THE RED QUEEN in the DASHBOARD: CO-EVOLUTIONARY DYNAMICS of ALGORITHMIC CONTROL and WORKER RESISTANCE

As gig economy platforms increasingly rely on algorithmic management to optimize labor supply, workers are developing sophisticated counter-strategies to regain autonomy. Conventional microeconomic models often treat these interactions as static principal-agent problems. This paper adopts an Evolutionary Game Theory (EGT) framework to analyze the relationship between algorithmic control and worker behavior as a "Red Queen" dynamic—a co-evolutionary arms race where neither side reaches a stable static equilibrium. We model a population of workers choosing between compliance and algorithmic gaming (e.g., coordinated log-offs) against a platform adjusting its surveillance strictness. We demonstrate that strict algorithmic control paradoxically increases the evolutionary fitness of coordinated resistance, leading to persistent limit cycles rather than a stable compliant workforce. These findings suggest that "algorithmic unions" may evolve organically as a survival strategy against over-optimized management systems.