Economics 447—Economics of Information and Uncertainty Fall 2016

Lectures: Tuesday and Thursday 10:05–11:25am. Rutherford Physics Building, R115. Instructor: Jian Li. Office: 426 Leacock. Telephone: 514-398-3030 extension 00830. Email: [email protected] (please include “Econ 447” in subject for filtering.) Office hours: Tuesday 1:45-2:30pm and Thursday 2:30-3:15pm.

Goals of the Course This course is an upper-level undergraduate course on introduction to the microeconomic theory of uncertainty and information. The goal is to gain a deeper understanding of how uncertainty and information affect decisions and economic outcomes. We will learn, at a rigorous level, (i) how to model decision making under uncertainty and apply it to important economic problems, like insurance, production, financial market; (ii) how rational economic agents process and acquire information, (iii) how agents interact when they have different information.

Course Materials I will post lecture slides on MyCourse as the semester proceeds. They are drawn from a variety of books and academic papers. We have three textbooks for your references: Van Zandt, Timothy (2006), Introduction to the Economics of Uncertainty and Information, unpublished manuscript, URL: faculty.insead.edu/vanzandt/327files/EconInfoBook.pdf. Bikhchandani, S, J. Hirshleifer, and J. G. Riley (2013), The Analytics of Uncertainty and Information, 2nd edition, Cambridge University Press. (Chapter 5.2 only. An e-book is available in the McGill library.) Macho-Stadler, I. and D. Perez-Castrillo (2005), An Introduction to the Economics of Information, Oxford University Press, 2nd edition.

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Prerequisites The course is most suitable for students who enjoy microeconomic theory and thinking in a deep and rigorous way. You should have taken Econ 230 or Econ 250. If you did well in them, then you are more likely to enjoy this class. Besides, you also need knowledge of calculus I and II (Math 140, 141). Knowing probability theory (for example, Math 323) is a great help.

Evaluations Problem Sets: a number of problem sets will be handed out. They will be graded based on four scales: fail to submit (0), incomplete (1), complete but with major error(2), complete and no major error (3). To do well in exams, you should feel comfortable solving the problems on your own. You are encouraged to work in groups, but you must write up solutions by yourself. Copied solutions will find a grade of zero. Presentation: You are required to form groups and study carefully a paper published in an economics journal. In the later part of the course, (depending on class size) roughly each week one group will make a presentation on the paper. Each presenting group will also need to write a three-page summary of the paper that is to be shared with the class, a week before the presentation. Everyone is expected to read the summary and participate in discussion during the presentation. You can either choose a paper from the list provided below or come up with your own choice. If you choose your own paper, it has to be a theory paper related to one of the topics and from the same type of academic journals. The group members, the paper, and the date of presentation need to be approved by me. I will make further annoucement about the presentation soon. The deadline of getting approval is September 22. Midterm (in-class): October 20. If you miss the midterm due to a medical emergency (with doctor’s note), then the weight for the midterm will be transferred to the final exam. No makeup midterm will be given. Final exam: an comprehensive exam arranged by the university during the exam period. It will cover everything taught during the semester. Your overall grade will be determined by the following formula: problem sets (10%) + presentation (25%) + midterm (25%)+ final exam (40%).

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McGill Policy Statements McGill University values academic integrity. Therefore, all students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct and Disciplinary Procedures (see www.mcgill.ca/students/srr/honest/ for more information). According to Senate regulations, instructors are not permitted to make special arrangements for final exams. Please consult the calendar, section 4.7.2.1, General University Information and Regulations, at www.mcgill.ca. In accord with McGill University’s Charter of Students’ Rights, students in this course have the right to submit in English or in French any written work that is to be graded.

Tentative Outline Below is a tentative outline of topics that will be covered. Actual lectures might be subjected to changes so please check often for updates. • Introduction • The economics of uncertainty: theory (Van Zandt) 1. Expected utility theory 2. Measures of risk aversion 3. Measures of risk • The economics of uncertainty: applications (Van Zandt) 1. Portfolio choice 2. Insurance demand 3. Firm under uncertainty 4. Efficient allocation of risk 5. Capital asset pricing model • The value of information (Bikhchandani, Hirshleifer, and Riley, Chapter 5.2) • The economics of information: principal-agent models (Macho-Stadler and PerezCastrillo) 3

1. Moral hazard 2. Adverse selection 3. Signalling 4. Cheap talk (if we have time)

A List of Papers for Presentation • Risk and risk aversion: Rothschild, M. and Stiglitz, J. E. (1970), Increasing risk: I. A definition, Journal of Economic Theory, 2, 225-243. Rothschild, M. and Stiglitz, J. E. (1971), Increasing risk II: Its economic consequences, Journal of Economic Theory, 3, 66-84. • Beyond expected-utility theory: Kahneman, D. and A. Tversky, (1979), Prospect Theory: An Analysis of Decision Under Risk, Econometrica 47, 263–291. Koszegi, B. and M. Rabin (2006), A Model of Reference-Dependent Preferences, Quarterly Journal of Economics, 121, 1133-1165. • Value of Information for non-expected utility agents: Palacios-Huerta, I. (1999), The Aversion to the Sequential Resolution of Uncertainty, Journal of Risk and Uncertainty, 18, 249-269. Koszegi, B., (2003), Health anxiety and patient behavior, Journal of Health Economics, 22, 1073-1084 Hoy, M., Peter, R. and Richter, A., (2014), Take-up for genetic tests and ambiguity, Journal of Risk and Uncertainty, 48, 111-133.

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• Information disclosure: Milgrom, P. R. (1981), Good News and Bad News: Representation Theorems and Applications, The Bell Journal of Economics, 12, pp. 380-391. Grossman, S. J., (1981), The Informational Role of Warranties and Private Disclosure about Product Quality, Journal of Law and Economics, 24, pp. 461-483. • Social learning: Scharfstein, D. S. and Stein, J. C., (1990), Herd Behavior and Investment, The American Economic Review, 80, pp. 465-479. Banerjee, A. V. (1992), A Simple Model of Herd Behavior, The Quarterly Journal of Economics, pp. 797-817. Eyster, E. and Rabin, M., (2010), Naive Herding in Rich-Information Settings, American Economic Journal: Microeconomics, 2, 221-43. • Experimentation: Weitzman, M. L., (1979), Optimal Search for the Best Alternative, Econometrica, 47, pp. 641-654. Rosenberg, D., Solan, E., and Vieille, N., (2007), Social Learning in One-Arm Bandit Problems, Econometrica, 75, pp. 1591-1611. • Application: Cremer, Garicano, Prat (2007), Language and the Theory of the Firm, The Quarterly Journal of Economics, 122, pp. 373-407.

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