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We study a stochastic game framework with dynamic set of players, formodeling and analyzing their computational investment strategies in distributedcomputing. Players obtain a certain reward for solving the problem or forproviding their computational resources, while incur a certain cost based onthe invested time and computational power. We first study a scenario where thereward is offered for solving the problem, such as in blockchain mining. Weshow that, in Markov perfect equilibrium, players with cost parametersexceeding a certain threshold, do not invest; while those with cost parametersless than this threshold, invest maximal power. Here, players need not know thesystem state. We then consider a scenario where the reward is offered forcontributing to the computational power of a common central entity, such as involunteer computing. Here, in Markov perfect equilibrium, only players withcost parameters in a relatively low range in a given state, invest. For thecase where players are homogeneous, they invest proportionally to the 'rewardto cost' ratio. For both the scenarios, we study the effects of players'arrival and departure rates on their utilities using simulations and provideadditional insights.

A Stochastic Game Framework for Analyzing Computational Investment Strategies in Distributed Computing