@Article{shoham97a,
  author =	 {Yoav Shoham and Moshe Tennenholtz},
  title =	 {On the emergence of social conventions: modeling,
                  analysis, and simulations},
  googleid =	 {MnR3R4DssjcJ:scholar.google.com/},
  journal =	 {Artificial Intelligence},
  year =	 1997,
  volume =	 94,
  pages =	 {139--166},
  abstract =	 {We define the notion of social conventions in a
                  standard game-theoretic framework, and identify
                  various criteria of consistency of such conventions
                  with the principle of individual rationality. We
                  then investigate the emergence of such conventions
                  in a stochastic setting; we do so within a stylized
                  framework currently popular in economic circles,
                  namely that of stochastic games. This framework
                  comes in several forms; in our setting agents
                  interact with each other through a random process,
                  and accumulate information about the system. As they
                  do so, they continually reevaluate their current
                  choice of strategy in light of the accumulated
                  information. We introduce a simple and natural
                  strategy-selection rule, called highest cumulative
                  reward (HCR). We show a class of games in which HCR
                  guarantees eventual convergence to a rationally
                  acceptable social convention. Most importantly, we
                  investigate the efficiency with which such social
                  conventions are achieved. We give an analytic lower
                  bound on this rate, and then present results about
                  how HCR works out in practice. Specifically, we pick
                  one of the most basic games, namely a basic
                  coordination game (as defined by Lewis), and through
                  extensive computer simulations determine not only
                  the effect of applying HCR, but also the subtle
                  effects of various system parameters, such as the
                  amount of memory and the frequency of update
                  performed by all agents.},
  keywords =     {multiagent learning game-theory},
  url =		 {http://jmvidal.cse.sc.edu/library/shoham97a.pdf},
  cluster = 	 {4013530253639513138}
}