@INPROCEEDINGS{vidal:94a,
  AUTHOR =	 {Jos\'{e} M. Vidal and Edmund H. Durfee},
  TITLE =	 {{RMM}'s Solution Concept and the Equilibrium Point
                  Solution},
  BOOKTITLE =	 {Proceedings of the 13th International Distributed
                  Artificial Intelligence Workshop},
  YEAR =	 1994,
  url =		 {http://jmvidal.cse.sc.edu/papers/vidal94a.pdf},
  postscript =	 {http://jmvidal.cse.sc.edu/papers/vidal94a.ps},
  citeseer =	 {vidal94rmms.html},
  googleid =	 {0akXaXXONPgJ:scholar.google.com/},
  keywords =	 {multiagent modeling},
  abstract =	 {Research in distributed AI has dealt with the
                  interactions of agents' both cooperative and
                  self-interested. The Recursive Modeling Method (RMM)
                  is one method used for modeling rational
                  self-interested agents. It assumes that knowledge is
                  nested to a finite depth. An expansion of RMM using
                  a sigmoid function was proposed with the hope that
                  the solution concept of the new RMM would
                  approximate the Nash EP in cases where RMMS
                  knowledge approximated the common knowledge that is
                  assumed by game theory. In this paper, we present a
                  mathematical analysis of RMM with the sigmoid
                  function and prove that it indeed tries to converge
                  to the Nash EP. However, we also show how and why it
                  fails to do so for most cases. Using this analysis
                  we argue for abandoning the sigmoid function as an
                  implicit representation of uncertainty about the
                  depth of knowledge in favor of an explicit
                  representation of the uncertainty. We also suggest
                  other avenues of research that might give us other
                  more efficient solution concepts which would also take
                  into consideration the cost of computation and the
                  expected gains.},
  cluster =	 {17885147023864736209}
}