Making sound business decisions is a challenge for virtually all organizations. The Decision Sciences PhD program focuses on developing analytic methods to help organizations make better decisions. This program will prepare you to pursue cutting-edge research under the mentorship of our renowned Decision Sciences faculty.

The teaching and research themes in this program are similar to those of top-tier PhD programs in operations research and management science – with an emphasis on mathematical rigor and a focus on methodology. As such, research and courses in decision sciences draw from and contributes to a variety of fields, including applied probability, economics, operations research and statistics. An ideal student will have strong mathematical abilities with an interest in theoretical research in decision sciences and operations research, as well as related applications. The Decision Sciences area at Fuqua offers a productive and supportive environment for its PhD students, with outstanding resources, and easy access to leading scholars in the field.

The Decision Sciences area focuses on developing analytic methods to help make better decisions, with the research emphasis on the development and rigorous analysis of models and methodologies. The training and research themes in our PhD program are comparable to that of top PhD programs in operations research and management science with an emphasis on mathematical rigor and a focus on methodology. Prospective PhD students with strong mathematics background and a desire for theoretical research in areas of decision sciences and operations research are encouraged to apply.

The focus of the curriculum is on original research, ultimately culminating with dissertation. The necessary foundation for this program are graduate-level courses in applied probability, computer science, decision sciences, economics, mathematics, operations research and statistics. Program of study and research is customized to individual interests, background and abilities of each student.

  • Area-Specific Requirements:
    • Course requirements
    • First and second year papers
    • Qualifying exam
    • Research and teaching assistantships
  • Preliminary Exam
  • Dissertation Proposal
  • Dissertation Defense

Check out the Curriculum

Our Decision Sciences faculty conduct research in highly interdisciplinary topics, contributing to a variety of fields such as applied probability, economics, operations research and statistics. Research streams that are currently being pursued by the faculty include:

  • Applied Probability - Several of our faculty members are exploring applying probabilistic methods to the modeling, analysis, and control of stochastic systems that occur in business, economics, engineering, and statistics. These methods include stochastic dynamic programming, queuing theory, empirical processes, point processes, limit theorems, large deviations, and concentration of measure phenomena.
  • Decision Theory and Decision Analysis - We focus on modeling decisions under uncertainty, ranging from decisions faced by individuals, to those faced by large corporations, to public policy decisions. Research in the area is similarly broad, including foundational work involving axioms of rational behavior and the implications of these axioms and the structuring and representing uncertainty and of preferences.
  • High-Dimensional Estimation – With the growth of available data from emerging new technologies, new (high-dimensional) models with a large number of parameters to be estimated are needed. Such high-dimensional estimations are increasingly common in biostatistics, business, economics, and signal processing. Our faculty members are active in developing new related methodologies and research questions.
  • Mechanism Design - During the past decade researchers have extended the mechanism design methodology and found an array of uses and applications ranging from screening customers/agents to improve the system performance, or maximizing revenues in digital advertising. Our faculty develop new methodologies that handle important and practically relevant aspects of emerging complex business environments and their market participants, e.g. budget constraints, externalities, impact of networks, limited liability, simultaneous and sequential market-clearing, etc.
  • Optimization and Dynamic Programming - Our Decision Sciences faculty have contributed to the theory of optimization and dynamic programming, as well as to their use in many applied problems. We have investigated several aspects, ranging from the computational complexity of algorithms to the numerical implementation to specific applications. In particular, convex optimization, integer programming and relaxation techniques have been extensively studied and applied by members of the group.

Recent Decision Sciences PhD students who have earned degree through the Duke University Graduate School have accepted the following placements:

  • Chen Chen (2020) - University of Chicago
  • Mingliu Chen (2020) - Columbia University
  • Xinnchang Xie (2020) - Cornell University
  • Levi DeValve (2019) - University of Chicago
  • Huseyin Gurkan (2019) - ESMT
  • Yunke Mai (2019) - University of Kentucky
  • Andrew Frazelle (2018) - University of Texas, Dallas
  • Asa Palley (2016) - Indiana University
  • Changrong Deng (2014) - Analysis Group
  • Kai Wang (2014) - Electronic Arts
  • Saed Alizamir (2013) - Yale University
  • Alvaro Mendoza (2013) - Refinancia S.A.