Research On Task Allocation Strategy Of Crowdsourcing Platform Based On Evolutionary Game

With the rapid development of Internet technology,crowdsourcing has become a new business model,which aggregates a large amount of wisdom to quickly and flexibly solve various problems.Currently,crowdsourcing has become an important driving force for the development of various shared network platforms.More and more individuals and enterprises choose to adopt crowdsourcing mode to save costs and improve efficiency.However,the dilemma of interaction between crowdsourcing entities has seriously constrained the long-term development of the platform,and the interests of users such as crowdsourcing workers and task publishers have always been inconsistent with the platform.Therefore,this article takes the individuals involved in the task allocation process of crowdsourcing platforms as the research object,taking human bounded rationality and self interest as the starting point,and uses the method of game theory to analyze the impact of strategic choices made by task publishers,crowdsourcing platforms,and crowdsourcing workers based on self interest psychology on their own benefits and platform benefits.Firstly,aiming at the problem of insufficient trust among users of crowdsourcing platforms,which leads to low participation enthusiasm,a task allocation strategy model for crowdsourcing platforms based on cooperative game theory is proposed with crowdsourcing platforms and task publishers as the core.Through the derivation and analysis of the cooperative game model between the two parties,it is found that the strategy choices of crowdsourcing platforms and task publishers both exhibit a certain regularity,and it is necessary for the platform to first monitor in place in order to constrain user behavior.Therefore,on this basis,further transform the crowdsourcing platform into the role of a supervisor,and then conduct a strategy selection study on it.Secondly,in response to the problems of imperfect reward and punishment and supervision mechanisms for crowdsourcing platforms and poor quality of task completion,the crowdsourcing platform in the above model is transformed into the role of a supervisor,and together with task publishers and crowdsourcing workers,a tripartite game entity is formed.A task allocation strategy model for crowdsourcing platforms based on a tripartite evolutionary game is proposed.In order to explore the impact of reward and punishment coefficients,payout costs,and other aspects on the third-party strategy selection,first calculate the payoff matrix for the outstanding outsourcing platform,task publishers,and crowdsourcing workers,and then use the evolutionary game method to derive the dynamic equation of third-party replication.The verification found that setting a reasonable reward and punishment mechanism,reasonable costs for crowdsourcing participants,and high credibility will ultimately promote the evolution of the strategy selection of game players towards the ideal direction.Finally,based on the insufficient trust of crowdsourcing platform users,imperfect rewards and punishment and monitoring mechanisms,incomplete user privacy protection,and poor quality of crowdsourcing task completion,and based on the results of both parties and thirdparty game model derivation and simulation experiment analysis,countermeasures and suggestions are proposed to improve crowdsourcing platform user trust,innovate crowdsourcing platform rewards and punishment measures,enhance crowdsourcing user security,and improve the quality of crowdsourcing platform task completion.