N. Bora Keskin

Associate Professor of Business Administration
Duke University
Fuqua School of Business
100 Fuqua Drive
Durham, NC 27708-0120
Office: A311
Phone: 919-660-1913

Email / CV / Google Scholar / LinkedIn

Research Interests

Dynamic pricing, revenue management, statistical learning, machine learning, exploration-exploitation, information asymmetry, product differentiation, applied probability

Selected Honors and Awards

• Winner, Lanchester Prize, 2019
• Finalist, INFORMS Data Mining Best Paper Competition, 2019
• Honorable Mention, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, 2018
• STAR Visitor, Netherlands Organization for Scientific Research, 2015

Publications and Papers Under Review

• Bayesian Dynamic Pricing Policies: Learning and Earning under a Binary Prior Distribution,
  Management Science, Vol. 58, No. 3, March 2012, pp. 570-586, with J.M. Harrison and A. Zeevi.
  [SSRN link]
• Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-myopic Policies,
  Operations Research, Vol. 62, No. 5, September-October 2014, pp. 1142-1167, with A. Zeevi.
  [SSRN link]
• Chasing Demand: Learning and Earning in a Changing Environment,
  Mathematics of Operations Research, Vol. 42, No. 2, May 2017, pp. 277-307, with A. Zeevi.
  — Lead Article.
  — Winner, Lanchester Prize, 2019.
  [SSRN link]
• On Incomplete Learning and Certainty-Equivalence Control,
  Operations Research, Vol. 66, No. 4, July-August 2018, pp. 1136-1167, with A. Zeevi.
  [SSRN link]
• Dynamic Selling Mechanisms for Product Differentiation and Learning,
  Operations Research, Vol. 67, No. 4, July-August 2019, pp. 1069-1089, with J. Birge.
  [SSRN link]
• Discontinuous Demand Functions: Estimation and Pricing,
  forthcoming, Management Science, with A. den Boer.
  [SSRN link]
• Competition between Two-Sided Platforms under Demand and Supply Congestion Effects,
  forthcoming, Manufacturing and Service Operations Management, with F. Bernstein and G. DeCroix.
  [SSRN link]
• Personalized Dynamic Pricing with Machine Learning: High Dimensional Features and Heterogeneous Elasticity,
  forthcoming, Management Science, with G.-Y. Ban.
  — Honorable Mention, INFORMS Junior Faculty Interest Group (JFIG) Paper Competition, 2018.
  — Finalist, INFORMS Data Mining Best Paper Competition, 2019.
  [SSRN link]
• Impact of Information Asymmetry and Limited Production Capacity on Business Interruption Insurance,
  forthcoming, Management Science, with Y.-M. Kao and K. Shang.
  [SSRN link]
• Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors,
  forthcoming, Operations Research, with J. Birge, Y. Feng, and A. Schultz.
  — Preliminary Version in the Proceedings of the 2019 ACM Conference on Economics and Computation (EC '19).
  — Featured in Chicago Booth Review.
  [SSRN link]
• Dynamic Pricing with Demand Learning and Reference Effects,
  under review, with A. den Boer.
  [SSRN link]
• Selling Quality-Differentiated Products in a Markovian Market with Unknown Transition Probabilities,
  under revision, with M. Li.
  [SSRN link]
• Data-driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment,
  under revision, with Y. Li and J. Song.
  [SSRN link]
• Markdown Policies for Demand Learning with Forward-looking Customers,
  under revision, with J. Birge and H. Chen.
  [SSRN link]
• Data-driven Clustering and Feature-based Retail Electricity Pricing with Smart Meters,
  under review, with Y. Li and N. Sunar.
  [SSRN link]
• A Geotemporal Clustering Model for COVID-19 Projection,
  under review, with X. Min and J. Song.
  [SSRN link]
• Optimal Dynamic Pricing with Demand Model Uncertainty: A Squared-Coefficient-of-Variation Rule for Learning and Earning,
  under revision.
  [SSRN link]

Working Papers

• Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility,
  with Y.-M. Kao and K. Shang.
• Learning and Earning for Congestion-Prone Service Systems,
  with P. Afèche.
• Data-driven Inventory Management with Demand Shifts,
  with X. Min and J. Song.
• To Interfere or Not to Interfere: Information Revelation and Price-Setting Incentives in Multiagent Learning Environments,
  with J. Birge, H. Chen, and A. Ward.