Algorithmic Contract Theory: A Survey

TL;DR

This survey introduces the emerging field of algorithmic contract theory, exploring how economic contract concepts are applied and extended in computer science, especially in online, data-driven, and algorithmic contexts.

Contribution

It provides a computer science-friendly overview of contract theory, highlighting recent work and future research directions in algorithmic contract design and analysis.

Findings

Bridges economic contract concepts with computer science methods.

Highlights the importance of algorithmic tools in contract analysis.

Identifies future research avenues in data-driven contracts.

Abstract

A contract is an economic tool used by a principal to incentivize one or more agents to exert effort on her behalf, by defining payments based on observable performance measures. A key challenge addressed by contracts -- known in economics as moral hazard -- is that, absent a properly set up contract, agents might engage in actions that are not in the principal's best interest. Another common feature of contracts is limited liability, which means that payments can go only from the principal -- who has the deep pocket -- to the agents. With classic applications of contract theory moving online, growing in scale, and becoming more data-driven, tools from contract theory become increasingly important for incentive-aware algorithm design. At the same time, algorithm design offers a whole new toolbox for reasoning about contracts, ranging from additional tools for studying the tradeoff between simple and optimal contracts, through a language for discussing the computational complexity of contracts in combinatorial settings, to a formalism for analyzing data-driven contracts. This survey aims to provide a computer science-friendly introduction to the basic concepts of contract theory. We give an overview of the emerging field of "algorithmic contract theory" and highlight work that showcases the potential for interaction between the two areas. We also discuss avenues for future research.