An Iterative Incentive Mechanism Design For Crowd Sensing Using Best Response Dynamics

Recent studies have modeled the incentive mechanism as a complete information game where the contributors have common knowledge. However, that assumption is not realistic in real world scenarios. In this paper, we present an incentive mechanism for CS in sealed markets in which participants have incomplete information on other participants' behavior. An iterative game framework is introduced where the solution is achieved after a number of iterations. We also address the question "do uncoordinated contributors converge to an equilibrium?. In fact, we are concerned with the convergence of the contributors to an equilibrium under a natural dynamics. The well-known best response dynamics with different rules of play is studied. In addition, a strategy for the platform to assign the optimal budget for the initial state of the market is presented. Through theoretical analyses and extensive simulations, we also evaluate the performance of the proposed incentive mechanism.