Date of Award
Spring 5-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computational and Data Sciences
First Advisor
David Porter
Second Advisor
Stephen Rassenti
Third Advisor
Ryan French
Abstract
This dissertation presents a reinforcement learning-based approach to modeling and optimizing decision-making in professional basketball free agency and related economic environments. A Markov Decision Process (MDP) framework is introduced to capture the strategic interactions of NBA teams bidding for free agents under budgetary and roster constraints. To address computational scalability challenges, a reinforcement learning (RL) environment is developed, leveraging Proximal Policy Optimization (PPO) to approximate optimal policies for team decision-making.
Empirical results demonstrate that the RL agent successfully learns strategic bidding behavior that aligns with dynamic programming benchmarks in simplified settings while scaling effectively to larger, intractable environments. The study further extends reinforcement learning applications to a job scheduling problem, where an agent must allocate resources to maximize returns under uncertainty, and to a capacity-constrained Cournot market, where firms strategically invest to maximize long-term profitability.
Findings indicate that reinforcement learning serves as a powerful tool for approximating optimal strategies in complex, non-tractable markets. This work contributes to the growing intersection of computational economics, market design, and artificial intelligence by showcasing the effectiveness of reinforcement learning in decision-support systems for economic and strategic environments.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
J. Cohn, "Reinforcement learning, modeling markets, and professional basketball free agency," Ph.D. dissertation, Chapman University, Orange, CA, 2025. https://doi.org/10.36837/chapman.000687
Included in
Artificial Intelligence and Robotics Commons, Behavioral Economics Commons, Data Science Commons, Design of Experiments and Sample Surveys Commons, Labor Economics Commons
