Artificial Intelligence for Economics

Overview

ABOUT THE COURSE: AI/ML are increasingly being used in various disciplines of science, technology and humanities. They have a wide scope of application in Economics, a subject which has profound influence on the workings of modern society. This course aims to inform students of the key ideas of AI/ML and how they can be applied in the domain of Economics & Finance. The course will include various use-cases based on modern research papers, while also equipping them with the necessary theoretical tools for the same. These will include relevant algorithms in Artificial Intelligence including optimization and search, Machine Learning including predictive algorithms for different types of data, Game Theory and Mechanism Design.INTENDED AUDIENCE: UG/PG/PhD students of AI/ML with interest in Economics.UG/PG/PhD students of Economics with interest in AI/MLPREREQUISITES: Basic Engineering/UG level Mathematics and StatisticsBasic Economics, AI or ML will be useful but not necessary.INDUSTRY SUPPORT:Finance and Banking, Online Retail and E-commerce, Energy, transportation and logistics, Government and Public Policy

Syllabus

Week 1: Motivating Applications of AI/ML in Economics & Politics. Basic ideas of AI/ML, formulating / deciphering real life problems using these techniques. - DB
Week 2:Optimization and Search techniques (unconstrained and constrained optimization, concept of pareto-optimality, heuristic search, game tree) - AM
Week 3:Basic Predictive Algorithms (Linear Regression, Decision Trees, Random Forests, Bayesian classifier), Neural Networks, Time Series Prediction - AM
Week 4:Causality and Attribution (Shapley value analysis of predictive models, Granger Causality, Causal Graphical Models and do-Calculus, Randomized Control Trials) - AM Put-call, Hedging - DB
Week 5:Introduction to Game Theory (Cooperative and noncooperative Game Theory, dilemma problems), Bayesian Games, Mechanism Design with Economics applications - DB
Week 6:Auction Theory (Vickrey, Myerson Auctions), Case studies of auctions, advertising strategies on the internet - PD
Week 7:Case Studies: i) Learning Theory for Economics ii) Customer Behavior Analysis for Recommender Systems - PD(i) DB(ii)
Week 8:Case studies: i) Reinforcement Learning in Finance, ii) Multi-agent simulation of economic systems, Econo-physics - AM