Bora Keskin’s main research studies operations management problems that involve decision making under uncertainty. He has developed an interest on such business problems during his consulting experience at McKinsey & Company, research experience at Nomis Solutions, and Ph.D. education at Stanford Graduate School of Business. In his dissertation, Dr. Keskin has examined the trade-off between learning and earning in dynamic pricing settings with demand model uncertainty. The starting point of his dissertation research is the estimate-and-then-optimize policies commonly used in pricing practice, and while the results of this research are applicable to many industries, his primary motivation is the financial applications in consumer lending industry, where commercial banks look for ways to learn about the price sensitivity of their customers. A major challenge that banks face is to find a fine balance between learning the demand curve and earning short-term profits, and his research addresses that problem in a broad context.
With J. M. Harrison and A. Zeevi,“Bayesian Dynamic Pricing Policies: Learning and Earning Under a Binary Prior Distribution,” Management Science, Vol. 58, No. 3, pp. 570–586 (March 2012).
With J. M. Harrison and A. Zeevi, “Dynamic Pricing with an Unknown Linear Demand Model: Asymptotically Optimal Semi-myopic Policies,” Operations Research (under review, 2012).