Nurturing Group-Beneficial Information-Gathering Behaviors Through Above-Threshold Criteria Setting

Authors

  • Igor Rochlin The College of Management Academic Studies
  • David Sarne Bar-Ilan University
  • Maytal Bremer The College of Management Academic Studies
  • Ben Grynhaus The College of Management Academic Studies

DOI:

https://doi.org/10.1609/aaai.v31i1.10705

Keywords:

Multi-Agent Exploration, Self-Interested Agents, Cooperation, Teamwork, Economically-Motivated Agents

Abstract

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 threshold-based 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.

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Published

2017-02-12

How to Cite

Rochlin, I., Sarne, D., Bremer, M., & Grynhaus, B. (2017). Nurturing Group-Beneficial Information-Gathering Behaviors Through Above-Threshold Criteria Setting. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10705

Issue

Section

AAAI Technical Track: Multiagent Systems