Collaborative regulation of the algorithmic exploitation of riders in online food delivery platforms: a tripartite evolutionary game analysis

Purpose Although advancements in algorithms used by online food delivery platforms have improved the efficiency of delivery activities for riders, the exploitative behavior of the platforms’ algorithms has significantly increased the risk of traffic accidents for riders. This paper explores how a collaborative regulation approach involving both the government and riders within the online food delivery system can regulate algorithmic exploitation by platforms and protect the legitimate rights and interests of riders. Design/methodology/approach By introducing the government as a regulatory actor and riders as key stakeholders overseeing the exploitative behavior of online food delivery platform algorithms, a tripartite evolutionary game model involving the platform, government, and riders was constructed. Using the stability theorem of differential equations, the conditions necessary to achieve an optimal equilibrium of the platform’s algorithm were analyzed from the standpoint of the actors’ respective interests. Following this analysis, numerical simulations based on actual platform data were conducted to further validate the game model’s results, and specific countermeasures and policy recommendations were proposed. Findings When the additional profits gained from algorithmic exploitation exceed the losses incurred by the platform under the collaborative regulation of the government and riders, proactive government regulation and riders’ rights assertion become ineffective, and the platform consistently opts for the algorithmic exploitation strategy. Proactive government regulation can incentivize the platform to adopt a balanced algorithm strategy, reducing the necessity for riders to assert their rights. When the cost of government intervention is lower than the benefits derived from enhancing the government’s public perception, and when government penalties sufficiently exceed the platform’s additional profits from this exploitative behavior, the platform’s exploitative behavior can be effectively curbed. Specifically, reducing the costs of government intervention, minimizing societal welfare losses caused by algorithmic exploitation, increasing penalties for platforms’ exploitative algorithms, and reasonably setting economic compensation for riders can expedite the platform’s adoption of a balanced algorithm strategy. Originality/value Research on the governance of algorithmic exploitation in online food delivery platforms from the perspective of balancing stakeholders’ interests is relatively limited. This study, combining evolutionary game analysis with numerical simulation, identifies the necessary conditions and influencing factors for achieving the optimal equilibrium of platform algorithms. The findings provide theoretical and policy guidance for key stakeholders, such as the government and riders, to participate in regulating exploitative practices, contributing to the sustainable growth of the online food delivery industry.