**Description:**

Leadership and decision making go hand in hand. The goal of the course is to provide an introduction to the modern decision theory, in which mathematical methods of statistics and economics are integrated with findings from psychology. The course will offer tools for improving individual decision making, avoiding mistakes when taking calculated risks, and better understanding the decisions of others. We will start with the decision theory in case of the objective uncertainty, or risk, which deals with situations where we do not know the outcome of a given situation, but can accurately measure the odds. We will discuss risk attitude of individuals and then look at various examples, mainly provided by psychologists, in which the classical decision theory is violated. We will also discuss mistakes which people make when working with statistical data. Decision making is rather a process than a one-time event. Optimal timing of actions is an essential part of decision making. We will consider such examples as timing fixed size investment in a risky project, strategic default on corporate debt, optimal switching from one cash flow to another, and investment with and embedded option of default. The recent financial crisis happened partly due to inability of financial institutions to evaluate the riskiness of their investment decisions. In particular, this crisis has cast new attention on subjective uncertainty. The second part of the course will be dedicated to the subjective uncertainty, i.e., situations where we cannot infer all the information we need in order to set accurate odds statistically from existing data. We will discuss by which extent the mathematical tools of probability theory can be used in such situations. Single and multiple subjective priors models will be presented. The course will culminate with a flexible expected utility theory, which incorporates several well known models of expected utility.

**Possible Readings:**

The required text book is I. Gilboa, “Making Better Decisions: Decision Theory in Practice,” Wiley-Blackwell, 2011

Also, required readings are Lecture notes posted in due time on Canvas Additional text books:

• A useful complementary text for those who have more philosophical state of mind is M. Peterson, “An Introduction to Decision Theory,” Cambridge University Press, 2009 1 2

• A useful complementary text for those who have more analytical state of mind is K. Binmore, “Rational Decisions,” Princeton University Press, 2009

**Course Requirements:**

Course Expectations: By the end of the course, students should be able to

• understand the difference between objective and subjective uncertainty

• solve basic decision problems that involve maximization of expected utility

• solve basic problems of optimal timing of decisions

• understand various behavioral biases in decision making process

Assessment:

• regular problem sets that allow students to sharpen their analytical and quantitative skills;

• in-class group and individual assignments;

• one midterm exam (8th week of the semester) to test problem solving skills;

• two writing assignments;

• the final writing project.

Two weeks after we finish the topic on optimal timing of decisions, each student has to submit two copies a research proposal (two A4 pages single spaced), where one of the real life timing problems is to be analyzed. Proposals may include, but are not limited to, real investment opportunities or insurance contracts, entry into a new market, capital accumulation, product or project innovations, default on household or sovereign debt, exit from a declining industry, quitting an old job and accepting a new offer, natural resource extraction. Each student is supposed to clearly formulate the nature of the problem and argue its importance, discuss which model of risk is appropriate to solve the problem and sketch the method of solution. One copy will be revised by me and returned to the student with comments. Another copy should be a blind copy; it will be assigned by me to an “anonymous referee” (another student in class), and the “referee” is expected to write a detailed report on the proposal (one A4 page single spaced) with comments and suggestions a week after (s)he gets the assignment. “Referee reports” will be given to students as additional feedback and will be evaluated by me.

During the last week of the class, students will submit their final projects (five-six A4 pages single spaced), where they will present solution to the model they proposed earlier taking into account suggestions by their “referee” and my comments; moreover, students are supposed to evaluate at least one of the following modifications: (i) how conclusions of their model may change if one replaces risk with a subjective uncertainty model, or (ii) what happens in case of a multiple priors model, or (iii) what happens if one of the behavioral biases is present. All writing assignments will be judged by me both by content and writing skills. In addition to writing skills, the final project should also demonstrate strong quantitative and analytical skills.

There will be no make-up date for the midterm. In case a student misses the exam for a documented illness, emergency, a religious observance or other university-approved reason, (s)he will be given an additional “referee report” to write. The final score will be the weighted sum of the following:

• problem sets 10% (total)

• class assignments 10% (total)

• midterm 20%

• research proposal 20%

• referee report 20%

• final project 20%

I will use Plus/Minus grading for the final grade. “A” range will cover scores 80-100, “B” range will cover scores 60-80, “C” range will cover scores 40-60, “D” range will cover scores 20-40. Students who get a score less than 20 will be assigned “F”s.

**Autobiography:**

I received an MSc in Mathematics (1978) and a PhD in Mathematics (1983) from the Rostov State University (now part of the South Federal University), Rostov-on-Don, Russia. I had a 10 year teaching experience, starting as an assistant professor and ending as an associate professor, at the Don State Technical University, Rostov-on-Don-Russia. I earned my MA degree in Economics (1997) from the Central European University, Budapest Hungary, and was admitted that year into a PhD program in Economics at the University of Pennsylvania, Philadelphia, U.S.A. Upon completion of my Ph.D. in Economics in 2001, I co-authored a novel approach to optimal stopping problems that works for wide classes of L´evy processes with regime shifts and random walks, and general payoff functions. This method is more efficient than the standard technique even in the case of Gaussian processes. It can be explained to different audiences, from undergraduate students to professionals, at an appropriate level of rigorousness. Furthermore, the method provides solutions to optimal stopping problems in a more meaningful form. To be more specific, in a paper published in the American Economic Review (2004), I formulated the record setting news principles that extend and generalize Bernanke’s bad news principle.

I also co-authored the generalized Black-Scholes equation for a wide class of non-Gaussian processes and the KoBol model of asset prices, which is quite popular in finance, and subclass of which is known as the CGMY model.

The results were published in two monographs and a number of papers in peer-reviewed journals, including American Economic Review, Games and Economic Behavior, Economic Theory, International Economic Review, and Journal of Mathematical Economics. My results were also presented at numerous international conferences . I have been recently asked to write a survey paper on the frontiers of Real Options by the Editor-in-Chief of The B.E. Journal of Theoretical Economics.

While teaching at the Economics Department at UT Austin, I was awarded an NSF grant (2006, 24 months), College of Liberal Arts Research Fellowship Award 2016-17, Big XII Faculty Fellowship 2006-07, and Faculty Development Program Summer Research Assignment award, 2005 and 2002.

My current research interests are efficient option pricing methods, optimal stopping problems under risk and uncertainty, stopping time games and experimentation and learning models. My non-academic interests include reading, classical music and traveling.