A Computer Science Toolbox for Modern Economic Theory
ECON 2070
Subject & Catalog Number
Course Information
Description
How can we use tools from machine learning theory to design better auctions? Can we use cryptography to better implement matching mechanisms? And how should we approach formally proving that welfare in Nash equilibria for many games is not "much worse" than in the social optimum? This course explores the application of diverse ideas, techniques, and solution aesthetics from theoretical computer science to derive meaningful new insights into classic economic problems. The three main themes are approximation theorems (including bounding the loss in revenue or welfare due to lack of information, to unpriced externalities, or to restricting the functional form of mechanisms); various notions of complexity (including computational complexity, communication complexity, and machine-learning sample complexity); and cryptographic tools (including cryptographic commitments, multiparty computation, and zero-knowledge proofs). Economic applications mostly include analysis of equilibria, pricing, and mechanism design. No prior knowledge in computer science is required.
Course Notes
This course is also offered as CS 2370. Students may not take both for credit.
Recommended Prep
Graduate-level Economic Theory at the Level of MWG
Available for Harvard Cross Registration
