Pseudo-Competitive Games and Algorithmic Pricing
Algorithmic pricing is increasingly a staple of e-commerce platform operations; however, while such data-driven pricing techniques are known to work well in non-strategic environments, their performance in competitive settings remains poorly understood. To this end, we investigate market outcomes that may arise when multiple competing firms deploy local price experimentation algorithms while treating their market environment as a black-box. For price-competition games induced by a broad class of well-validated customer behavior models, we demonstrate that price trajectories resulting from natural local learning dynamics may converge to outcomes in which firms can experience unbounded losses in revenue compared to the best price equilibrium. We moreover design a novel learning algorithm to address this concern.
This work falls under a broader range of questions in people-centric operations, wherein new markets and platforms fail to fully harness advances in optimization and AI due to inadequately accounting for the utilities of agents, firms, and society as a whole. Such questions arise both in competitive settings, as discussed above, but also in collaborative settings; I will highlight this in the latter part of my talk by briefly discussing my work on the design of multi-modal transportation systems.
Chamsi Hssaine is a final-year Ph.D. candidate in the School of Operations Research and Information Engineering at Cornell University, where she is advised by Professor Sid Banerjee. She graduated magna cum laude from Princeton University in 2016, with a B.S. in Operations Research and Financial Engineering. Her research centers around algorithm and incentive design for smart societal systems, with a focus on incorporating more realistic models of behavior under incentives, and better understanding the effect of policy decisions on stakeholders. Chamsi was selected for the 2020 Rising Stars in EECS workshop at UC Berkeley, as well as the 2020 Rising Scholars conference at the Stanford Graduate School of Business. In 2019, she was a visitor at the Simons Institute for the program on Online and Matching-Based Market Design. Her paper "Real-Time Approximate Routing for Smart Transit Systems" (joint with Sid Banerjee, Noémie Périvier, and Samitha Samaranayake) was a finalist for the 2021 INFORMS Minority Issues Forum Paper Competition.