Collaborative Research: AF: Small: RUI: Data Science from Economic Foundations

Drexel University, Philadelphia PA

Investigators

Abstract

This project seeks to develop a theory of computational learning adapted to the particular challenges of economic environments. Such environments often have strategic participants who are aware that their data will be used by a platform designer to make future decisions. When participants anticipate these decisions, they may change their behavior; a consumer may wait for a coupon to purchase, and a contractor may start their price high to facilitate later negotiations. Strategic manipulation in turn necessitates new learning algorithms which interpret the data correctly and use it carefully. The applications of interest range from e-commerce and managerial decision-making, where firms optimize operations from data, to the design of the social safety net, where the selection of recipients impacts the livelihoods of the most economically vulnerable. The end goal is theoretical conclusions that can guide both designers of and regulators for such systems. The standard theory of online learning is insufficient in settings with strategic concerns. A designer now needs to control a subtle feedback loop: the way data will be used dictates what data will be provided in the first place. It is additionally important to understand which learning algorithms are stable when the learner can change their algorithm in response to the data. To tackle these issues, this project seeks to use and expand on techniques from data privacy in computer science and the theory of dynamic games in economics. The research will apply these tools in two primary ways. The first is comparative: understanding many distinct applications will highlight the structural features that help or hinder algorithmic learning. These include standard economic models from contract theory and delegation, pricing, and targeting of social benefits. The second approach is to apply the insights from the comparative analysis to design new systems. The goal is to produce new algorithms for well-studied strategic environments, or understand how these environments can be modified by a government or firm to enable learning (or mitigate its harms). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.