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Show Results For
-
All HBS Web
(11,760)
- People (75)
- News (2,847)
- Research (3,656)
- Events (31)
- Multimedia (331)
- Faculty Publications (2,325)
- 2004
- Working Paper
The Recovery Window: Organizational Learning Following Ambiguous Threats in High-Risk Organizations
By: Amy C. Edmondson, Michael A. Roberto, Richard M.J. Bohmer, Erika M. Ferlins and Laura R. Feldman
- February 2024
- Teaching Note
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
Teaching Note for HBS Case No. 224-029. Levi Strauss & Co. (“Levi Strauss”) partnered with the IT services company Wipro to incorporate more sophisticated methods, such as machine learning, into their financial forecasting process starting in 2018. The decision to...
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- November 1992
- Article
Executive Succession and Organization Outcomes in Turbulent Environments: An Organizational Learning Approach
By: Michael Tushman, B. Virany and E. Romanelli
Tushman, Michael, B. Virany, and E. Romanelli. "Executive Succession and Organization Outcomes in Turbulent Environments: An Organizational Learning Approach." Organization Science 3, no. 4 (November 1992): 72–92.
- 26 Apr 2020
- Other Presentation
Towards Modeling the Variability of Human Attention
By: Kuno Kim, Megumi Sano, Julian De Freitas, Daniel Yamins and Nick Haber
Children exhibit extraordinary exploratory behaviors hypothesized to contribute to the building of models of their world. Harnessing this capacity in artificial systems promises not only more flexible technology but also cognitive models of the developmental processes...
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Keywords:
Exploratory Learning Behaviors;
Modeling;
Artificial Intelligence;
AI and Machine Learning
Kim, Kuno, Megumi Sano, Julian De Freitas, Daniel Yamins, and Nick Haber. "Towards Modeling the Variability of Human Attention." In Bridging AI and Cognitive Science (BAICS) Workshop. 8th International Conference on Learning Representations (ICLR), April 26, 2020.
- January 2024 (Revised February 2024)
- Case
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
The case examines Levi Strauss’ journey in implementing machine learning and AI into its financial forecasting process. The apparel company partnered with the IT company Wipro in 2017 to develop a machine learning algorithm that could help Levi Strauss forecast its...
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Keywords:
Investor Relations;
Forecasting;
Machine Learning;
Artificial Intelligence;
Apparel;
Corporate Finance;
Forecasting and Prediction;
AI and Machine Learning;
Digital Transformation;
Apparel and Accessories Industry;
United States
Egan, Mark. "Data-Driven Denim: Financial Forecasting at Levi Strauss." Harvard Business School Case 224-029, January 2024. (Revised February 2024.)
- May 2022 (Revised July 2022)
- Supplement
AWS and Amazon SageMaker (C): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (C): The Commercialization of Machine Learning Services." Harvard Business School Supplement 622-087, May 2022. (Revised July 2022.)
- May 2022
- Supplement
AWS and Amazon SageMaker (B): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (B): The Commercialization of Machine Learning Services." Harvard Business School Supplement 622-086, May 2022.
- June, 2021
- Article
Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19
By: Edward L. Glaeser, Ginger Zhe Jin, Benjamin T. Leyden and Michael Luca
During the COVID-19 pandemic, states issued and then rescinded stay-at-home orders that restricted mobility. We develop a model of learning by deregulation, which predicts that lifting stay-at-home orders can signal that going out has become safer. Using restaurant...
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Keywords:
COVID-19;
Lockdown;
Reopening;
Impact;
Coronavirus;
Public Health Measures;
Mobility;
Health Pandemics;
Governing Rules, Regulations, and Reforms;
Consumer Behavior
Glaeser, Edward L., Ginger Zhe Jin, Benjamin T. Leyden, and Michael Luca. "Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19." Journal of Regional Science 61, no. 4 (June, 2021): 696–709.
- April 2024
- Article
A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification
By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),...
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Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
- 01 Sep 2020
- News
Happy New Year: The HBS Alumni Board's Newest Members Look to the Future
to learn from and serve as a resource to the Dean, faculty members, staff, and students of the School, representing the perspectives and interests of HBS alumni worldwide, and...
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- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed...
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Keywords:
Machine Learning;
Econometric Analysis;
Instrumental Variable;
Random Forest;
Causal Inference;
AI and Machine Learning;
Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- 2014
- Working Paper
Learning from the Kursk Submarine Rescue Failure: the Case for Pluralistic Risk Management
By: Anette Mikes and Amram Migdal
The Kursk, a Russian nuclear-powered submarine, sank in the relatively shallow waters of the Barents Sea in August 2000 during a naval exercise. Numerous survivors were reported to be awaiting rescue, and within a week, an international rescue party gathered at...
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Mikes, Anette, and Amram Migdal. "Learning from the Kursk Submarine Rescue Failure: the Case for Pluralistic Risk Management." Harvard Business School Working Paper, No. 15-003, July 2014.
- Web
10 Things I Learned During My First Month in the MS/MBA: Engineering Sciences Program - MBA
Blog Blog MBA Voices Filter Results Arrow Down Arrow Up Read posts from Author Alumni Author Career and Professional Development Staff Author HBS Community Author HBS Faculty Author MBA Admissions Author MBA Students Topics Topics 1st Year (RC) 2+2 Program 2nd Year...
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- Summer 2020
- Article
Venture Capital's Role in Financing Innovation: What We Know and How Much We Still Need to Learn
By: Josh Lerner and Ramana Nanda
Venture capital is associated with some of the most high-growth and influential firms in the world. Academics and practitioners have effectively articulated the strengths of the venture model. At the same time, venture capital financing also has real limitations in its...
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Lerner, Josh, and Ramana Nanda. "Venture Capital's Role in Financing Innovation: What We Know and How Much We Still Need to Learn." Journal of Economic Perspectives 34, no. 3 (Summer 2020): 237–261.
- May 1998
- Article
Learning in High Stakes Ultimatum Games: An Experiment in the Slovak Republic
By: R. Slonim and A. E. Roth
Slonim, R., and A. E. Roth. "Learning in High Stakes Ultimatum Games: An Experiment in the Slovak Republic." Econometrica 63, no. 3 (May 1998): 569–596.
- 2017
- Working Paper
Learning by Doing: The Value of Experience and the Origins of Skill for Mutual Fund Managers
By: Elisabeth Kempf, Alberto Manconi and Oliver Spalt
Learning by doing matters for professional investors. We develop a new methodology to show that mutual fund managers outperform in industries where they have obtained experience on the job. The key to our identification strategy is that we look "inside" funds and...
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Kempf, Elisabeth, Alberto Manconi, and Oliver Spalt. "Learning by Doing: The Value of Experience and the Origins of Skill for Mutual Fund Managers." SSRN Working Paper Series, No. 2124896, May 2017.