On the Emergence of Social Conventions: Modeling, Analysis, and Simulations

We de ne 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 strat egy 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 impor tantly we investigate the e ciency 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 Speci cally we pick one of the most basic games namely a basic coordination game as de ned by Lewis and through extensive computer simulations determine not only the e ect of applying HCR but also the subtle e ects of various system parameters such as the amount of memory and the frequency of update performed by all agents This work has been partially supported by AFOSR grant AF F