Kevin Hu, a member of the Class of 2020 at Dartmouth College majored in Quantitative Social Science (QSS). All QSS students complete research projects as part of their undergraduate curricula, and Kevin wrote an honors thesis under the supervision of Feng Fu, Assistant Professor of Mathematics and member of the QSS Steering Committee. The title of Kevin's thesis is, "Oscillating Replicator Dynamics with Attractor Arcs: A Game-Theoretical Exploration of Technology, Policy and Market Influences on Gig Economy Labor Strategies."
Over the course of the 2020-21 academic year, Kevin worked with Professor Fu and their work was recently published in Games. According to Professor Fu, Kevin was the driving force behind their article, which examines the influence of technology, policy, and markets on firm and worker preferences for gig labor using an innovative framework of evolutionary game theory. One reviewer of the article wrote that, "[It] is a very important contribution to... applied economics." Both Professor Fu and Kevin are tremendously grateful for the support they received from the QSS honors thesis program, which provides a stimulating and nurturing platform for students to explore cutting-edge research.
Kevin is currently a product manager at Microsoft working on Cloud and AI Cybersecurity. According to Kevin, writing and revising his QSS Honors Thesis "was an exciting and incredibly rewarding project for me. I'm really grateful for Professor Fu's mentorship and encouragement throughout the whole project and for the QSS department's support when I was writing my thesis last year. Looking back on my time at Dartmouth, the QSS thesis was one of the most memorable and rewarding experiences."
The abstract of the published article, which is titled, "Evolutionary Dynamics of Gig Economy Labor Strategies under Technology, Policy and Market Influence," is as follows:
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.