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(837)
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Show Results For
- All HBS Web
(837)
- News (79)
- Research (639)
- Events (12)
- Multimedia (4)
- Faculty Publications (632)
- July–August 2023
- Article
Demand Learning and Pricing for Varying Assortments
By: Kris Ferreira and Emily Mower
Problem Definition: We consider the problem of demand learning and pricing for retailers who offer assortments of substitutable products that change frequently, e.g., due to limited inventory, perishable or time-sensitive products, or the retailer’s desire to... View Details
Keywords: Experiments; Pricing And Revenue Management; Retailing; Demand Estimation; Pricing Algorithm; Marketing; Price; Demand and Consumers; Mathematical Methods
Ferreira, Kris, and Emily Mower. "Demand Learning and Pricing for Varying Assortments." Manufacturing & Service Operations Management 25, no. 4 (July–August 2023): 1227–1244. (Finalist, Practice-Based Research Competition, MSOM (2021) and Finalist, Revenue Management & Pricing Section Practice Award, INFORMS (2019).)
- July 1990
- Background Note
Perceptual Mapping: A Manager's Guide
By: Robert J. Dolan
Describes the perceptual mapping technique in a non-technical fashion. The procedure is useful for the depiction of the structure of the market. Discusses alternative methods, presents examples of each, and shows how the maps can be used in marketing decision making. View Details
Dolan, Robert J. "Perceptual Mapping: A Manager's Guide." Harvard Business School Background Note 590-121, July 1990.
- 1999
- Other Unpublished Work
Estimating Industry Multiples
By: Malcolm Baker and R. S. Ruback
We analyze industry multiples for the S&P 500 in 1995. We use Gibbs sampling to estimate simultaneously the error specification and small sample minimum variance multiples for 22 industries. In addition, we consider the performance of four common multiples: the simple... View Details
Baker, Malcolm, and R. S. Ruback. "Estimating Industry Multiples." 1999.
- Profile
Tessa Vacher-Desvernais
analytics, but truly appreciate aesthetics. I’m very conscious of my inner tension between analytical and creative thinking.” Following her mathematical and sciences baccalaureate, she pursued liberal arts at an all-girl military boarding... View Details
- 1981
- Chapter
Risk Aversion and Solutions to Nash's Bargaining Problem
By: R. Kihlstrom, A. E. Roth and D. Schmeidler
- May 2020
- Article
Inventory Auditing and Replenishment Using Point-of-Sales Data
By: Achal Bassamboo, Antonio Moreno and Ioannis Stamatopoulos
Spoilage, expiration, damage due to employee/customer handling, employee theft, and customer shoplifting usually are not reflected in inventory records. As a result, records often report phantom inventory, i.e., units of good not available for sale. We derive an... View Details
Keywords: Shelf Availability; Inventory Record Inaccuracy; Optimal Replenishment; Retail Analytics; Performance Effectiveness; Analysis; Mathematical Methods
Bassamboo, Achal, Antonio Moreno, and Ioannis Stamatopoulos. "Inventory Auditing and Replenishment Using Point-of-Sales Data." Production and Operations Management 29, no. 5 (May 2020): 1219–1231.
- April 1979
- Article
Statistical Models of Bond Ratings: A Methodological Inquiry
By: Robert S. Kaplan and Gabriel Urwitz
Kaplan, Robert S., and Gabriel Urwitz. "Statistical Models of Bond Ratings: A Methodological Inquiry." Journal of Business (April 1979): 231–261.
- 01 Sep 2009
- News
Of Value and Values
of investment management. “I think that vote captured a powerful tension inside the students,” she remarks. “They have plenty of mathematical and financial models to support the point of view that restricting the size of an ‘investable... View Details
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Neel, Seth, and Aaron Leon Roth. "Mitigating Bias in Adaptive Data Gathering via Differential Privacy." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- June 2005 (Revised March 2006)
- Case
E Ink in 2005
By: David B. Yoffie and Barbara Mack
Explores the challenges of commercializing a bleeding-edge technology. After seven years, E Ink has spent more than $100 million to commercialize electronic ink. With business momentum picking up, but resources running out, the case examines the key trade-offs in... View Details
Keywords: Technological Innovation; Commercialization; Mathematical Methods; Consumer Products Industry; Technology Industry
Yoffie, David B., and Barbara Mack. "E Ink in 2005." Harvard Business School Case 705-506, June 2005. (Revised March 2006.)
