The Economics of No-regret Learning Algorithms

TL;DR

This paper reviews the intersection of no-regret learning algorithms and economics, exploring how these algorithms influence economic outcomes, manipulation, inference, and collusion, and providing a foundational understanding for future research.

Contribution

It synthesizes computer science and economics literature on no-regret algorithms, highlighting emerging issues like manipulation and collusion in economic settings.

Findings

No-regret algorithms can model economic agent behavior.

Manipulation and collusion are significant concerns with algorithmic actors.

The review identifies key research directions in algorithmic economics.

Abstract

A fundamental challenge for modern economics is to understand what happens when actors in an economy are replaced with algorithms. Like rationality has enabled understanding of outcomes of classical economic actors, no-regret can enable the understanding of outcomes of algorithmic actors. This review article covers the classical computer science literature on no-regret algorithms to provide a foundation for an overview of the latest economics research on no-regret algorithms, focusing on the emerging topics of manipulation, statistical inference, and algorithmic collusion.