Rule Enforcing Through Ordering
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Abstract (arXiv)
In many real world situations, like minor traffic offenses in big cities, a central authority is tasked with periodic administering punishments to a large number of individuals. Common practice is to give each individual a chance to suffer a smaller fine and be guaranteed to avoid the legal process with probable considerably larger punishment. However, thanks to the large number of offenders and a limited capacity of the central authority, the individual risk is typically small and a rational individual will not choose to pay the fine. Here we show that if the central authority processes the offenders in a publicly known order, it properly incentives the offenders to pay the fine. We show analytically and on realistic experiments that our mechanism promotes non-cooperation and incentives individuals to pay. Moreover, the same holds for an arbitrary coalition. We quantify the expected total payment the central authority receives, and show it increases considerably.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 9 references that are in the map. First posted on arXiv 31 Mar 2023.
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5 of 9 references.
References · 5
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 313
- 2017A Unified Game-Theoretic Approach to Multiagent Reinforcement Learningcited by 268
- 2020An operator view of policy gradient methodscited by 17
- 2013Tight Lower Bound on the Probability of a Binomial Exceeding its Expectationcited by 7
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