False-name-proof matching

Taiki Todo, Vincent Conitzer

Research output: Contribution to conferencePaper

17 Citations (Scopus)

Abstract

Matching a set of agents to a set of objects has many real applications. One well-studied framework is that of priority-based matching, in which each object is assumed to have a priority order over the agents. The Deferred Acceptance (DA) and Top-Trading-Cycle (TTC) mechanisms are the best-known strategy-proof mechanisms. However, in highly anonymous environments, the set of agents is not known a priori, and it is more natural for objects to instead have priorities over characteristics (e.g., the student's GPA or home address). In this paper, we extend the model so that each agent reports not only its preferences over objects, but also its characteristic. We derive results for various notions of strategy-proofness and false-name-proofness, corresponding to whether agents can only report weaker characteristics or also incomparable or stronger ones, and whether agents can only claim objects allocated to their true accounts or also those allocated to their fake accounts. Among other results, we show that DA and TTC satisfy a weak version of false-name-proofness. Furthermore, DA also satisfies a strong version of false-name-proofness, while TTC fails to satisfy it without an acyclicity assumption on priorities.

Original languageEnglish
Pages311-318
Number of pages8
Publication statusPublished - Jan 1 2013
Externally publishedYes
Event12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 - Saint Paul, MN, United States
Duration: May 6 2013May 10 2013

Other

Other12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013
CountryUnited States
CitySaint Paul, MN
Period5/6/135/10/13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Todo, T., & Conitzer, V. (2013). False-name-proof matching. 311-318. Paper presented at 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013, Saint Paul, MN, United States.