Nurturing group-beneficial information-gathering behaviors through above-threshold criteria setting

Igor Rochlin, David Sarne, Maytal Bremer, Ben Grynhaus

Research output: Contribution to conferencePaperpeer-review


This paper studies a criteria-based mechanism for nurturing and enhancing agents' group-benefiting individual efforts whenever the agents are self-interested. The idea is that only those agents that meet the criteria get to benefit from the group effort, giving an incentive to contribute even when it is otherwise individually irrational. Specifically, the paper provides a comprehensive equilibrium analysis of a thresholdbased criteria mechanism for the common cooperative information gathering application, where the criteria is set such that only those whose contribution to the group is above some pre-specified threshold can benefit from the contributions of others. The analysis results in a closed form solution for the strategies to be used in equilibrium and facilitates the numerical investigation of different model properties as well as a comparison to the dual mechanism according to only an agent whose contribution is below the specified threshold gets to benefit from the contributions of others. One important contribution enabled through the analysis provided is in showing that, counter-intuitively, for some settings the use of the above-threshold criteria is outperformed by the use of the below-threshold criteria as far as collective and individual performance is concerned.

Original languageEnglish
Number of pages8
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States
Duration: 4 Feb 201710 Feb 2017


Conference31st AAAI Conference on Artificial Intelligence, AAAI 2017
Country/TerritoryUnited States
CitySan Francisco

Bibliographical note

Funding Information:
This research was partially supported by the ISRAEL SCIENCE FOUNDATION (grants No. 1083/13) and the ISF-NSFC joint research program (grant No. 2240/15).

Publisher Copyright:
Copyright © 2017, Association for the Advancement of Artificial Intelligence ( All rights reserved.

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