Evolutionary Dynamics of Gig Economy Labor Strategies under Technology, Policy and Market Influence

The emergence of the modern gig economy introduces a new set of employment considerations for firms and laborers that include various trade-offs. With a game-theoretical approach, we examine the influences of technology, policy and markets on firm and worker preferences for gig labor. Theoretically, we present new conceptual extensions to the replicator equation and model oscillating dynamics in two-player asymmetric bi-matrix games with time-evolving environments, introducing concepts of the attractor arc, trapping zone and escape. While canonical applications of evolutionary game theory focus on the evolutionary stable strategy, our model assumes that the system exhibits oscillatory dynamics and can persist for long temporal intervals in a pseudo-stable state. We demonstrate how changing market conditions result in distinct evolutionary patterns across labor economies. Informing tensions regarding the future of this new employment category, we present a novel payoff framework to analyze the role of technology on the growth of the gig economy. Regarding governance, we explore regulatory implications within the gig economy, demonstrating how intervals of lenient and strict policy alter firm and worker sensitivities between gig and employee labor strategies. Finally, we establish an aggregate economic framework to explain how technology, policy and market environments engage in an interlocking dance, a balancing act, to sustain the observable co-existence of gig and employee labor strategies.

Automated summary

This research examines the impacts of technology, policy, and markets on preferences for gig labor within the gig economy using a game-theoretical approach. The study presents new conceptual extensions to the replicator equation and models oscillating dynamics in two-player asymmetric bi-matrix games. It demonstrates how changing market conditions result in distinct evolutionary patterns across labor economies and analyzes the role of technology on the growth of the gig economy through a novel payoff framework.