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(657)
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Show Results For
- All HBS Web
(657)
- News (145)
- Research (427)
- Events (20)
- Multimedia (12)
- Faculty Publications (303)
- Forthcoming
- Article
Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by... View Details
Keywords: AI and Machine Learning; Analytics and Data Science; Forecasting and Prediction; Digital Marketing
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation." Management Science (forthcoming). (Pre-published online March 24, 2025.)
- September 2017
- Article
It Doesn't Hurt to Ask: Question-asking Increases Liking
By: K. Huang, M. Yeomans, A.W. Brooks, J. Minson and F. Gino
Conversation is a fundamental human experience, one that is necessary to pursue intrapersonal and interpersonal goals across myriad contexts, relationships, and modes of communication. In the current research, we isolate the role of an understudied conversational... View Details
Keywords: Question-asking; Liking; Responsiveness; Conversation; Natural Language Processing; Interpersonal Communication; Behavior
Huang, K., M. Yeomans, A.W. Brooks, J. Minson, and F. Gino. "It Doesn't Hurt to Ask: Question-asking Increases Liking." Journal of Personality and Social Psychology 113, no. 3 (September 2017): 430–452.
- March 2008
- Article
Deferred Acceptance Algorithms: History, Theory, Practice, and Open Questions
By: Alvin E. Roth
The deferred acceptance algorithm proposed by Gale and Shapley (1962) has had a profound influence on market design, both directly, by being adapted into practical matching mechanisms, and, indirectly, by raising new theoretical questions. Deferred acceptance... View Details
Keywords: History; Market Design; Labor; System; Practice; Performance; Theory; Boston; New York (city, NY)
Roth, Alvin E. "Deferred Acceptance Algorithms: History, Theory, Practice, and Open Questions." Prepared for Gale's Feast: A Day in Honor of the 85th Birthday of David Gale International Journal of Game Theory 36, nos. 3-4 (March 2008): 537–569.
- 2007
- Working Paper
Deferred Acceptance Algorithms: History, Theory, Practice, and Open Questions
By: Alvin E. Roth
The deferred acceptance algorithm proposed by Gale and Shapley (1962) has had a profound influence on market design, both directly, by being adapted into practical matching mechanisms, and, indirectly, by raising new theoretical questions. Deferred acceptance... View Details
- 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
- December 2019
- Article
Costly Concessions: An Empirical Framework for Matching with Imperfectly Transferable Utility
By: Alfred Galichon, Scott Duke Kominers and Simon Weber
We introduce an empirical framework for models of matching with imperfectly transferable utility and unobserved heterogeneity in tastes. Our framework allows us to characterize matching equilibrium in a flexible way that includes as special cases the classic fully- and... View Details
Keywords: Sorting; Matching; Marriage Market; Intrahousehold Allocation; Imperfectly Transferable Utility; Marketplace Matching; Mathematical Methods
Galichon, Alfred, Scott Duke Kominers, and Simon Weber. "Costly Concessions: An Empirical Framework for Matching with Imperfectly Transferable Utility." Journal of Political Economy 127, no. 6 (December 2019): 2875–2925.
- 1999
- Article
Effects of Instructional Style on Problem-Solving Creativity
By: A. M. Ruscio and T. M. Amabile
This study sought to determine the impact of 2 differing instructional approaches on creative problem-solving performance. Eighty-two college students completed a novel structure-building task after receiving algorithmic instruction (providing a rote, step-by-step... View Details
Ruscio, A. M., and T. M. Amabile. "Effects of Instructional Style on Problem-Solving Creativity." Creativity Research Journal 12, no. 4 (1999): 251–266.
- 2021
- Working Paper
First Law of Motion: Influencer Video Advertising on TikTok
By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging... View Details
Keywords: Influencer Advertising; Video Advertising; Computer Vision; Machine Learning; Advertising; Online Technology
Yang, Jeremy, Juanjuan Zhang, and Yuhan Zhang. "First Law of Motion: Influencer Video Advertising on TikTok." Working Paper, March 2021.
