks, heterogeneity, and cooperation in public good experiments

Computational behavioral models in public goods games with migration between groups

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