Noah Gans

Noah Gans
  • Anheuser-Busch Professor of Management Science at Wharton
  • Professor of Operations, Information and Decisions
  • Department Chair

Contact Information

  • office Address:

    3730 Walnut Street
    564 Jon M. Huntsman Hall
    Philadelphia, PA 19104

Research Interests: service operations, stochastic processes and the control of queueing systems

Links: CV


Noah Gans’s research focuses on service operations, and he has a particular interest in the management of telephone call centers. He is the Department Editor of Stochastic Models and Simulation for Management Science.  In 2010-2011, he was the President of Manufacturing and Service Operations Management Society (MSOM).  At Wharton, Noah teaches an MBA core course on Business Analytics, an MBA elective course on Service Operations, and Ph.D. courses in operations management. 

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  • Stephen E Chick, Noah Gans, Ozge Yapar (Work In Progress), The Effect of Post-Authorization Pricing Regulation on Clinical Trial Design.

    Abstract: Any estimate of a new medical treatments value that relies on clinical trial data can have signicant residual uncertainty. So-called post-marketing data, captured after the treatment has entered the market and is used by the general population, can augment clinical-trial data to better validate the safety, efficacy, and economic value of the treatment. In fact, new risk-sharing contracts, in which a treatments price is a function of post-marketing data, are under consideration around the world. We consider these new risk-sharing arrangements, explore how different examples of these arrangements may affect the design of Phase III clinical trials, and analyze what types of treatments might benefit from the new, flexible arrangements and which would not.

  • Stephen E Chick, Noah Gans, Ozge Yapar (Under Review), Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions.

    Abstract: We integrate emerging trends intended to improve clinical trial design: design for cost-effectiveness, which ensures health-economic improvement of a new intervention over the current standard intervention; adaptive design, which dynamically adjusts the sample size and allocation of patients to different interventions; and multi-arm trial design, which compares multiple interventions simultaneously. Our goal is to identify a sequential sampling policy that dynamically decides the interventions to which patients should be allocated, as well as when to stop patient recruitment, in order to maximize the expected population-level benefit minus the cost of the trial. The literature on sequential sampling develops indices that either accommodate correlation among the mean rewards of alternatives or are based on optimal stopping times that can dynamically change as samples are taken, but not both. We develop the first tractable allocation and stopping rules whose indices capture both correlation and dynamic stopping times, and our numerical experiments demonstrate the value of considering both problem elements in the context of clinical trials.

  • Alessandro Arlotto, Stephen E Chick, Noah Gans (2014), Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn, Management Science, 60 (1), pp. 110-129.

    Abstract: We study the hiring and retention of heterogeneous workers who learn over time. We show that the problem can be analyzed as an infinite-armed bandit with switching costs, and we apply results from Bergemann and Välimäki (2001) to characterize the optimal hiring and retention policy. For problems with Gaussian data, we develop approximations that allow the efficient implementation of the optimal policy and the evaluation of its performance. Our numerical examples demonstrate that the value of active monitoring and screening of employees can be substantial.

  • Nitin Bakshi, Stephen E Flynn, Noah Gans Thwarting Nuclear Terrorism Through Container Inspections.

    Abstract: Bakshi, Nitin, Steven Flynn, and Noah Gans (2010). Thwarting Nuclear Terrorism Through Container Inspections. Administrative & Regulatory Law News 37, 10-11.

  • Nitin Bakshi, Stephen E Flynn, Noah Gans (2011), Estimating the Operational Impact of Container Inspections at International Ports, Management Science, 57, 1-20.

    Abstract: A US law mandating nonintrusive imaging and radiation detection for 100% of U.S.-bound containers at international ports has provoked widespread concern that the resulting congestion would hinder trade significantly. Using detailed data on container movements, gathered from two large international terminals, we simulate the impact of the two most important inspection policies that are being considered. We find that the current inspection regime being advanced by the U.S. Department of Homeland Security can only handle a small percentage of the total load. An alternate inspection protocol that emphasizes screening—a rapid primary scan of all containers, followed by a more careful secondary scan of only a few containers that fail the primary test—holds promise as a feasible solution for meeting the 100% scanning requirement.

  • Noah Gans Service Times in Call Centers: Agent Heterogeneity and Learning with Some Operational Consequences.

    Abstract: Gans, Noah, Nan Liu, Avishai Mandelbaum, Haipeng Shen, and Han Ye (2010). Service Times in Call Centers: Agent Heterogeneity and Learning with Some Operational Consequences, James O. Berger, T. Tony Cai, and Ian Johnstone eds., IMS Collections 6, 99-123.

  • Nitin Bakshi and Noah Gans (2010), Securing the Containerized Supply Chain: Analysis of Government Incentives for Private Investment, Management Science, 56 (2), pp. 219-233.

  • Stephen E Chick and Noah Gans (2008), Economic Analysis of Simulation Selection Problems, Management Science, 55, 421 - 437.

