Game Dynamics And Strategy Optimization Based On Zero-Determinant Theory

Recently,iterative game is a model for collaboration between socioeconomic and biological systems.For iterative prisoner’s dilemma,the discovery of Zero-Determinant(ZD)Strategy is of great significance.The discovery of this strategy has prompted many scholars to explore how different ZD strategies evolve under different evolutionary conditions.With the rapid development of globalization and the progress of network technology,a new working mode of network-crowdsourcing has been developed rapidly.Many companies have used crowdsourcing to achieve some innovative results.Now,crowdsourcing has become a cheap and effective way to get a solution to a task,but at the same time,crowdsourcing faces many challenges.For example,under incomplete information,how to choose appropriate strategies to maximize their own benefits due to the greed and selfishness of participants.A player taking zero-determinant strategies could enforce a linear relationship between his payoff and opponents’ payoff,thereby control the total excepted payoff of opponents.On the other hand,the winner-take-all(WTA)behaviors are universal in society.This paper focuses on the evolution dynamics of ZD strategies in WTA game,where we explore how the focused player controls the sum of the opponent’s payoff under the dynamic rule of WTA.Numerical simulations also illustrate the effect of parameters on total payoff.The crowdsourcing system refers to the practice of a company or organization outsourcing work tasks that used to be performed by workers in a voluntary manner to non-specific(and often large)mass networks.Usually the requester will publish the task on the crowdsourcing platform and provide a certain reward for the task.The workers will choose to accept the task according to their own situations and submit the solutions within the specified time,in which workers have incomplete informations(that is,workers only know their own strategies and do not know the decisions of other workers).However,selfish workers in crowdsourcing systems will submit solutions of different quality in order to maximize payoffs.Therefore,the following question arises: what strategies will the selfish workers choose to maximize their payoffs in such a scenario? In this paper,we propose an optimal method based on zero-determinant strategies(ZD strategies)and analyze the optimal decision of workers in crowdsourcing system according to the winner-take-allbehavior.The numerical simulations illustrate the performance of different strategies and the effects of the parameters on the earnings of the focused worker.