Dynamic problem solving in economics and business

2023/2024

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Economics

Course type:

Elective course

When:

3 year, 2 module

Course Syllabus

Abstract

The discipline is elective and introduces students to decision-making tools based on dynamic programming methods. Dynamic programming (DP) is a powerful tool for solving a wide class of sequential decision-making problems. DP methods enables computing optimal decision rules that specify the best possible decision in different situation. Dynamic programming is used in areas where we have problems that can be divided into smaller sub-problems, and their solutions are used to solve larger problems, including economics and business. In economics, it is used to solve optimization problems in finance, production, resource allocation, and to provide a rigorous, but not too complicated, treatment of optimal growth models in infinite discrete time horizon. In business it can be applied to decision-making for management of a company. Many product-based companies like to evaluate their applicants' basic problem-solving abilities. And optimization problems are the must-haves for these interviews since they feature complex and large solution spaces.

Learning Objectives

• By the end of the course, students will understand the basics of dynamic programming and will be able to define and solve problems with intertemporal trade-offs.

Expected Learning Outcomes

• Students know the standard methods for the solution of problems with intertemporal trade-offs.
• Students know the problems with intertemporal trade-offs, which require to weight costs of the current decisions against the benefits of the future outcomes, have extremely wide applicability in various disciplines of economic science.
• Students understand the basics of dynamic programming, a method of solving problems with intertemporal trade-offs.
• Students can see how dynamic programming can be applied to a wide range of real-life situations, and then move to some of the most popular (macro) economic applications of the method.
• Students are familiar with real-life applications, particularly how dynamic programming techniques can be used in the airline industry or revenue management applications.

Course Contents

• 1. Introduction to Dynamic Programming, Part 1.
• 2. Introduction to Dynamic Programming, Part 2.
• 3. DP in (Macro)Economics, Part 1.
• 4. DP in (Macro)Economics, Part 2
• 5. Numerical Methods.
• 6. Introduction to the problem of optimal control.
• 7. DP Applications.

Assessment Elements

• Homework Sample Task
• Midterm/Exam Sample Tasks
• In-class Participation
• Final Exam

Interim Assessment

• 2023/2024 2nd module
  0.5 * Final Exam + 0.15 * Homework Sample Task + 0.1 * In-class Participation + 0.25 * Midterm/Exam Sample Tasks

Bibliography

Recommended Core Bibliography

• Adda, J., & Cooper, R. W. (2003). Dynamic Economics : Quantitative Methods and Applications. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=100029
• Bertsekas, D. P. (2017). Stable Optimal Control and Semicontractive Dynamic Programming.
• Chiang, A. C. (2012). Elements of dynamic optimization. Waveland Press.
• Kenneth L. Judd. (1998). Numerical Methods in Economics. The MIT Press.

Recommended Additional Bibliography

• Chow, G. C. (1997). Dynamic Economics : Optimization by the Lagrange Method. New York: Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=176360
• Elements of Dynamic Optimization, 327 p., Chiang, A. C., 1992
• Elements of dynamic optimization, Chiang, A. C., 1992
• Numerical methods in economics, Judd, K. L., 1998
• Numerical methods in economics, Judd, K.L., 1998
• The theory and practice of revenue management, Talluri, K. T., 2004