Tripartite evolutionary game analysis of the collective intelligence design ecosystem

Abstract

The Collective Intelligence Design Ecosystem (CIDE) is a dynamic system with multiple interests intertwined, and the utilization of matching tools provides a way for all parties to create value-added benefits. However, not everyone will adopt a matching strategy after balancing the benefits and losses. Revealing the logic of each party's choice and how this choice affects the evolution of the CIDE is beneficial for improving the sustainability of the CIDE. Therefore, this paper constructs a three-party evolutionary game system that includes designers, requesters, and the platform. Some practical insights are derived by simulating the actual operational data of the Zhubajie (ZBJ) platform. First, a modest penalty for designers can speed up the evolution of the CIDE. Second, requesters will invariably choose the task matching mechanism, but mobilizing the platform and designers to participate in matching requires setting a reasonable allocation ratio of remuneration. Third, while the platform's compensation is immaterial to the designer, it is critical to the requester. Finally, although incentives motivate designers to make matches, the absence of incentives might hasten the CIDE's convergence. These conclusions may serve as a roadmap for ensuring CIDE's stability and orderly operation.