Yuan-Mao Kao

Yuan-Mao Kao

Asst Professor

Zicklin School of Business

Department: N. P. Loomba Dept of Mgt

Areas of expertise:

Email Address: yuan-mao.kao@baruch.cuny.edu

> View CV

Education

Ph.D., Operations Management, Duke University Durham NC

M.S., Industrial Engineering, National Taiwan University Taipei Taiwan

BBA, Business Administration, National Taiwan University Taipei Taiwan

SemesterCourse PrefixCourse NumberCourse Name
Fall 2023OPM3000Service Operations Management
Spring 2023OPM3000Service Operations Management
Fall 2022ODA75200Stochastic Optmztn: Dyn Modls
Fall 2022OPM3000Service Operations Management
Spring 2022OPM3000Service Operations Management
Spring 2022OPM3000Service Operations Management
Fall 2021OPM3000Service Operations Management

Journal Articles

Kao, Y., Keskin, N., & Shang, K. (2022). Impact of Information Asymmetry and Limited Production Capacity on Business Interruption Insurance. Management Science, 68(4). 2377-3174.

Presentations

Kao, Y., Asadpour Rahimabadi, A., & Zhuang, Y. (2024, October 21). Dynamic Pricing of Split Stays. INFORMS Annual Meeting. Seattle, WA: INFORMS.

Kao, Y., Keskin, N., & Shang, K. (2024, October 21). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. INFORMS Annual Meeting. Seattle, WA: INFORMS.

Kao, Y., Keskin, N., & Shang, K. (2024, October 21). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. INFORMS Annual Meeting. Phoenix, AZ: INFORMS.

Kao, Y., Keskin, N., & Shang, K. (2024, October 21). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. INFORMS Annual Meeting. Indianapolis, IN: INFORMS.

Kao, Y., Keskin, N., & Shang, K. (2024, October 21). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. INFORMS Annual Meeting. Anaheim, CA: INFORMS.

Kao, Y., Keskin, B., & Shang, K. (2021, June 30). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. MSOM Virtual Conference.

Kao, Y., Keskin, B., & Shang, K. (2020, November 30). Impact of Information Asymmetry and Limited Production Capacity on Business Interruption Insurance. Virtual INFORMS Annual Meeting.

Kao, Y., Keskin, B., & Shang, K. (2020, November 30). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. Virtual INFORMS Annual Meeting.

Kao, Y., Keskin, B., & Shang, K. (2019, May 31). Design and Pricing of Subscription Services under Model Uncertainty. POMS Conference. Washington, District of Columbia

Kao, Y., Keskin, B., & Shang, K. (2019, June 30). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. MSOM Conference. National University of Singapore, Singapore

Kao, Y., Keskin, B., & Shang, K. (2019, October 31). Bayesian Dynamic Pricing and Subscription Period Selection with Unknown Customer Utility. INFORMS Annual Meeting. Seattle, Washington

Kao, Y., Keskin, B., & Shang, K. (2018, July 31). Impact of Information Asymmetry and Capacity Constraints on Business Interruption Insurance. MSOM iFORM SIG Conference. Dallas, Texas

Kao, Y., Keskin, B., & Shang, K. (2018, November 30). Dynamic Pricing-and-Learning Strategies in Service Operations. INFORMS Annual Meeting. Phoenix, Arizona

Kao, Y., Keskin, B., & Shang, K. (2018, November 30). Impact of Information Asymmetry and Limited Production Capacity on Business Interruption Insurance. INFORMS Annual Meeting. Phoenix, Arizona

Kao, Y., Keskin, B., & Shang, K. (2017, October 31). The Impact of Demand Uncertainty on Business Interruption Insurance. INFORMS Annual Meeting. Houston, Texas

Research Currently in Progess

Kao, Y., Cheng, S., Wu, C., & Yang, Y.(n.d.). Price Competition in Global Operations: Considering the Effect of Parallel Trade.

