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Show Results For
- All HBS Web
(853)
- News (79)
- Research (640)
- Events (14)
- Multimedia (4)
- Faculty Publications (634)
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- November 1990 (Revised August 1996)
- Background Note
Sampling and Statistical Inference
An introduction to sampling and statistical inference that covers the main concepts (confidence intervals, tests of statistical significance, choice of sample size) that are needed in making inferences about a population mean or percent. Includes discussion of problems... View Details
Schleifer, Arthur, Jr. "Sampling and Statistical Inference." Harvard Business School Background Note 191-092, November 1990. (Revised August 1996.)
- January 2008 (Revised April 2008)
- Teaching Note
Pilgrim Bank (C): Statistics Review with Data Desk
By: Frances X. Frei
Teaching Note for [602103]. View Details
- 2008
- Working Paper
Unravelling in Two-Sided Matching Markets and Similarity of Preferences
By: Hanna Halaburda
This paper investigates the causes and welfare consequences of unravelling in two-sided matching markets. It shows that similarity of preferences is an important factor driving unravelling. In particular, it shows that under the ex-post stable mechanism (the mechanism... View Details
Halaburda, Hanna. "Unravelling in Two-Sided Matching Markets and Similarity of Preferences." Harvard Business School Working Paper, No. 09-068, November 2008.
- May 2007 (Revised March 2008)
- Background Note
Basic Techniques for the Analysis of Customer Information Using Excel 2003: A Step-by-Step Approach
By: Francisco de Asis Martinez-Jerez
Provides a set of easy, step-by-step guides for some analytical techniques that are useful in the analysis of cases discussed in the course "Competing and Winning Through Customer Information (CWCI)". The instructions that follow use datasets from three of the cases in... View Details
Martinez-Jerez, Francisco de Asis. "Basic Techniques for the Analysis of Customer Information Using Excel 2003: A Step-by-Step Approach." Harvard Business School Background Note 107-073, May 2007. (Revised March 2008.)
- 2009
- Chapter
Identity as a Variable
By: Rawi Abdelal, Yoshiko M. Herrera, Alastair Iain Johnston and Rose McDermott
Abdelal, Rawi, Yoshiko M. Herrera, Alastair Iain Johnston, and Rose McDermott. "Identity as a Variable." In Measuring Identity, edited by Rawi Abdelal, Yoshiko M. Herrera, Alastair Iain Johnston, and Rose McDermott, 17–32. Cambridge: Cambridge University Press, 2009.
- June 1999
- Article
Projections onto Efficient Frontiers: Theoretical and Computational Extensions to DEA
By: F. Frei and P. Harker
Frei, F., and P. Harker. "Projections onto Efficient Frontiers: Theoretical and Computational Extensions to DEA." Journal of Productivity Analysis 11, no. 3 (June 1999): 275–300.
- May 1990
- Background Note
Conjoint Analysis: A Manager's Guide
By: Robert J. Dolan
Presents a non-technical description of the conjoint analysis methodology. Discusses the process by which such a study is done and cites areas of application. View Details
Dolan, Robert J. "Conjoint Analysis: A Manager's Guide." Harvard Business School Background Note 590-059, May 1990.
- 2002
- Chapter
Contributions of Applied Systems Analysis to International Negotiation
By: Howard Raiffa
- April 1990
- Case
Clark Material Handling Group-Overseas: Brazilian Product Strategy (A&B) (Condensed)
By: Robert J. Dolan
Assumes some knowledge of conjoint analysis. Permits analysis of basic results and dynamic market simulations in one class session. View Details
Dolan, Robert J. "Clark Material Handling Group-Overseas: Brazilian Product Strategy (A&B) (Condensed)." Harvard Business School Case 590-081, April 1990.
- 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.
- 1997
- Chapter
Applications of Option-Pricing Theory: Twenty-Five Years Later
By: Robert C. Merton
Merton, Robert C. "Applications of Option-Pricing Theory: Twenty-Five Years Later." In Les Prix Nobel 1997, edited by Tore Frängsmyr. Stockholm: Nobel Foundation, 1997. (Reprinted in American Economic Review, June 1998.)
- spring 1987
- Article
Second-Sourcing and the Experience Curve: Price Competition in Defense Procurement
By: James J. Anton and Dennis A. Yao
We examine a dynamic model of price competition in defense procurement that incorporates the experience curve, asymmetric cost information, and the availability of a higher cost alternative system. We model acquisition as a two-stage process in which initial production... View Details
Anton, James J., and Dennis A. Yao. "Second-Sourcing and the Experience Curve: Price Competition in Defense Procurement." RAND Journal of Economics 18, no. 1 (spring 1987): 57–76. (Harvard users click here for full text.)
- 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.)
- 2024
- Article
A Universal In-Place Reconfiguration Algorithm for Sliding Cube-Shaped Robots in Quadratic Time
By: Zachary Abel, Hugo A. Akitaya, Scott Duke Kominers, Matias Korman and Frederick Stock
In the modular robot reconfiguration problem we are given n cube-shaped modules (or "robots") as well as two configurations, i.e., placements of the n modules so that their union is face-connected. The goal is to find a sequence of moves that reconfigures the modules... View Details
Abel, Zachary, Hugo A. Akitaya, Scott Duke Kominers, Matias Korman, and Frederick Stock. "A Universal In-Place Reconfiguration Algorithm for Sliding Cube-Shaped Robots in Quadratic Time." Proceedings of the International Symposium on Computational Geometry (SoCG) 40th (2024): 1:1–1:14.
- 2023
- Working Paper
Causal Interpretation of Structural IV Estimands
By: Isaiah Andrews, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan and Jesse M. Shapiro
We study the causal interpretation of instrumental variables (IV) estimands of nonlinear, multivariate structural models with respect to rich forms of model misspecification. We focus on guaranteeing that the researcher's estimator is sharp zero consistent, meaning... View Details
Keywords: Mathematical Methods
Andrews, Isaiah, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan, and Jesse M. Shapiro. "Causal Interpretation of Structural IV Estimands." NBER Working Paper Series, No. 31799, October 2023.
- Article
Learning Models for Actionable Recourse
By: Alexis Ross, Himabindu Lakkaraju and Osbert Bastani
As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely... View Details
Ross, Alexis, Himabindu Lakkaraju, and Osbert Bastani. "Learning Models for Actionable Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- Mar 2020
- Conference Presentation
A New Analysis of Differential Privacy's Generalization Guarantees
By: Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Moshe Shenfeld
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and... View Details
Jung, Christopher, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld. "A New Analysis of Differential Privacy's Generalization Guarantees." Paper presented at the 11th Innovations in Theoretical Computer Science Conference, Seattle, March 2020.
- Article
Active World Model Learning with Progress Curiosity
By: Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber and Daniel Yamins
World models are self-supervised predictive models of how the world evolves. Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal... View Details
Kim, Kuno, Megumi Sano, Julian De Freitas, Nick Haber, and Daniel Yamins. "Active World Model Learning with Progress Curiosity." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).