Learning Nested Agent Models in an Information Economy

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dc.creatorVidal, Jose M.
dc.creatorDurfee, Edmund H.
dc.date1998-09-26
dc.date.accessioned2026-07-07T03:23:40Z
dc.date.available2026-07-07T03:23:40Z
dc.descriptionWe present our approach to the problem of how an agent, within an economic Multi-Agent System, can determine when it should behave strategically (i.e. learn and use models of other agents), and when it should act as a simple price-taker. We provide a framework for the incremental implementation of modeling capabilities in agents, and a description of the forms of knowledge required. The agents were implemented and different populations simulated in order to learn more about their behavior and the merits of using and learning agent models. Our results show, among other lessons, how savvy buyers can avoid being ``cheated'' by sellers, how price volatility can be used to quantitatively predict the benefits of deeper models, and how specific types of agent populations influence system behavior.
dc.identifierhttps://arxiv.org/abs/cs/9809108
dc.identifierhttp://arxiv.org/abs/cs/9809108
dc.identifierJournal of Experimental and Theoretical Artificial Intelligence. 10(1998)291-308
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/33037
dc.subjectMultiagent Systems
dc.subjectArtificial Intelligence
dc.subjectI 2.11
dc.titleLearning Nested Agent Models in an Information Economy
dc.typetext

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