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(11,984)
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Show Results For
- All HBS Web
(11,984)
- People (75)
- News (2,995)
- Research (3,829)
- Events (39)
- Multimedia (353)
- Faculty Publications (2,494)
- 2013
- Article
The Strategic Fitness Process: A Collaborative Action Research Method for Developing Organizational Prototypes and Dynamic Capabilities
By: Michael Beer
Organizations underperform and sometimes fail because their leaders are unable to learn the unvarnished truth from relevant stakeholders about how the design and behavior of the organization is misaligned with its goals and strategy. The Strategic Fitness Process (SFP)... View Details
Keywords: Organization Alignment; Dynamic Capabilities; Organization Design; Organizational Prototyping; Organizational Silence; Organizational Learning; Organizational Change and Adaptation; Strategic Planning; Organizational Design
Beer, Michael. "The Strategic Fitness Process: A Collaborative Action Research Method for Developing Organizational Prototypes and Dynamic Capabilities." Journal of Organization Design 2, no. 1 (2013).
- 14 Aug 2017
- Conference Presentation
A Convex Framework for Fair Regression
By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying... View Details
Berk, Richard, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. "A Convex Framework for Fair Regression." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), August 14, 2017.
- 01 Dec 2019
- News
From Das’s Desk
A central tenet of lifelong learning today is “anywhere, anytime.” The more learning opportunities can be delivered whenever you—the user—want them, wherever you are, the way you want them, and when you most... View Details
- 01 Dec 2013
- News
Recognizing Potential
as cochair. Stevens, managing director of S-Cubed Capital and a former managing partner at Sequoia Capital, has invested in Internet companies for nearly two decades, but it was his own encounter with distance learning 30 years ago that... View Details
- December 2020
- Supplement
VIA Science (B)
By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman
Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the... View Details
Keywords: Data Analytics; Machine Learning; Artificial Intelligence; Strategy; Business Startups; AI and Machine Learning; Telecommunications Industry; Utilities Industry; United States; Japan
Alcácer, Juan, Rembrand Koning, Annelena Lobb, and Kerry Herman. "VIA Science (B)." Harvard Business School Supplement 721-368, December 2020.
- 2010
- Working Paper
Commodity Chains: What Can We Learn from a Business History of the Rubber Chain? (1870-1910)
By: Felipe Tamega Fernandes
The literature on the rubber boom applied a Dependendist view of rubber production in the Brazilian Amazon. Even though a sizable surplus was generated in the rubber chain, it was mostly appropriated by foreigners. This view is in tune with the Global Commodity Chain... View Details
Keywords: Cross-Cultural and Cross-Border Issues; Business History; Supply Chain; Manufacturing Industry; Rubber Industry; Brazil
Fernandes, Felipe Tamega. "Commodity Chains: What Can We Learn from a Business History of the Rubber Chain? (1870-1910)." Harvard Business School Working Paper, No. 10-089, April 2010.
- 2006
- Working Paper
Learning and Equilibrium As Useful Approximations: Accuracy of Prediction on Randomly Selected Constant Sum Games
By: Ido Erev, Alvin E. Roth, Robert L. Slonim and Greg Barron
- 2022
- Article
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a... View Details
Keywords: Machine Learning Models; Counterfactual Explanations; Adversarial Examples; Mathematical Methods
Pawelczyk, Martin, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, and Himabindu Lakkaraju. "Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 25th (2022).
- Working Paper
Diversification as an Adaptive Learning Process: An Empirical Study of General-Purpose and Market-Specific Technological Know-How in New Market Entry
By: Dominika Kinga Randle and Gary P. Pisano
An enduring trait of modern corporations is their propensity to diversify into multiple lines of business. Penrosian theories conceptualize diversification as a strategy to exploit a firm’s fungible, yet “untradeable,” resources and point to redeployment of... View Details
Keywords: Growth and Development Strategy; Technology Adoption; Diversification; Market Entry and Exit; Transformation
Randle, Dominika Kinga, and Gary P. Pisano. "Diversification as an Adaptive Learning Process: An Empirical Study of General-Purpose and Market-Specific Technological Know-How in New Market Entry." Harvard Business School Working Paper, No. 23-032, December 2022.
