Incentive-Compatible Classification

Yakov Babichenko; Oren Dean; Moshe Tennenholtz

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arXiv · 1911.08849

Incentive-Compatible Classification

Yakov Babichenko·Oren Dean·Moshe Tennenholtz

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Abstract

We investigate the possibility of an incentive-compatible (IC, a.k.a. strategy-proof) mechanism for the classification of agents in a network according to their reviews of each other. In the $ α$-classification problem we are interested in selecting the top $ α$ fraction of users. We give upper bounds (impossibilities) and lower bounds (mechanisms) on the worst-case coincidence between the classification of an IC mechanism and the ideal $ α$-classification. We prove bounds which depend on $ α$ and on the maximal number of reviews given by a single agent, $ Δ$. Our results show that it is harder to find a good mechanism when $ α$ is smaller and $ Δ$ is larger. In particular, if $ Δ$ is unbounded, then the best mechanism is trivial (that is, it does not take into account the reviews). On the other hand, when $ Δ$ is sublinear in the number of agents, we give a simple, natural mechanism, with a coincidence ratio of $ α$.

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BibTeX RIS

Yakov Babichenko, Oren Dean, Moshe Tennenholtz. 2019-11-20. Incentive-Compatible Classification. https://arxiv.org/abs/1911.08849

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