Constraining information sharing to improve cooperative information gathering

Igor Rochlin, David Same

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper considers the problem of cooperation between self-interested agents in acquiring better information regarding the nature of the different options and opportunities available to them. By sharing individual findings with others, the agents can potentially achieve a substantial improvement in overall and individual expected benefit. Alas, when it comes to self-interested agents, it is well known that equilibrium considerations often dictate solutions that are far from the fully cooperative ones, hence the agents do not manage to fully exploit the potential benefits encapsulated in such cooperation. In this paper we introduce, analyze and demonstrate the benefit of two methods aiming to improve cooperative information gathering. Common to all two that they constrain and limit the information sharing process. Nevertheless, the decrease in benefit due to the limited sharing is outweighed by the resulting substantial improvement in the equilibrium individual information gathering strategies. The equilibrium analysis that is given in the paper, which, in itself, is an important contribution to the study of cooperation between self-interested agents, enables demonstrating that for a wide range of settings with the use of the two methods all agents end up with an improved individual expected benefit.

Original languageEnglish
Title of host publication13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Pages237-244
Number of pages8
ISBN (Electronic)9781634391313
StatePublished - 2014
Event13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014 - Paris, France
Duration: 5 May 20149 May 2014

Publication series

Name13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Volume1

Conference

Conference13th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2014
Country/TerritoryFrance
CityParis
Period5/05/149/05/14

Keywords

  • Cooperation
  • Economically-motivated agents
  • Multi-agent exploration
  • Self-interested agents
  • Teamwork

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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