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,
    forthcoming, M&SOM, with F. Bernstein and G. DeCroix.
    [SSRN link]
  9. Impact of Information Asymmetry and Limited Production Capacity on Business Interruption Insurance,
    forthcoming, Management Science, with Y.-M. Kao and K. Shang.
    [SSRN link]
  10. 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]
  11. Data-driven Dynamic Pricing and Ordering with Perishable Inventory in a Changing Environment,
    forthcoming, Management Science, with Y. Li and J. Song.
    [SSRN link]
  12. Dynamic Pricing with Demand Learning and Reference Effects,
    forthcoming, Management Science, 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. Markdown Policies for Demand Learning with Forward-looking Customers,
    with J. Birge and H. Chen.
    [SSRN link]
  3. Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility,
    with Y.-M. Kao and K. Shang.
    [SSRN link]
  4. 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.
    [SSRN link]
  5. The Nonstationary Newsvendor: Data-Driven Nonparametric Learning,
    with X. Min and J. Song.
    [SSRN link]
  6. To Interfere or Not to Interfere: Information Revelation and Price-Setting Incentives in Multiagent Learning Environments,
    with J. Birge, H. Chen, and A. Ward.
    [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.
    [SSRN link]

Working Papers

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