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 framework to analyze the relationship between algorithmic control and worker behavior as a “Red Queen” dynamic—a co-evolutionary arms race in which the system does not converge to a stable static equilibrium. We model a population of workers choosing between compliance and algorithmic gaming (e.g., coordinated log-offs) against a platform that adjusts its surveillance strictness. Within a Lotka–Volterra–type replicator structure, the interior equilibrium is characterized as a center, generating path-dependent, non-convergent cyclical trajectories. We show that strict algorithmic control can increase the evolutionary fitness of coordinated resistance, producing persistent, neutrally stable oscillatory dynamics in the form of families of closed orbits around an interior center. These findings suggest that “algorithmic unions” may emerge organically as adaptive responses within ongoing, non-convergent platform–worker interactions.