Evolutionary games for cooperation in open data management

Abstract

In the era of big data, open data has become a critical factor in production. To establish a stable and long-term open data management mechanism, we investigate the evolution of cooperative behaviors in open data management based on networked evolutionary games, where complex networks are used to model the interaction structure between open data managers and game theory is employed to illustrate the social dilemmas faced by these managers. In addition, we account for the dynamic nature of social dilemmas in the interactions between managers, recognizing that the dilemmas they encounter are not static but rather evolve over time. To model this, we use different game models to represent various social dilemmas and propose social dilemma transitions to capture the evolving dilemmas faced between open data managers. In our simulations, we explore how payoff parameters and transition rates influence the emergence and sustainability of cooperation across different population structures, finding that both factors play a significant role in the evolution of cooperation. Furthermore, the cooperative evolution dynamics is analyzed on a square lattice network with periodic boundaries from a microscopic perspective. We also study the influence of different patterns of social dilemma transition on the evolution of cooperation. The findings presented in this paper may offer valuable insights for open data managers, helping them make informed decisions, and fostering the evolution of cooperation within open data management systems.