How Can Credit Supervision Mechanism Improve Security Crowdsourcing Ecosystem Governance: An Evolutionary Game Theory Perspective

Abstract

To address the issues of illegal trading vulnerabilities and data leakage among security personnel resulting from the traditional governance in the security crowdsourcing ecosystem, this study introduces a 'novel' credit supervision mechanism. By constructing a tripartite evolutionary game model involving the government, security crowdsourcing platforms, and security personnel, we analyze factors and mechanisms influencing the strategic choices of the three entities. The impact of different mechanisms on the system's evolutionary path is explored by numerical simulation. The results show that: 1) enhancing credibility benefits, reducing regulation costs, and strengthening governance efforts of the regulators can promote 'good behavior' of the three entities; 2) the security crowdsourcing ecosystem is able to achieve an ideal stable state under certain conditions; 3) the 'novel' credit supervision mechanism can be operated effectively based on the credit feedback mechanism; 4) improved collaborative governance effectively restrains security personnel from engaging in the illegal trading of vulnerabilities; 5) the government implementing a reasonable reward and punishment mechanism for platforms is optimal for achieving an ideal stable state. This study provides insights for enhancing the governance system in the security crowdsourcing ecosystem.