- 18 Nov 2016
- Conference Presentation
Rawlsian Fairness for Machine Learning
By: Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
Motivated by concerns that automated decision-making procedures can unintentionally lead to discriminatory behavior, we study a technical definition of fairness modeled after John Rawls' notion of "fair equality of opportunity". In the context of a simple model of... View Details
Joseph, Matthew, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Rawlsian Fairness for Machine Learning." Paper presented at the 3rd Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), November 18, 2016.
- 2008
- Chapter
Assessing Creativity and Its Antecedents: An Exploration of the Componential Theory of Creativity
By: T. M. Amabile and Jennifer Mueller
Amabile, T. M., and Jennifer Mueller. "Assessing Creativity and Its Antecedents: An Exploration of the Componential Theory of Creativity." In Handbook of Organizational Creativity, edited by Jing Zhou and Christina E. Shalley. Lawrence Erlbaum Associates, 2008.
- March 1999 (Revised December 2001)
- Background Note
Analyzing Consumer Preferences
By: Robert J. Dolan
Presents a non-traditional description of the conjoint analysis methodology. Discusses the process by which a study is done and cites areas of application. View Details
Dolan, Robert J. "Analyzing Consumer Preferences." Harvard Business School Background Note 599-112, March 1999. (Revised December 2001.)
- 01 Jun 1997
- News
Jennifer L. Scott
year the show is reinvented," she explains. "It's a lot like a startup." Scott has always sought out new challenges and ventures. After graduating from Georgetown University's School of Foreign Service, she taught mathematics and business... View Details
Keywords: Susan Young
- Article
Who Will Vote Quadratically? Voter Turnout and Votes Cast Under Quadratic Voting
By: Louis Kaplow and Scott Duke Kominers
Who will vote quadratically in large-N elections under quadratic voting (QV)? First, who will vote? Although the core QV literature assumes that everyone votes, turnout is endogenous. Drawing on other work, we consider the representativeness of endogenously... View Details
Keywords: Voting Turnout; Paradox Of Voting; Quadratic Voting; Pivotality; Elections; Voting; Political Elections; Mathematical Methods
Kaplow, Louis, and Scott Duke Kominers. "Who Will Vote Quadratically? Voter Turnout and Votes Cast Under Quadratic Voting." Special Issue on Quadratic Voting and the Public Good. Public Choice 172, nos. 1-2 (July 2017): 125–149.
- May 2000
- Article
Maxmin Expected Utility over Savage Acts with a Set of Priors
By: Ramon Casadesus-Masanell, Peter Klibanoff and Emre Ozdenoren
This paper provides an axiomatic foundation for a maxmin expected utility over a set of priors (MMEU) decision rule in an environment where the elements of choice are Savage acts. This characterization complements the original axiomatizations of MMEU developed in a... View Details
Keywords: Uncertainty Aversion; Ambiguity; Expected Utility; Set Of Priors; Knightian Uncertainty; Decision Making; Game Theory; Risk and Uncertainty; Mathematical Methods
Casadesus-Masanell, Ramon, Peter Klibanoff, and Emre Ozdenoren. "Maxmin Expected Utility over Savage Acts with a Set of Priors." Journal of Economic Theory 92, no. 1 (May 2000): 35–65.
- 2023
- Working Paper
Design-Based Inference for Multi-arm Bandits
By: Dae Woong Ham, Iavor I. Bojinov, Michael Lindon and Martin Tingley
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire to use the available... View Details
Ham, Dae Woong, Iavor I. Bojinov, Michael Lindon, and Martin Tingley. "Design-Based Inference for Multi-arm Bandits." Harvard Business School Working Paper, No. 24-056, March 2024.
- 2024
- Working Paper
Bootstrap Diagnostics for Irregular Estimators
By: Isaiah Andrews and Jesse M. Shapiro
Empirical researchers frequently rely on normal approximations in order to summarize and communicate uncertainty about their findings to their scientific audience. When such approximations are unreliable, they can lead the audience to make misguided decisions. We... View Details
Andrews, Isaiah, and Jesse M. Shapiro. "Bootstrap Diagnostics for Irregular Estimators." NBER Working Paper Series, No. 32038, January 2024.
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details
Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li. "Distributionally Robust Causal Inference with Observational Data." Working Paper, February 2023.
- 2023
- Article
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
By: Anna P. Meyer, Dan Ley, Suraj Srinivas and Himabindu Lakkaraju
The Right to Explanation is an important regulatory principle that allows individuals to request actionable explanations for algorithmic decisions. However, several technical challenges arise when providing such actionable explanations in practice. For instance, models... View Details
Meyer, Anna P., Dan Ley, Suraj Srinivas, and Himabindu Lakkaraju. "On Minimizing the Impact of Dataset Shifts on Actionable Explanations." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 39th (2023): 1434–1444.