Strategic investments in distributed computing: A stochastic game perspective

International audience; We study a stochastic game with a dynamic set of players, for modeling and analyzing their computational investment strategies in distributed computing. Players obtain a certain reward for solving a problem, while incurring a certain cost based on the invested time and computational power. We present our framework while considering a contemporary application of blockchain mining, and show that the framework is applicable to certain other distributed computing settings as well. For an in-depth analysis, we consider a particular yet natural scenario where the rate of solving the problem is proportional to the total computational power invested by the players. We show that, in Markov perfect equilibrium, players with cost parameters exceeding a certain threshold, do not invest; while those with cost parameters less than this threshold, invest maximal power. We arrive at an interesting conclusion that the players need not have information about the system state as well as each others' parameters, namely, cost parameters and arrival/departure rates. With extensive simulations and insights through mean field approximation, we study the effects of players' arrival/departure rates and the system parameters on the players' utilities.