Noah Gans

• Ozge Yapar, Stephen E Chick, Noah Gans (Forthcoming), Conditional Approval and Value-Based Pricing for New Health Technologies. Abstract

  Health technology assessments often inform decisions made by public payers, such as the UK’s NHS, as they negotiate the pricing of companies’ new health technologies. A common assessment mechanism compares the incremental cost effectiveness ratio (ICER) of the new health technology, relative to a standard of care, to a maximum threshold on the cost-per-QALY (CPQ). In much research and practice, these assessments may not distinguish between cost-per-patient and negotiated price, effectively ignoring the value-based-pricing principle that better health outcomes merit higher prices. Other research models this distinction but does not account for uncertainty in the ICER associated with clinical trial data that are limited in size and scope. This paper models the strategic behavior of a payer and a company as they price a new health technology, and it considers the use of conditional approval (CA) schemes whose post-marketing trials reduce ICER uncertainty before final pricing decisions are made. Analytical results suggest a very different view of the value-based pricing negotiations underlying these schemes: interim prices used during CA post-marketing trials should reflect cost-sharing for the CA scheme, not just cost-effectiveness goals for a treatment. Moreover, the types of caps on interim prices used by entities such as the UK Cancer Drugs Fund may hinder the development of new technologies and lead to suboptimal CA designs. We propose a new risk-sharing mechanism to remedy this. Numerical results, calibrated to approval data of an oncology drug, illustrate the issues in a practical setting.

• Jingxing (Rowena) Gan, Noah Gans, Gerry Tsoukalas (Under Revision), Overbooking with Bumping-Sensitive Demand. Abstract

  Overbooking can help service providers improve revenues, but it comes with costs, such as the compensation paid to “bumped” customers. The existing operations management (OM) literature focused on booking limits implicitly assumes that, given pre-defined price
  and bumping compensation, those booking limits do not affect customer demand. Empirical research in marketing however, suggests that bumping negatively affects demand, an important effect that is the focus of this paper.  With airlines as a backdrop, we formulate a model in which a service provider jointly determines price, booking limit, and bumping compensation, and customer reactions to the three induce an equilibrium demand distribution. For the traditional setting in which price and bumping compensation are exogenously fixed, we provide sufficient conditions under which bumping sensitivity leads the airline to reduce overbooking. These conditions suggest that airlines can decouple the value customers obtain from flying from their cost of being bumped.  We demonstrate that effective overbooking policies must nevertheless jointly determine bumping compensation and booking limits. We then consider auction-based compensation schemes and show that, for the airline, they dominate fixed-compensation schemes. Numerical experiments demonstrate that: the equilibrium bumping rates obtained from our model are consistent with those observed in practice; fixed-compensation policies that account for bumping-sensitive demand can significantly outperform those that do not; and auction-based policies can bring smaller but still significant additional gains over those provided by fixed-compensation schemes.  Our results support the empirical observation that bumping negatively affects customer demand and should be carefully managed, and they suggest that, contrary to traditional booking-limit controls, effective overbooking policies must jointly determine booking limits and bumping compensation. Finally, they demonstrate that recently adopted auction-based compensation schemes are a particularly effective means of managing bumping-sensitive demand.

• Stephen E Chick, Noah Gans, Ozge Yapar (2021), 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.

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• 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 Research from Noah Gans »

Current Courses

• 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.

  OIDD6120002 ( Syllabus )

  OIDD6120004 ( Syllabus )

Past 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.

• 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.

• OIDD9100 - Linear Optimization

  This course covers the basics of linear optimization. You will learn what linear programs (LPs) are, what they are used for, and how they are solved. During the course, you will also find connections between LPs and some of the basics of microeconomic theory. The mathematical prerequisites for the course are few. You should understand the basics of linear algebra, and we will review matrix operations, linear independence, and matrix inversion. While not strictly necessary, an understanding of partial derivatives will also help you to appreciate certain topics.

• OIDD9340 - Dynamic Programming

  The course goal is to provide a brief but fairly rigorous introduction to the formulation and solution of dynamic programs. Its focus is primarily methodological. We will cover discrete state space problems, over finite or infinite time horizon, with and without discounting. Structured policies and their theoretical foundation will be of particular interest. Computational methods and approximation methods will be addressed. Applications are presented throughout the course, such as inventory policies, production control, financial decisions, and scheduling.

• OIDD9890 - Topics in Oidd

  The specific content of this course varies form semester to semester, depending on student and faculty interests.

• Runner Up, William Peirskalla Best Paper Award, 2018
• Wharton MBA Core Curriculum Excellence in Teaching Award, 2010
• NSF Service Enterprise Engineering Award, Co-Principal Investigator, 2008
• Miller-Scherrerd Award for Outstanding Teaching in the Wharton MBA Core, 2004
• Senior Personnel, NSF Engineering the Service Sector Award, 2003
• Co-Principal Investigator, Alfred P. Sloan Foundation Grant, 2003
• 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
• First Prize, INFORMS George E. Nicholson Student Paper Competition, 1995

• Concerns over US Efforts to Stop Nuclear Terror, CNN - 04/14/2012
• Business-School Research: No Longer All at Sea, The Economist Online - 03/23/2011

Knowledge @ Wharton

• Overwhelmed by Too Many Choices When Shopping? Wharton Retail Expert Weighs In, Knowledge @ Wharton - 12/19/2024
• Michael Lee, Oatly North America’s EVP & Executive Creative Director, Knowledge @ Wharton - 12/19/2024
• Why Anchor Institutions Are Vital to Local Economies, Knowledge @ Wharton - 12/17/2024
• Is the American Dream of Homeownership Still Within Reach?, Knowledge @ Wharton - 12/17/2024
• Even Under Trump, AI Governance Is Here to Stay, Knowledge @ Wharton - 12/17/2024
• 2024 Ripple Rewind (Part 2), Knowledge @ Wharton - 12/17/2024
• What Are the Best Countries of 2024?, Knowledge @ Wharton - 12/16/2024
• Your Pride Could Affect Your Workplace Reputation, Knowledge @ Wharton - 12/12/2024
• Samantha Baker, VP of Commerce at theSkimm, Knowledge @ Wharton - 12/12/2024
• NFL Stats & Analysis with Ben Solak, Knowledge @ Wharton - 12/11/2024