Artificial Intelligence for Economics

Week 1: Motivating Applications of AI/ML in Economics & Politics. Basic ideas of AI/ML, formulating / deciphering real life problems using these techniques. - DBWeek 2:Optimization and Search techniques (unconstrained and constrained optimization, concept of pareto-optimality, heuristic search, game tree) - AMWeek 3:Basic Predictive Algorithms (Linear Regression, Decision Trees, Random Forests, Bayesian classifier), Neural Networks, Time Series Prediction - AMWeek 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 - DBWeek 5:Introduction to Game Theory (Cooperative and noncooperative Game Theory, dilemma problems), Bayesian Games, Mechanism Design with Economics applications - DBWeek 6:Auction Theory (Vickrey, Myerson Auctions), Case studies of auctions, advertising strategies on the internet - PDWeek 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