Georgia Tech SCL Georgia Tech SCL Georgia Tech SCL

Title:

Algorithm and Incentive Design for Sustainable Resource Allocation: Beyond
Classical Fisher Markets
 

Abstract: 

Technological advances have opened new avenues for designing market
mechanisms for resource allocation, from enhancing resource allocation eDiciency with widespread data availability to enabling real-time algorithm implementation. While these technological advancements hold significant promise, they also introduce new societal challenges pertaining to equity, privacy, data uncertainty, and security that existing market mechanisms often fail to address. My research develops data-driven and online learning algorithms and incentive schemes to address these challenges of traditional market mechanisms, thereby advancing the science and practice of market design for sustainable and society-aware resource allocation. 

In this talk, I focus on addressing data uncertainty and privacy issues in the context of Fisher markets, a classical framework for fair resource allocation where the problem of computing equilibrium prices relies on complete information of user attributes, which are typically unavailable in practice. Motivated by this practical limitation, we study a modified online incomplete information variant of Fisher markets, where users with privately known utility and budget parameters, drawn i.i.d. from a distribution, arrive sequentially. In this novel market, we establish the limitations of static pricing and design dynamic posted-price algorithms with improved guarantees. Our main result is a posted-price algorithm that solely
relies on revealed preference (RP) feedback, i.e., observations of user consumption,
achieving the best-known guarantees for first-order algorithms in the RP setting while providing a regret analysis of a fairness-promoting logarithmic objective, unlike typical nonnegative and bounded eDiciency-promoting objectives in online learning. 

Link to Paper: https://arxiv.org/pdf/2205.00825

Bio: 

Devansh Jalota is a PhD candidate in Computational and Mathematical Engineering at Stanford University, where he is a Stanford Interdisciplinary Graduate Fellow. His research develops data-driven learning algorithms and incentive schemes to advance the science and practice of market design for sustainable resource allocation, with a particular focus on applications in future mobility systems and electricity markets. Prior to joining Stanford, he received his bachelor’s in applied mathematics and civil engineering at UC Berkeley.