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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.

openreview.net

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dl.acm.org

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ieeexplore.ieee.org

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researchgate.net

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scholar.google.com

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proceedings.mlr.press

The Proceedings of Machine Learning Research (formerly JMLR Workshop and Conference Proceedings) is a series aimed specifically at publishing machine learning research presented at workshops and conferences. Each volume is separately titled and associated with a particular workshop or conference. Volumes are published online on the PMLR web site. The Series Editors are Neil D. Lawrence and Mark Reid.

ai.umich.edu

Supporting Life-Changing EducationThe Academic Innovation Fund has supported hundreds of innovations from U-M faculty and staff that benefit U-M students and global learners. Learn MoreHelping Students Build Early Academic MomentumThe Center for Academic Innovation is working with university partner