Noah Gans

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

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 has been Department Editor of Stochastic Models and Simulation at Management Science and the President of Manufacturing and Service Operations Management Society (MSOM).  At Wharton, Noah coordinates  the OID Department’s PhD Program, and he teaches an MBA core course on Business Analytics, as well as MBA elective courses on Analytics for Services and for Revenue Management.

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

    Abstract: We propose and analyze the first model for clinical trial design that integrates each of three important trends intending to improve the effectiveness of clinical trials that inform health-technology adoption decisions: adaptive design, which dynamically adjusts the sample size and allocation of interventions to different patients; multi-arm trial design, which compares multiple interventions simultaneously; and value-based design, which focuses on cost-benefit improvements of health interventions over a current standard of care.  Example applications are to seamless Phase II/III dose-finding trials and to trials that test multiple combinations of therapies. Our objective is to maximize the expected population health-economic benefit of health-technology adoption decisions less clinical trial costs. We show that unifying the adaptive, multi-arm, and value-based approaches to trial design can reduce the cost and duration of multi-arm trials with efficient adaptive look-ahead policies that focus on value to patients, and account for correlated rewards across arms.  Features that differentiate our approach from much other work on stochastic optimization include stopping times that balance sampling costs and the expected value of information of those samples, performance guarantees offered by new asymptotic convergence proofs, and the modeling of arms' potentially different sampling costs. Our proposed solution can be computed feasibly and can randomize  patients. The class of trials for the base model assumes that health-economic data are collected and observed quickly. Related work from Bayesian optimization can enable the further inclusion of trials with intermediate duration delays between the time of treatment initiation and observation of outcomes.

  • Jingxing (Rowena) Gan, Noah Gans, Gerry Tsoukalas (Under Revision), Overbooking with Endogenous Demand.

    Abstract: Using airlines as a backdrop, we study optimal overbooking policies with endogenous customer demand, i.e., when customers can internalize their expected cost of being "bumped". We first consider the traditional setting in which compensation for bumped passengers is fixed and booking limits are the airline's only form of control. We provide sufficient conditions under which demand endogeneity leads to lower overbooking limits in this case. We then consider the broader problem of joint control of ticket price, bumping compensation and booking limit. We show that price and compensation can act as substitutes, which reduces the general problem to a more tractable one-dimensional search for optimal overbooking compensation, and effectively allows the value of flying to be decoupled from the cost of being bumped. Finally, we extend our analysis to the case of auction-based compensation schemes, and demonstrate that these generally outperform fixed compensation schemes. Numerical experiments to gauge magnitudes suggests that fixed-compensation policies that account for demand endogeneity can significantly outperform those that do not, and that auction-based policies bring smaller but significant additional gains.

  • Stephen E Chick, Noah Gans, Ozge Yapar (Work In Progress), Risk-sharing Agreements For New Medical Treatments.

    Abstract: Any estimate of a new medical treatment’s value that relies on clinical-trial data can have significant 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 its safety, efficacy, and economic value. To better manage the risks associated with this post-trial residual uncertainty, new price updating mechanisms are under consideration around the world. We analyze these new risk-sharing arrangements.

  • 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.


All Courses

  • ESE8990 - PhD Independent Study

    For students who are studying a specific advanced subject area in electrical engineering. Students must submit a proposal outlining and detailing the study area, along with the faculty supervisor's consent, to the graduate group chair for approval. A maximum of 1 c.u. of ESE 8990 may be applied toward the MSE degree requirements. A maximum of 2 c.u.'s of ESE 8990 may be applied toward the Ph.D. degree requirements.

  • ESE9990 - Thesis/Diss Res

    For students working on an advanced research program leading to the completion of master's thesis or Ph.D. dissertation requirements.

  • OIDD6120 - Business Analytics

    "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.

  • OIDD6420 - Analytics For Services

    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. Students who have already taken OIDD 611, OIDD 612, and STAT 613 should be wellequipped for the class. Other students should have a solid understanding of elementary probability, statistics and linear programming. For questions regarding the specifics of your background, please contact the instructor.

  • OIDD6430 - Analy For Revenue Mgmt

    This course introduces you to the essential concepts and techniques required to understand 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. Prerequisites: Students who have already taken OIDD 612 and STAT 613 should be well equipped for this class. Other students should have a solid understanding of elementary probability, statistics and constrained optimization. For questions regarding the specifics of your background, please contact the instructor.

  • OIDD9010 - Oid Faculty and Research

    This course introduces first-year Operations, Information and Decisions (OID) PhD students to OID Department faculty members and their research. The course is designed to meet once a week, both in the fall and the spring, allowing most (if not all) OID faculty to present to first-year PhD students either classic or current research in their fields of expertise. The course's goals are twofold. First, it seeks to introduce first-year PhD students to OID faculty in a substantive (as opposed to social) manner and to expose students to the breadth of research conducted in the department. Second, through early exposure, the course aims to pique students' interest in the department's foundational courses in decision making, information systems, and operations management.

Awards and Honors

  • Runner Up, William Peirskalla Best Paper Award, 2018
  • 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 (Forthcoming), Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions.
All Research

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. Read More

Knowledge at Wharton - 4/23/2013
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Wharton Magazine

Final Exam

The U.S. Board of Immigration Appeals in Philadelphia has a backlog of almost 5,000 cases waiting to be processed. Which of the five attorneys would you hope it would be assigned?

Wharton Magazine - 11/16/2011