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      • February 2021
      • Tutorial

      Assessing Prediction Accuracy of Machine Learning Models

      By: Michael Toffel and Natalie Epstein
      This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and... View Details
      Keywords: Statistics; Experiments; Forecasting and Prediction; Performance Evaluation; AI and Machine Learning
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      Toffel, Michael, and Natalie Epstein. Assessing Prediction Accuracy of Machine Learning Models. Harvard Business School Tutorial 621-706, February 2021. (Click here to access this tutorial.)
      • 2021
      • Working Paper

      Entrepreneurial Learning and Strategic Foresight

      By: Aticus Peterson and Andy Wu
      We study how learning by experience across projects affects an entrepreneur's strategic foresight. In a quantitative study of 314 entrepreneurs across 722 crowdfunded projects supplemented with a program of qualitative interviews, we counterintuitively find that... View Details
      Keywords: Experience; Interdependency; Strategic Foresight; Crowdfunding; Timeline; Delay; Forecasting; Entrepreneurship; Learning; Complexity; Forecasting and Prediction; Product Development; Planning
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      Peterson, Aticus, and Andy Wu. "Entrepreneurial Learning and Strategic Foresight." Harvard Business School Working Paper, No. 21-123, January 2021. (Revised May 2021.)
      • September 2020
      • Article

      Analyst Forecast Bundling

      By: Michael Drake, Peter Joos, Joseph Pacelli and Brady Twedt
      Changing economic conditions over the past two decades have created incentives for sell-side analysts to both provide their institutional clients tiered services and to streamline their written research process. One manifestation of these changes is an increased... View Details
      Keywords: Analysts; Earnings Forecasts; Forecast Accuracy; Forecast Bundling; Business Earnings; Forecasting and Prediction
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      Drake, Michael, Peter Joos, Joseph Pacelli, and Brady Twedt. "Analyst Forecast Bundling." Management Science 66, no. 9 (September 2020): 4024–4046.
      • August 2020 (Revised September 2020)
      • Technical Note

      Assessing Prediction Accuracy of Machine Learning Models

      By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne
      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... View Details
      Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
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      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
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      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.)
      • August 2020 (Revised March 2021)
      • Case

      Migros Turkey: Scaling Online Operations (A)

      By: Antonio Moreno and Gamze Yucaoglu
      The case opens in November 2019 as Ozgur Tort and Mustafa Bartin, CEO and chief large-format and online retail officer of Migros Ticaret A.S. (Migros), Turkey’s oldest and one of its largest supermarket chains, are contemplating what the best fulfillment format and... View Details
      Keywords: Retail; Grocery; Business Model; Emerging Markets; For-Profit Firms; Strategy; Digital Platforms; Information Technology; Technology Adoption; Value Creation; Globalization; Competition; Expansion; Logistics; Profit; Resource Allocation; Corporate Strategy; Turkey
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      Moreno, Antonio, and Gamze Yucaoglu. "Migros Turkey: Scaling Online Operations (A)." Harvard Business School Case 621-026, August 2020. (Revised March 2021.)
      • Article

      Oracle Efficient Private Non-Convex Optimization

      By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
      One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it.... View Details
      Keywords: Machine Learning; Algorithms; Objective Perturbation; Mathematical Methods
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      Neel, Seth, Aaron Leon Roth, Giuseppe Vietri, and Zhiwei Steven Wu. "Oracle Efficient Private Non-Convex Optimization." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
      • 2021
      • Conference Presentation

      An Algorithmic Framework for Fairness Elicitation

      By: Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton and Zhiwei Steven Wu
      We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders.... View Details
      Keywords: Algorithmic Fairness; Machine Learning; Fairness; Framework; Mathematical Methods
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      Jung, Christopher, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton, and Zhiwei Steven Wu. "An Algorithmic Framework for Fairness Elicitation." Paper presented at the 2nd Symposium on Foundations of Responsible Computing (FORC), 2021.
      • June 2020
      • Article

      How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections

      By: Maria Ibanez and Michael W. Toffel
      Accuracy and consistency are critical for inspections to be an effective, fair, and useful tool for assessing risks, quality, and suppliers—and for making decisions based on those assessments. We examine how inspector schedules could introduce bias that erodes... View Details
      Keywords: Assessment; Bias; Inspection; Scheduling; Econometric Analysis; Empirical Research; Regulation; Health; Food; Safety; Quality; Performance Consistency; Governing Rules, Regulations, and Reforms
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      Ibanez, Maria, and Michael W. Toffel. "How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections." Management Science 66, no. 6 (June 2020): 2396–2416. (Revised February 2019. Featured in Harvard Business Review, Forbes, Food Safety Magazine, Food Safety News, and KelloggInsight. (2020 MSOM Responsible Research Finalist.))
      • Article

      Human Capital and the Future of Work: Implications for Investors and ESG Integration

      By: Sakis Kotsantonis and George Serafeim
      Human capital development (HCD) is a key consideration for most companies, but only recently have investors focused on understanding the risks and opportunities related to human capital with the emergence of environmental, social, and governance (ESG) investment... View Details
      Keywords: Future Of Work; ESG; Employee Engagement; Employee Compensation; Human Capital; Human Resources; Employees; Compensation and Benefits; Wages
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      Kotsantonis, Sakis, and George Serafeim. "Human Capital and the Future of Work: Implications for Investors and ESG Integration." Journal of Financial Transformation 51 (April 2020): 115–130.
      • 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
      Keywords: Machine Learning; Transfer Theorem; Mathematical Methods
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      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.
      • 2020
      • Article

      Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion

      By: Dimitris Bertsimas and Michael Lingzhi Li
      We formulate the problem of matrix completion with and without side information as a non-convex optimization problem. We design fastImpute based on non-convex gradient descent and show it converges to a global minimum that is guaranteed to recover closely the... View Details
      Keywords: Mathematical Methods
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      Bertsimas, Dimitris, and Michael Lingzhi Li. "Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion." Journal of Machine Learning Research 21, no. 1 (2020).
      • January 2020
      • Article

      The Job Rating Game: Revolving Doors and Analyst Incentives

      By: Elisabeth Kempf
      Investment banks frequently hire analysts from rating agencies. While many argue that this "revolving door" creates captured analysts, it can also create incentives to improve accuracy. To study this issue, I construct an original dataset, linking analysts to their... View Details
      Keywords: Credit Rating Agencies; Investment Banking; Recruitment; Performance Evaluation; Financial Services Industry
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      Kempf, Elisabeth. "The Job Rating Game: Revolving Doors and Analyst Incentives." Journal of Financial Economics 135, no. 1 (January 2020): 41–67.
      • August 2019
      • Article

      When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation

      By: Yicheng Song, Nachiketa Sahoo and Elie Ofek
      Sometimes we desire change, a break from the same or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However,... View Details
      Keywords: Recommender Systems; Personalization; Recommendation Diversity; Variety Seeking; Collaborative Filtering; Consumer Utility Models; Digital Media; Clickstream Analysis; Learning-to-rank; Consumer Behavior; Media; Customization and Personalization; Strategy; Mathematical Methods
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      Song, Yicheng, Nachiketa Sahoo, and Elie Ofek. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation." Management Science 65, no. 8 (August 2019): 3737–3757.
      • 2025
      • Working Paper

      Mammography - Early Detection, Precise Diagnoses: Case Histories of Transformational Advances

      By: Amar Bhidé, Srikant M. Datar and Katherine Stebbins
      This case history describes how the development of x-ray-based techniques and equipment (“mammography”) led to widespread screening for breast cancer and enabled “minimally invasive” biopsies of breast tumors. Specifically, we chronicle how: 1) new protocols and... View Details
      Keywords: Health Care and Treatment; Technological Innovation; Innovation Strategy; Technology Adoption; Collaborative Innovation and Invention; Innovation and Invention; Governing Rules, Regulations, and Reforms
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      Bhidé, Amar, Srikant M. Datar, and Katherine Stebbins. "Mammography - Early Detection, Precise Diagnoses: Case Histories of Transformational Advances." Harvard Business School Working Paper, No. 20-002, July 2019. (Revised January 2025.)
      • Article

      Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting

      By: Raymond H. Mak, Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani and Eva C. Guinan
      Importance: Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial... View Details
      Keywords: Crowdsourcing; AI Algorithms; Health Care and Treatment; Collaborative Innovation and Invention; AI and Machine Learning
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      Mak, Raymond H., Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani, and Eva C. Guinan. "Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting." JAMA Oncology 5, no. 5 (May 2019): 654–661.
      • February 2019 (Revised July 2025)
      • Case

      Theranos: Who Has Blood on Their Hands? (A)

      By: Nien-hê Hsieh, Christina R. Wing, Emilie Fournier and Anna Resman
      This case covers the rise and fall of Theranos, the company founded by Elizabeth Holmes in 2004 to revolutionize the blood testing industry by creating a device that could provide from a small finger prick the same results and accuracy as intravenous blood draws. As... View Details
      Keywords: Health Testing and Trials; Corporate Accountability; Organizational Culture; Misleading and Fraudulent Advertising; Crime and Corruption; Ethics; Entrepreneurship; Lawsuits and Litigation
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      Hsieh, Nien-hê, Christina R. Wing, Emilie Fournier, and Anna Resman. "Theranos: Who Has Blood on Their Hands? (A)." Harvard Business School Case 619-039, February 2019. (Revised July 2025.)
      • Article

      Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

      By: Katrina Ligett, Seth Neel, Aaron Leon Roth, Bo Waggoner and Steven Wu
      Traditional approaches to differential privacy assume a fixed privacy requirement ϵ for a computation, and attempt to maximize the accuracy of the computation subject to the privacy constraint. As differential privacy is increasingly deployed in practical settings, it... View Details
      Keywords: Differential Privacy; Empirical Risk Minimization; Accuracy First
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      Ligett, Katrina, Seth Neel, Aaron Leon Roth, Bo Waggoner, and Steven Wu. "Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM." Journal of Privacy and Confidentiality 9, no. 2 (2019).
      • 2019
      • Article

      An Empirical Study of Rich Subgroup Fairness for Machine Learning

      By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
      Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
      Keywords: Machine Learning; Fairness; AI and Machine Learning
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      Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.
      • 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... View Details
      Keywords: Machine Learning; Information; Sales; Forecasting and Prediction; Social Media
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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.
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