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121 - 130 of 145 results for: ECON

ECON 284: Simplicity and Complexity in Economic Theory (CS 360)

Technology has enabled the emergence of economic systems of formerly inconceivable complexity. Nevertheless, some technology-related economic problems are so complex that either supercomputers cannot solve them in a reasonable time, or they are too complex for humans to comprehend. Thus, modern economic designs must still be simple enough for humans to understand, and must address computationally complex problems in an efficient fashion. This topics course explores simplicity and complexity in economics, primarily via theoretical models. We will focus on recent advances. Key topics include (but are not limited to) resource allocation in complex environments, communication complexity and information aggregation in markets, robust mechanisms, dynamic matching theory, influence maximization in networks, and the design of simple (user-friendly) mechanisms. Some applications include paired kidney exchange, auctions for electricity and for radio spectrum, ride-sharing platforms, and the diffusion of information. Prerequisites: Econ 203 or equivalent.
Terms: Spr | Units: 3-5

ECON 285: Matching and Market Design

This is an introduction to market design, intended mainly for second year PhD students in economics (but also open to other graduate students from around the university and to undergrads who have taken undergrad market design). It will emphasize the combined use of economic theory, experiments, and empirical analysis to analyze and engineer market rules and institutions. In this first quarter we will pay particular attention to matching markets, which are those in which price doesn't do all of the work, and which include some kind of application or selection process. We will also cover some of the basics of auction theory, with a particular emphasis on its connections to matching. In recent years market designers have participated in the design and implementation of a number of marketplaces, and the course will emphasize the relation between theory and practice, for example in the design of labor market clearinghouses for American doctors, school choice programs in a growing number of more »
This is an introduction to market design, intended mainly for second year PhD students in economics (but also open to other graduate students from around the university and to undergrads who have taken undergrad market design). It will emphasize the combined use of economic theory, experiments, and empirical analysis to analyze and engineer market rules and institutions. In this first quarter we will pay particular attention to matching markets, which are those in which price doesn't do all of the work, and which include some kind of application or selection process. We will also cover some of the basics of auction theory, with a particular emphasis on its connections to matching. In recent years market designers have participated in the design and implementation of a number of marketplaces, and the course will emphasize the relation between theory and practice, for example in the design of labor market clearinghouses for American doctors, school choice programs in a growing number of American cities (including New York and Boston), the allocation of organs for transplantation, online advertising auctions, and the market for transportation. Various forms of market failure will also be discussed. Assignment: One final paper. The objective of the final paper is to study an existing market or an environment with a potential role for a market, describe the relevant market design questions, and evaluate how the current market design works and/or propose improvements on the current design.
Terms: Aut | Units: 2-5

ECON 286: Game Theory and Economic Applications

Aims to provide a solid basis in game-theoretic tools and concepts, both for theorists and for students focusing in other fields. Technical material will include solution concepts and refinements, potential games, supermodular games, repeated games, reputation, and bargaining models. The class will also address some foundational issues, such as epistemic and evolutionary modeling.Prerequisite: 203 or consent of instructor.
Terms: Aut | Units: 3-5
Instructors: Carroll, G. (PI)

ECON 287: Mechanism and Market Design (MS&E 365)

Primarily for doctoral students. Focus on quantitative models dealing with sustainability and related to operations management. Prerequisite: consent of instructor. May be repeated for credit.
Terms: Win | Units: 3 | Repeatable for credit
Instructors: Ashlagi, I. (PI)

ECON 289: Advanced Topics in Game Theory and Information Economics

Topics course covering a variety of game theory topics with emphasis on market design, such as matching theory and auction theory. Final paper required. Prerequisites: ECON 285 or equivalent. ECON 283 recommended.
Last offered: Winter 2020

