| Crowdsourcing brings together the wisdom of a group of people to solve tasks that were previously performed by dedicated personnel,enabling the solution of many of humanity’s most difficult problems.Thanks to this unique solution model,crowdsourcing is widely used in many fields.However,the problem of crowdsourcing dilemmas among interacting agents in crowdsourcing severely limits the efficient application of the system,such as cooperation dilemma and service quality dilemma.Previous research has tended to focus on solving only one of the above dilemmas,which is not suitable for the service architectural pattern of crowdsourcing system.Therefore,this paper proposes a crowdsourcing dilemma solution based on the two-order game model to overcome these shortcomings.The specific researches are as follows:A cooperation dilemma solution is proposed for the cooperation dilemma problem between requesters and crowdsourcing platforms.The solution models interactions between requesters and the crowdsourcing platform as the first-order reputation-quality game based on the theory of network game dynamics.To promote requesters to play cooperative strategy,we formulate reputation-evaluation rules based on the transfer theory,design reputation-quality payment rules to encourage requesters to play cooperation strategy for increasing their reputation and reducing the cost,and set a maximum number of transgressors punishment mechanism to force short-sighted requesters to play cooperative strategy for obtaining long-term payoff.To promote the crowdsourcing platform to play cooperative strategy,we develop a gray index autoregressive method based on autoregressive moving average model to estimate crowdsourcing platform’s cost and design a decision-making algorithm to determine whether the crowdsourcing platform plays cooperative strategy.From the platform decision algorithm,it can be seen that the increase of request reputation helps to reverse the adoption of cooperative strategy by the crowdsourcing platform;A quality of service dilemma solution is proposed for the service quality dilemma between crowdsourcing platforms and workers.The solution models interactions between the crowdsourcing platform and workers as a the second-order quality-time game based on network game dynamics theory.To promote high quality and timely completion of tasks by workers,we present a gray scale interval estimation model to estimate the reasonable recruitment range of workers,so that the crowdsourcing platform can select high-quality workers to complete the task.Meanwhile,we use the fuzzy logic method to evaluate the service of quality level of workers,and design quality-time reward allocation method to motivate workers to complete the task in high quality and timely.And then,to prevent crowdsourcing platforms from not paying workers to complete tasks,we divide rewards paid by the platform to workers into three categories(i.e.,service of quality reward,completion time reward,and extra reward)and punish the platform that plays betray strategy with payoff,forcing the crowdsourcing platform to mutual strategy to reduce the loss;The connection between cooperative dilemma solutions and service quality dilemma solutions: Cooperative dilemma solution and quality of service dilemma solution are integrated through the requestor’s reputation-quality payment rules.Specifically,in reputation-quality payment rules,the crowdsourcing platform charges low-reputation requesters based on their reputation to force requesters to choose cooperation to reduce the cost,and promotes the crowdsourcing platform to choose cooperation through the improvement of requesters’ reputation.For high-reputation requesters,the crowdsourcing platform charges according to the quality of service,which can motivate requesters to continuously play to cooperate to obtain high-quality service,and prompt the crowdsourcing platform to play mutual strategy in the game between the crowdsourcing platform and workers to earn higher payment fees. |
