Learning nested agent models in an information economy
Open Access
- 1 July 1998
- journal article
- research article
- Published by Taylor & Francis in Journal of Experimental & Theoretical Artificial Intelligence
- Vol. 10 (3) , 291-308
- https://doi.org/10.1080/095281398146770
Abstract
We 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 modelling 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 behaviour 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 behaviour.Keywords
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