ECON 290: Multiperson Decision Theory

Students and faculty review and present recent research papers on basic theories and economic applications of decision theory, game theory and mechanism design. Applications include market design and analyses of incentives and strategic behavior in markets, and selected topics such as auctions, bargaining, contracting, and computation.
Terms: Spr | Units: 3
Instructors: Wilson, R. (PI)

ECON 291: Social and Economic Networks

Synthesis of research on social and economic networks by sociologists, economists, computer scientists, physicists, and mathematicians, with an emphasis on modeling. Includes methods for describing and measuring networks, empirical observations about network structure, models of random and strategic network formation, as well as analyses of contagion, diffusion, learning, peer influence, games played on networks, and networked markets.
Terms: Spr | Units: 3-5

ECON 292: Quantitative Methods for Empirical Research

This is an advanced course on quantitative methods for empirical research. Students are expected to have taken a course in linear models before. In this course I will discuss modern econometric methods for nonlinear models, including maximum likelihood and generalized method of moments. The emphasis will be on how these methods are used in sophisticated empirical work in social sciences. Special topics include discrete choice models and methods for estimating treatment effects.
Terms: Aut | Units: 3-5

ECON 293: Machine Learning and Causal Inference

This course will cover statistical methods based on the machine learning literature that can be used for causal inference. In economics and the social sciences more broadly, empirical analyses typically estimate the effects of counterfactual policies, such as the effect of implementing a government policy, changing a price, showing advertisements, or introducing new products. This course will review when and how machine learning methods can be used for causal inference, and it will also review recent modifications and extensions to standard methods to adapt them to causal inference and provide statistical theory for hypothesis testing. We consider causal inference methods based on randomized experiments as well as observational studies, including methods such as instrumental variables and those based on longitudinal data. We consider the estimation of average treatment effects as well as personalized policies. Lectures will focus on theoretical developments, while classwork will consis more »
This course will cover statistical methods based on the machine learning literature that can be used for causal inference. In economics and the social sciences more broadly, empirical analyses typically estimate the effects of counterfactual policies, such as the effect of implementing a government policy, changing a price, showing advertisements, or introducing new products. This course will review when and how machine learning methods can be used for causal inference, and it will also review recent modifications and extensions to standard methods to adapt them to causal inference and provide statistical theory for hypothesis testing. We consider causal inference methods based on randomized experiments as well as observational studies, including methods such as instrumental variables and those based on longitudinal data. We consider the estimation of average treatment effects as well as personalized policies. Lectures will focus on theoretical developments, while classwork will consist primarily of empirical applications of the methods. Prerequisite: Prior coursework in basic observational study methods for causal inference, including instrumental variables, fixed effects modeling, regression discontinuity designs, etc. Students should be comfortable reading and engaging with empirical research in economics and related fields.
Terms: Spr | Units: 3

