@Article{	  vidal:98b,
  author =	 {Jos\'{e} M. Vidal and Edmund H. Durfee},
  title =	 {Learning Nested Models in an Information Economy},
  journal =	 {Journal of Experimental and Theoretical Artificial
                  Intelligence},
  volume =	 10,
  number =	 3,
  pages =	 {291--308},
  year =	 1998,
  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 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. },
  url =		 {http://xxx.lanl.gov/abs/cs.MA/9809108},
  arxiv = 	 {cs.MA/9809108},
  googleid = 	 {C6ZmJUoqfToJ:scholar.google.com/},
  keywords = 	 {multiagent learning auctions},
  cluster = 	 {4214571324232082955}
}