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baai.ac.cn

智源研究院是人工智能领域的新型研发机构，汇集国际顶尖人工智能学者，聚焦核心技术与原始创新，旨在推动人工智能领域发展政策、学术思想、理论基础、顶尖人才与产业生态的五大源头创新。

researchgate.net

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sciencedirect.com

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scholar.google.com

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ieeexplore.ieee.org

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arxiv.org

A system of non-tradable credits that flow between individuals like karma, hence proposed under that name, is a mechanism for repeated resource allocation that comes with attractive efficiency and fairness properties, in theory. In this study, we test karma in an online experiment in which human subjects repeatedly compete for a resource with time-varying and stochastic individual preferences or urgency to acquire the resource. We confirm that karma has significant and sustained welfare benefits even in a population with no prior training. We identify mechanism usage in contexts with sporadic high urgency, more so than with frequent moderate urgency, and implemented as a simple (binary) karma bidding scheme as particularly effective for welfare improvements: relatively larger aggregate efficiency gains are realized that are (almost) Pareto superior. These findings provide guidance for further testing and for future implementation plans of such mechanisms in the real world.