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All HBS Web
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- Faculty Publications (217)
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools...
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Keywords:
Machine Learning;
Statistics;
Econometric Analyses;
Experimental Methods;
Data Analysis;
Data Analytics;
Forecasting and Prediction;
Analytics and Data Science;
Analysis;
Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- 2021
- Working Paper
Time and the Value of Data
By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti
Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of... View Details
Keywords:
Economics Of AI;
Machine Learning;
Non-stationarity;
Perishability;
Value Depreciation;
Analytics and Data Science;
Value
Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. (Revised November 2021.)
- 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...
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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).
- 2021
- Working Paper
Hunting for Talent: Firm-Driven Labor Market Search in the United States
By: Rembrand Koning, Sharique Hasan and Ines Black
This article analyzes the phenomenon of firm-driven labor market search—or outbound recruiting—where recruiters are increasingly “hunting for talent” rather than passively relying on workers to search for and apply to job vacancies. Our research methodology leverages...
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Keywords:
Hiring;
Referrals;
Outbound Recruiting;
Labor Markets;
Selection and Staffing;
Networks;
Recruitment;
Strategy;
United States
Koning, Rembrand, Sharique Hasan, and Ines Black. "Hunting for Talent: Firm-Driven Labor Market Search in the United States." SSRN Working Paper Series, No. 3576498, September 2021.
- March 2020
- Supplement
People Analytics at Teach For America (B)
By: Jeffrey T. Polzer and Julia Kelley
This is a supplement to the People Analytics at Teach For America (A) case. In this supplement, situated one year after the A case, Managing Director Michael Metzger must decide how to apply his team's predictive models generated from the previous year’s data.
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Keywords:
Analytics;
Human Resource Management;
Data;
Workforce;
Hiring;
Talent Management;
Forecasting;
Predictive Analytics;
Organizational Behavior;
Recruiting;
Analytics and Data Science;
Forecasting and Prediction;
Recruitment;
Selection and Staffing;
Talent and Talent Management
Polzer, Jeffrey T., and Julia Kelley. "People Analytics at Teach For America (B)." Harvard Business School Supplement 420-086, March 2020.
- 2020
- Working Paper
Topic Preference Detection: A Novel Approach to Understand Perspective Taking in Conversation
By: Michael Yeomans and Alison Wood Brooks
Although most humans engage in conversations constantly throughout their lives, conversational mistakes are commonplace— interacting with others is difficult, and conversation re-quires quick, relentless perspective-taking and decision making. For example: during every...
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Keywords:
Natural Language Processing;
Interpersonal Communication;
Perspective;
Decision Making;
Perception
Yeomans, Michael, and Alison Wood Brooks. "Topic Preference Detection: A Novel Approach to Understand Perspective Taking in Conversation." Harvard Business School Working Paper, No. 20-077, February 2020.
- February 2020
- Article
Being 'Good' or 'Good Enough': Prosocial Risk and the Structure of Moral Self-regard
By: Julian Zlatev, Daniella M. Kupor, Kristin Laurin and Dale T. Miller
The motivation to feel moral powerfully guides people’s prosocial behavior. We propose that people’s efforts to preserve their moral self-regard conform to a moral threshold model. This model predicts that people are primarily concerned with whether their...
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Keywords:
Prosocial Behavior;
Moral Sensibility;
Decision Making;
Risk and Uncertainty;
Behavior;
Perception
Zlatev, Julian, Daniella M. Kupor, Kristin Laurin, and Dale T. Miller. "Being 'Good' or 'Good Enough': Prosocial Risk and the Structure of Moral Self-regard." Journal of Personality and Social Psychology 118, no. 2 (February 2020): 242–253.
- 2020
- Conference Presentation
A Performance-optimized Limb Detection Model Selectively Predicts Behavioral Responses Based on Movement Similarity
By: X. Zhao, J. De Freitas, L. Tarhan and G. A. Alvarez
- 2019
- Working Paper
Soul and Machine (Learning)
By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, improved medical diagnostics, and machine translation, but can it improve marketing decisions? It can. Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to rich media...
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Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Harvard Business School Working Paper, No. 20-036, September 2019.
- July 2019
- Case
Autonomous Vehicles: Smooth or Bumpy Ride Ahead?
By: Elie Ofek and Akhil Waghmare
In early 2019, transportation was set to undergo a major transformation with the advent of autonomous vehicles (AVs), also referred to as driverless cars, which were nearing completion from an R&D and testing phase. Yet many questions remained open regarding exactly...
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Keywords:
Transportation;
Technological Innovation;
Disruptive Innovation;
Transformation;
Technology Adoption;
Business Model;
Governing Rules, Regulations, and Reforms;
Transportation Industry;
Auto Industry
Ofek, Elie, and Akhil Waghmare. "Autonomous Vehicles: Smooth or Bumpy Ride Ahead?" Harvard Business School Case 520-008, July 2019.
- 2023
- Working Paper
The Customer Journey as a Source of Information
By: Nicolas Padilla, Eva Ascarza and Oded Netzer
In the face of heightened data privacy concerns and diminishing third-party data access,
firms are placing increased emphasis on first-party data (1PD) for marketing decisions.
However, in environments with infrequent purchases, reliance on past purchases 1PD...