Pricing a global product differently across multiple regions is a common but controversial practice. While price differentiation helps capture unique market characteristics, it also encourages parallel trade, which may lead to price competition between local business units (LBUs) of a global firm and affect overall corporate performance. We formulate this pricing problem as a two-stage game-theoretic model that consists of a single global business unit (GBU) and multiple LBUs. The GBU designs and manufactures the product and sets the transfer price for supplying the product to all LBUs, while LBUs independently decide retail prices for their respective regional markets. The parallel trade phenomenon emerges naturally when regional prices differ; thus, when LBUs are to set prices for their own regions, they also have to take into account how other LBUs might set their prices, making the pricing problem game-theoretic. Our theoretical results verify the existence of a pure-strategy Nash equilibrium game. A learning-based algorithm is then developed to find a pure-strategy Nash equilibrium for all LBUs. Numerical studies are conducted using data from the fast-moving consumer products industries. Different from most other research that considers only two or three decision makers, the proposed learning-based algorithm is computationally efficient. Even for cases with 100 decision makers, the pure-strategy Nash equilibrium can be obtained within 30 minutes. Moreover, although parallel trades are detrimental to some LBUs, the existence of parallel trade surprisingly leads to higher overall corporate profits, which is made possible through making the product available at different price points in a market. The proposed method also enables the quantitative validation of several conjectures on parallel trading practices.

Kao, Y., Keskin, N., & Shang, K.(n.d.). Bayesian Dynamic Pricing and Subscription Period Selection With Unknown Customer Utility.

We consider a service provider offering a subscription service to customers over a multi-period planning horizon. The customers decide whether to subscribe according to a utility model that represents their preferences for the service. The provider has a prior belief about the customer utility model and updates its belief based on the transaction data of new customers and the usage data of existing subscribers. The provider aims to minimize its regret, namely the expected profit loss relative to a clairvoyant who knows the customer utility model. To analyze regret, we first study the clairvoyant's full-information problem. The resulting dynamic program, however, suffers from the curse of dimensionality. We develop a customer-centric approach to resolve this issue and obtain the optimal policy for the full-information problem. This approach balances the provider's immediate and future profits from an individual customer. When the provider does not have full information, we find that the simple and commonly used certainty-equivalence policy, which learns only passively, exhibits poor performance. We illustrate that this can be due to incomplete or slow learning but can also occur because of offering a suboptimal contract with a long subscription period in the beginning. We propose an information-threshold policy that is adaptive based on the accumulated information, and the provider is guaranteed to have enough information to make decisions when crossing the threshold. We show that our policy achieves asymptotically optimal performance with its regret growing logarithmically in the planning horizon. Our results indicate that offering a long subscription period could be costly when the provider knows little about customers' usage and the service cost is highly uncertain.

TitleFunding Agency SponsorStart DateEnd DateAwarded DateTotal FundingStatus
Personalized Dynamic Pricing of Subscription Services with Learning Endogenous Customer Features PSC CUNY 5307/01/202212/31/202304/15/20223500Funded - In Progress
Honor / AwardOrganization SponsorDate ReceivedDescription
Doctoral Student FellowshipDuke University2016
Honorable MemberPhi Tau Phi Scholastic Honor Society of Taiwan2015

College

Committee NamePosition RoleStart DateEnd Date
PhD 2nd-Year Exam CommitteeCommittee MemberPresent
PhD 1st-Year Exam CommitteeCommittee MemberPresent

Professional

OrganizationPosition RoleOrganization StateOrganization CountryStart DateEnd DateAudience
Production and Operations ManagementReviewer, Journal Article1/1/2021Present
Management ScienceReviewer, Journal Article1/1/2020Present
IMFORMS Annual MeetingSession Chair1/1/2018Present
Judge for 2023 MSOM Student Paper CompetitionMember7/1/202310/18/2023International
ACM Conference on Economics and Computation (EC)Reviewer, Conference Paper1/1/202112/31/2021
MSOM ConferenceSession ChairSingapore6/1/20196/30/2019