- Article
Adaptive Machine Unlearning
By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees... View Details
Gupta, Varun, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. "Adaptive Machine Unlearning." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- August 2021 (Revised November 2024)
- Case
Intenseye: Powering Workplace Health and Safety with AI (A)
By: Michael W. Toffel and Youssef Abdel Aal
Intenseye was a Turkey-based technology startup that deployed machine learning algorithms to workplace camera feeds in order to identify unsafe worker actions and unsafe working conditions, in order to help improve worker safety. The case describes how Intenseye’s... View Details
Keywords: Privacy; Product Development; Operations; Technological Innovation; Value Creation; Production; Distribution; Safety; Risk and Uncertainty; Technology Industry; Manufacturing Industry; Distribution Industry; Turkey; Middle East; United States
Toffel, Michael W., and Youssef Abdel Aal. "Intenseye: Powering Workplace Health and Safety with AI (A)." Harvard Business School Case 622-037, August 2021. (Revised November 2024.)
- September 2020 (Revised February 2024)
- Teaching Note
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and... View Details
- 25 Aug 2018
- News
Are Superstar Firms and Amazon Effects Reshaping the Economy?
- 14 Nov 2016
- News
Why Big Data Isn’t Enough
- 2025
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers
By: Matthew DosSantos DiSorbo, Kris Ferreira, Maya Balakrishnan and Jordan Tong
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). How can humans and algorithms work together to make... View Details
DosSantos DiSorbo, Matthew, Kris Ferreira, Maya Balakrishnan, and Jordan Tong. "Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers." Working Paper, May 2025.
- October 2019
- Article
Making Sense of Recommendations
By: Michael Yeomans, Anuj Shah, Sendhil Mullainathan and Jon Kleinberg
Computer algorithms are increasingly being used to predict people's preferences and make recommendations. Although people frequently encounter these algorithms because they are cheap to scale, we do not know how they compare to human judgment. Here, we compare computer... View Details
Keywords: Recommender Systems; Artificial Intelligence; Interpretability; Information Technology; Forecasting and Prediction; Decision Making; Attitudes
Yeomans, Michael, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg. "Making Sense of Recommendations." Journal of Behavioral Decision Making 32, no. 4 (October 2019): 403–414.
- November 2018
- Case
Sportradar (A): From Data to Storytelling
By: Ramon Casadesus-Masanell, Karen Elterman and Oliver Gassmann
In 2013, the Swiss sports data company Sportradar debated whether to expand from its core business of data provision to bookmakers into sports media products. Sports data was becoming a commodity, and in the future, sports leagues might reduce their dependence on... View Details
Keywords: Sports Data; Data; Sport; Sportradar; Football; Soccer; Gambling; Betting; Betting Markets; Statistics; Odds; Live Data; Bookmakers; Betradar; Visualization; Integrity; Monitoring; Gaming; Streaming; 2013; St.Gallen; Algorithm; Mathematical Modeling; Carsten Koerl; Betandwin; Bwin; Wagering; Probability; Sports; Analytics and Data Science; Mathematical Methods; Games, Gaming, and Gambling; Transition; Strategy; Media; Sports Industry; Technology Industry; Information Technology Industry; Media and Broadcasting Industry; Europe; Switzerland; Asia; Austria; Germany; England
Casadesus-Masanell, Ramon, Karen Elterman, and Oliver Gassmann. "Sportradar (A): From Data to Storytelling." Harvard Business School Case 719-429, November 2018.
Edward McFowland III
Edward McFowland III is an Assistant Professor in the Technology and Operations Management Unit at Harvard Business School. He teaches the first-year TOM course in the required curriculum.
Professor McFowland’s research interests – which lie at the... View Details
- 25 Sep 2015
- Blog Post
4 Challenges All Early-Stage Startups Face
During our first year at HBS, my classmates and I took the opportunity to build cleverlayover, a flight search engine that uses advanced algorithms to find flights hundreds of dollars cheaper than any other search engine. We were able to... View Details
- 2020
- Working Paper
Machine Learning for Pattern Discovery in Management Research
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a... View Details
Keywords: Machine Learning; Theory Building; Induction; Decision Trees; Random Forests; K-nearest Neighbors; Neural Network; P-hacking; Analytics and Data Science; Analysis
Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised June 2020.)