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Keywords:
Customer Journey;
Privacy;
Consumer Behavior;
Analytics and Data Science;
AI and Machine Learning;
Customer Focus and Relationships
Padilla, Nicolas, Eva Ascarza, and Oded Netzer. "The Customer Journey as a Source of Information." Harvard Business School Working Paper, No. 24-035, October 2023. (Revised October 2023.)
- 2024
- Working Paper
Consumer Inertia and Market Power
By: Alexander MacKay and Marc Remer
We study the pricing decisions of firms in the presence of consumer inertia. Inertia, which can arise from habit formation, brand loyalty, and switching costs, generates dynamic pricing incentives. These incentives mediate the impact of competition on market power in...
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Keywords:
Consumer Inertia;
Market Power;
Dynamic Competition;
Demand Estimation;
Consumer Behavior;
Markets;
Performance;
Competition;
Price
MacKay, Alexander, and Marc Remer. "Consumer Inertia and Market Power." Harvard Business School Working Paper, No. 19-111, April 2019. (Revised January 2024. Direct download.)
- February 2019 (Revised August 2019)
- Case
KangaTech
By: Karim R. Lakhani, Patrick J. Ferguson, Sarah Fleischer, Jin Hyun Paik and Steven Randazzo
On a warm January afternoon in 2019, Steve Saunders, Dave Scerri, Carl Dilena, and Nick Haslam (see Exhibit 1 for biographies), co-founders of KangaTech, wrapped up the latest round of discussions about the future direction of their sports-technology start-up. Focused...
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Keywords:
Startup;
Technology Commercialization;
Prototype;
Business Startups;
Technological Innovation;
Sports;
Health;
Commercialization;
Research and Development;
Decision Making;
Growth and Development Strategy;
Technology Industry;
Sports Industry;
Health Industry;
Australia
Lakhani, Karim R., Patrick J. Ferguson, Sarah Fleischer, Jin Hyun Paik, and Steven Randazzo. "KangaTech." Harvard Business School Case 619-049, February 2019. (Revised August 2019.)
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can...
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Keywords:
Customer Management;
Targeting;
Deep Exponential Families;
Probabilistic Machine Learning;
Cold Start Problem;
Customer Relationship Management;
Customer Value and Value Chain;
Consumer Behavior;
Analytics and Data Science;
Mathematical Methods;
Retail Industry
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
- 2019
- Article
Fair Algorithms for Learning in Allocation Problems
By: Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth and Zachary Schutzman
Settings such as lending and policing can be modeled by a centralized agent allocating a scarce resource (e.g. loans or police officers) amongst several groups, in order to maximize some objective (e.g. loans given that are repaid, or criminals that are apprehended)....
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Elzayn, Hadi, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth, and Zachary Schutzman. "Fair Algorithms for Learning in Allocation Problems." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 170–179.
- Article
Faithful and Customizable Explanations of Black Box Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to trust them. To...
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Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Jure Leskovec. "Faithful and Customizable Explanations of Black Box Models." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2019).
- January 2019
- Article
Making Moves Matter: Experimental Evidence on Incentivizing Bureaucrats Through Performance-Based Postings
By: Adnan Q. Khan, Asim Ijaz Khwaja and Benjamin A. Olken
Bureaucracies often post staff to better or worse locations, ostensibly to provide incentives. Yet we know little about whether this works, with heterogeneity in preferences over postings impacting effectiveness. We propose a performance-ranked serial dictatorship...
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Keywords:
Serial Dictatorship Mechanism;
Employment;
Geographic Location;
Motivation and Incentives;
Performance
Khan, Adnan Q., Asim Ijaz Khwaja, and Benjamin A. Olken. "Making Moves Matter: Experimental Evidence on Incentivizing Bureaucrats Through Performance-Based Postings." American Economic Review 109, no. 1 (January 2019): 237–270.
- November–December 2018
- Article
Slack Time and Innovation
By: Ajay Agrawal, Christian Catalini, Avi Goldfarb and Hong Luo
Traditional innovation models assume that new ideas are developed up to the point where the benefit of the marginal project is just equal to the cost. Because labor is a key input to innovation when the opportunity cost of time is lower, such as during school breaks or...
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Agrawal, Ajay, Christian Catalini, Avi Goldfarb, and Hong Luo. "Slack Time and Innovation." Organization Science 29, no. 6 (November–December 2018): 1056–1073.
- October 2018
- Article
The Operational Value of Social Media Information
By: Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang
While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to...
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Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774.
- Article
Applying Random Coefficient Models to Strategy Research: Identifying and Exploring Firm Heterogeneous Effects
By: Juan Alcácer, Wilbur Chung, Ashton Hawk and Gonçalo Pacheco-de-Almeida
Strategy aims at understanding the differential effects of firms’ actions on performance. However, standard regression models estimate only the average effects of these actions across firms. Our paper discusses how random coefficient models (RCMs) may generate new...
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Alcácer, Juan, Wilbur Chung, Ashton Hawk, and Gonçalo Pacheco-de-Almeida. "Applying Random Coefficient Models to Strategy Research: Identifying and Exploring Firm Heterogeneous Effects." Strategy Science 3, no. 3 (September 2018): 481–553.