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


  1. 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]
  2. 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]
  3. 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]
  4. On Incomplete Learning and Certainty-Equivalence Control,
    Operations Research, Vol. 66, No. 4, July-August 2018, pp. 1136-1167, with A. Zeevi.
    [SSRN link]
  5. 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]
  6. Discontinuous Demand Functions: Estimation and Pricing,
    Management Science, Vol. 66, No. 10, October 2020, pp. 4516-4534, with A. den Boer.
    [SSRN link]
  7. Personalized Dynamic Pricing with Machine Learning: High-Dimensional Features and Heterogeneous Elasticity,
    Management Science, Vol. 67, No. 9, September 2021, pp. 5549-5568, 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]
  8. Competition between Two-Sided Platforms under Demand and Supply Congestion Effects,
    M&SOM, Vol. 23, No. 5, September-October 2021, pp. 1043-1061, with F. Bernstein and G. DeCroix.
    [SSRN link]
  9. Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors,
    Operations Research, Vol. 69, No. 6, November-December 2021, pp. 1746-1766, 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]
  10. Data-driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment,
    Management Science, Vol. 68, No. 3, March 2022, pp. 1938-1958, with Y. Li and J. Song.
    [SSRN link]
  11. Impact of Information Asymmetry and Limited Production Capacity on Business Interruption Insurance,
    Management Science, Vol. 68, No. 4, April 2022, pp. 2824-2841, with Y.-M. Kao and K. Shang.
    [SSRN link]
  12. Dynamic Pricing with Demand Learning and Reference Effects,
    Management Science, Vol. 68, No. 10, October 2022, pp. 7112-7130, with A. den Boer.
    [SSRN link]

Papers Under Review or Revision

  1. Selling Quality-Differentiated Products in a Markovian Market with Unknown Transition Probabilities,
    with M. Li.
    [SSRN link]
  2. To Interfere or Not to Interfere: Information Revelation and Price-Setting Incentives in Multiagent Learning Environments,
    with J. Birge, H. Chen, and A. Ward.
    Featured in Chicago Booth Review.
    Featured in China Business Knowledge.
    [SSRN link]
  3. Markdown Policies for Demand Learning with Forward-looking Customers,
    with J. Birge and H. Chen.
    Featured in Chicago Booth Review.
    [SSRN link]
  4. Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility,
    with Y.-M. Kao and K. Shang.
    [SSRN link]
  5. Data-driven Clustering and Feature-based Retail Electricity Pricing with Smart Meters,
    with Y. Li and N. Sunar.
    — Winner, INFORMS Data Mining Best Paper Competition, 2020.
    — Winner, INFORMS Service Science Best Paper Competition, 2022.
    — Invited to the 2022 Conference on AI, ML, & Business Analytics at Harvard Business School.
    [SSRN link]
  6. The Nonstationary Newsvendor: Data-Driven Nonparametric Learning,
    with X. Min and J. Song.
    [SSRN link]
  7. A Geotemporal Clustering Model for COVID-19 Projection,
    with X. Min and J. Song.
    [SSRN link]
  8. Optimal Dynamic Pricing with Demand Model Uncertainty: A Squared-Coefficient-of-Variation Rule for Learning and Earning,
    [SSRN link]
  9. The Blockchain Newsvendor: Value of Freshness Transparency and Smart Contracts,
    with C. Li and J. Song.
    — Winner, Best Paper Award, Digital Supply Chain and Supplier Diversity Conference, 2022.
    — Winner, Fan Favorite Award, YinzOR Conference, 2022.
    [SSRN link]

Works in Progress

  1. Deep Learning for Visual Advertising on Digital Platforms,
    with Y. Li and J. Song.
  2. Learning and Earning for Congestion-Prone Service Systems,
    with P. Afèche.
  3. Dynamic Learning for Joint Pricing, Advertising and Inventory Management,
    with H. Gürkan and R. Parker.