    Abstract: Ranking and selection procedures are standard methods for selecting the best of a finite number of simulated design alternatives, based on a desired level of statistical evidence for correct selection. But the link between statistical significance and financial significance is indirect and poorly understood. This paper presents a new approach to the simulation selection problem, one that maximizes the expected net present value (NPV) of decisions made when using stochastic simulation. We provide a framework for answering these managerial questions: When does a proposed system design, whose performance is unknown, merit the time and money needed to develop a simulation to infer its performance? For how long should the simulation analysis continue before a design is approved or rejected? We frame the simulation selection problem as a “stoppable” version of a Bayesian bandit problem that treats the ability to simulate as a real option prior to project implementation. For a single proposed system, we solve a free boundary problem for a heat equation that approximates the solution to a dynamic program that finds optimal simulation project stopping times and that answers the managerial questions. For multiple proposed systems, we extend previous Bayesian selection procedures to account for discounting and simulation-tool development costs.

  • Noah Gans, George Knox, Rachel TA Croson (2007), Simple Models of Discrete Choice and Their Performance in Bandit Experiments, M&SOM, Vol 9, 383-408.

    Abstract: Recent operations management papers model customers as solving multiarmed bandit problems, positing that consumers use a particular heuristic when choosing among suppliers. These papers then analyze the resulting competition among suppliers and mathematically characterize the equilibrium actions. There remains a question, however, as to whether the original customer models on which the analyses are built are reasonable representations of actual consumer choice. In this paper, we empirically investigate how well these choice rules match actual performance as people solve two-armed Bernoulli bandit problems. We find that some of the most analytically tractable models perform best in tests of model fit. We also find that the expected number of consecutive trials of a given supplier is increasing in its expected quality level, with increasing differences, a result consistent with the models’ predictions as well as with loyalty effects described in the popular management literature.

  • Noah Gans and Sergei Savin (2007), Pricing and Capacity Rationing in Rentals with Uncertain Durations, Management Science, 390-407.

    Abstract: We consider a rental firm with two types of customers. Contract customers pay fixed, prenegotiated rental fees and expect a high quality of service. Walk-in customers have no contractual relations with the firm and are “shopping for price.” Given multiple contract and walk-in classes, the rental firm has to decide when to offer service to contract customers and what fees to charge walk-in customers for service. We formulate this rental management problem as a problem in stochastic control and characterize optimal policies for managing contract and walk-in customers. We also consider static, myopic controls that are simpler to implement, and we analytically establish conditions under which these policies perform optimally. Complementary numerical tests provide a sense of the range of systems for which myopic policies are effective.


Past Courses


    "Managing the Productive Core: Business Analytics" is a course on business analytics tools and their application to management problems. Its main topics are optimization, decision making under uncertainty, and simulation. The emphasis is on business analytics tools that are widely used in diverse industries and functional areas, including operations, finance, accounting, and marketing.


    This course covers a range of analytical methods that are useful tools for capacity management in services, and it will provide you with insights into the economics of a range of services businesses including (i) High-level planning models that account for multiple dimensions of service capacity, (ii) Low-level models of system congestion that capture the relationship between capacity choices, quality of service and, in some cases, system revenue, (iii) Statistical estimation and forecasting models to characterize key measures of future supply and demand.


    This course introduces you to the essential concepts and techniques required tounderstand and implement revenue management (RM). The need for repeated, rapid and cycles of estimation and optimization has driven the development of a set of analytical tools that are particularly well suited for RM. This course focuses on those tools.

Awards and Honors

  • Wharton MBA Core Curriculum Excellence in Teaching Award, 2010-2011
  • NSF Service Enterprise Engineering Award, Co-Principal Investigator, 2008-2010
  • Miller-Scherrerd Award for Outstanding Teaching in the Wharton MBA Core, 2004
  • Senior Personnel, NSF Engineering the Service Sector Award, 2003-2004
  • Co-Principal Investigator, Alfred P. Sloan Foundation Grant, 2003-2004
  • Mitchner Award in Quality Sciences and Quality Management, 2003
  • Meritorious Service Award, as Associate Editor of Operations Research, 2001
  • NSF CAREER Award, 1998
  • Wharton MBA Core Curriculum Cluster Teaching Award, 1997-2001
  • First Prize, INFORMS George E. Nicholson Student Paper Competition, 1995

In the News


Latest Research

Stephen E Chick, Noah Gans, Ozge Yapar (Work In Progress), The Effect of Post-Authorization Pricing Regulation on Clinical Trial Design.
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In the News

Is the Death of the PC Imminent?

Despite record low shipments of personal computers last quarter, an informal survey of Wharton faculty says that PCs -- including laptops and desktops -- are not going away any time soon.

Knowledge @ Wharton - 2013/04/23
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Awards and Honors

Wharton MBA Core Curriculum Excellence in Teaching Award 2010
All Awards