ECON 299: Practical Training

Students obtain employment in a relevant research or industrial activity to enhance their professional experience consistent with their degree programs. At the start of the quarter, students must submit a one page statement showing the relevance of the employment to the degree program along with an offer letter. Submit this documentation to the Econ professor, who has agreed to the student enrolling in their Econ 299 section. At the end of the quarter, a three page final report must be supplied documenting work done and relevance to degree program. May be repeated for credit.
Terms: Aut, Win, Spr, Sum | Units: 1-10 | Repeatable for credit
Instructors: Abramitzky, R. (PI) ; Admati, A. (PI) ; Amador, M. (PI) ; Amemiya, T. (PI) ; Arora, A. (PI) ; Athey, S. (PI) ; Attanasio, O. (PI) ; Auclert, A. (PI) ; Bagwell, K. (PI) ; Baron, D. (PI) ; Bekaert, G. (PI) ; Bernheim, B. (PI) ; Bettinger, E. (PI) ; Bhattacharya, J. (PI) ; Bloom, N. (PI) ; Bocola, L. (PI) ; Boskin, M. (PI) ; Brady, D. (PI) ; Bresnahan, T. (PI) ; Bulow, J. (PI) ; Canellos, C. (PI) ; Carroll, G. (PI) ; Chandrasekhar, A. (PI) ; Chaudhary, L. (PI) ; Chetty, R. (PI) ; Clerici-Arias, M. (PI) ; Cogan, J. (PI) ; David, P. (PI) ; Diamond, R. (PI) ; Duffie, D. (PI) ; Duggan, M. (PI) ; Dupas, P. (PI) ; Einav, L. (PI) ; Fafchamps, M. (PI) ; Falcon, W. (PI) ; Fetter, D. (PI) ; Fitzpatrick, M. (PI) ; Fuchs, V. (PI) ; Garber, A. (PI) ; Gentzkow, M. (PI) ; Gould, A. (PI) ; Goulder, L. (PI) ; Greif, A. (PI) ; Haak, D. (PI) ; Haber, S. (PI) ; Hall, R. (PI) ; Hamilton, J. (PI) ; Hammond, P. (PI) ; Hanson, W. (PI) ; Hanushek, E. (PI) ; Harris, D. (PI) ; Hartmann, W. (PI) ; Henry, P. (PI) ; Hickman, B. (PI) ; Hong, H. (PI) ; Hope, N. (PI) ; Horvath, M. (PI) ; Hoxby, C. (PI) ; Imbens, G. (PI) ; Jackson, M. (PI) ; Jagolinzer, A. (PI) ; Jarosch, G. (PI) ; Jones, C. (PI) ; Jost, J. (PI) ; Judd, K. (PI) ; Kehoe, P. (PI) ; Kessler, D. (PI) ; Klenow, P. (PI) ; Kochar, A. (PI) ; Kojima, F. (PI) ; Kolstad, C. (PI) ; Krueger, A. (PI) ; Kuran, T. (PI) ; Kurlat, P. (PI) ; Kurz, M. (PI) ; Lambert, N. (PI) ; Larsen, B. (PI) ; Lau, L. (PI) ; Lazear, E. (PI) ; Leeson, R. (PI) ; Levin, J. (PI) ; MaCurdy, T. (PI) ; Malmendier, U. (PI) ; McClellan, M. (PI) ; McKeon, S. (PI) ; Meier, G. (PI) ; Milgrom, P. (PI) ; Miller, G. (PI) ; Morten, M. (PI) ; Naylor, R. (PI) ; Nechyba, T. (PI) ; Niederle, M. (PI) ; Noll, R. (PI) ; Owen, B. (PI) ; Oyer, P. (PI) ; Pencavel, J. (PI) ; Persson, P. (PI) ; Piazzesi, M. (PI) ; Pistaferri, L. (PI) ; Polinsky, A. (PI) ; Qian, Y. (PI) ; Reiss, P. (PI) ; Richards, J. (PI) ; Roberts, J. (PI) ; Romano, J. (PI) ; Romer, P. (PI) ; Rosenberg, N. (PI) ; Rossi-Hansberg, E. (PI) ; Rosston, G. (PI) ; Roth, A. (PI) ; Royalty, A. (PI) ; Rozelle, S. (PI) ; Sargent, T. (PI) ; Schaffner, J. (PI) ; Schneider, M. (PI) ; Segal, I. (PI) ; Sharpe, W. (PI) ; Shotts, K. (PI) ; Shoven, J. (PI) ; Singleton, K. (PI) ; Skrzypacz, A. (PI) ; Sorkin, I. (PI) ; Sweeney, J. (PI) ; Taylor, J. (PI) ; Tendall, M. (PI) ; Topper, M. (PI) ; Vytlacil, E. (PI) ; Wacziarg, R. (PI) ; Weingast, B. (PI) ; Williams, H. (PI) ; Wilson, R. (PI) ; Wolak, F. (PI) ; Wolitzky, A. (PI) ; Wright, G. (PI) ; Wright, M. (PI) ; Yotopoulos, P. (PI)
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