- Research Summary
Overview
I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
1. How to build... View Details
1. How to build... View Details
- Article
Towards Robust and Reliable Algorithmic Recourse
By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan
approvals), there has been growing interest in post-hoc techniques which provide recourse to affected
individuals. These techniques generate recourses under the assumption... View Details
Keywords: Machine Learning Models; Algorithmic Recourse; Decision Making; Forecasting and Prediction
Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. "Towards Robust and Reliable Algorithmic Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- July 2019 (Revised November 2019)
- Case
Osaro: Picking the Best Path
By: William R. Kerr, James Palano and Bastiane Huang
The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied to new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product—a system which would allow robotic arms... View Details
Keywords: Artificial Intelligence; Machine Learning; Robotics; Robots; Ecommerce; Fulfillment; Warehousing; AI; Startup; Technology Commercialization; Business Startups; Entrepreneurship; Logistics; Order Taking and Fulfillment; Information Technology; Commercialization; Learning; Complexity; Competition; E-commerce
Kerr, William R., James Palano, and Bastiane Huang. "Osaro: Picking the Best Path." Harvard Business School Case 820-012, July 2019. (Revised November 2019.)
- October 2023
- Article
Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years.... View Details
Keywords: Safety Regulations; Regulations; Regulatory Enforcement; Machine Learning Models; Safety; Operations; Service Operations; Production; Forecasting and Prediction; Decisions; United States
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics 15, no. 4 (October 2023): 30–67. (Profiled in the Regulatory Review.)
- 2017
- Working Paper
The Need for Speed: Effects of Uncertainty Reduction in Patenting
By: Mike Horia Teodorescu
Patents are essential in commerce to establish property rights for ideas and to give equal protection to firms that develop new technologies. Young firms especially depend on the protection of intellectual property to bring a product from concept to market. However,... View Details
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details
Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science
Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).
- Research Summary
Paper - Commodity Chains: what can we learn from a business history of the rubber chain? (1870-1910)
The literature on the rubber boom applied a Marxist/Dependendist view of rubber production in the Brazilian Amazon. Even though a sizeable surplus was generated in the rubber chain, it was mostly appropriated by foreigners. This view is in tune with the Global... View Details
- April 2017
- Case
The Future of Patent Examination at the USPTO
By: Prithwiraj Choudhury, Tarun Khanna and Sarah Mehta
The U.S. Patent and Trademark Office (USPTO) is the federal government agency responsible for evaluating and granting patents and trademarks. In 2015, the USPTO employed approximately 8,000 patent examiners who granted nearly 300,000 patents to inventors. As of April... View Details
Keywords: Machine Learning; Telework; Collaborating With Unions; Human Resources; Recruitment; Retention; Intellectual Property; Copyright; Patents; Trademarks; Knowledge Sharing; Technology Adoption; Organizational Change and Adaptation; Performance Productivity; Performance Improvement; District of Columbia
Choudhury, Prithwiraj, Tarun Khanna, and Sarah Mehta. "The Future of Patent Examination at the USPTO." Harvard Business School Case 617-027, April 2017.
- Article
Repositioning and Cost-Cutting: The Impact of Competition on Platform Strategies
By: Robert Seamans and Feng Zhu
Organizational structures are increasingly complex. In particular, more firms today operate as multi-sided platforms. In this paper, we study how platform firms use repositioning and cost-cutting in response to competition, elucidate external and internal factors that... View Details
Keywords: Platform Strategy; Repositioning; Cost-cutting; Intra-firm Learning; Multi-Sided Platforms; Cost Management; Product Positioning; Organizational Structure; Competitive Strategy; Knowledge Acquisition; Journalism and News Industry
Seamans, Robert, and Feng Zhu. "Repositioning and Cost-Cutting: The Impact of Competition on Platform Strategies." Strategy Science 2, no. 2 (June 2017): 83–99.