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- All HBS Web (1,026)
- Faculty Publications (347)
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- All HBS Web (1,026)
- Faculty Publications (347)
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- 1995
- Chapter
Alternative Models of Negotiated Outcomes and the Nontraditional Utility Concerns That Limit Their Predictability
By: S. B. White, M. H. Bazerman and M. A. Neale
White, S. B., M. H. Bazerman, and M. A. Neale. "Alternative Models of Negotiated Outcomes and the Nontraditional Utility Concerns That Limit Their Predictability." In Research on Negotiation in Organizations, edited by R. J. Bies, R. Lewicki, and B. Sheppard. Greenwich, CT: JAI Press, 1995.
- 2013
- Working Paper
Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific Coefficients, and Estimating Strategy Trade-Offs
By: Juan Alcacer, Wilbur Chung, Ashton Hawk and Goncalo Pacheco-de-Almeida
Although Strategy research aims to understand how firm actions have differential effects on performance, most empirical research estimates the average effects of these actions across firms. This paper promotes Random Coefficients Models (RCMs) as an ideal empirical... View Details
Alcacer, Juan, Wilbur Chung, Ashton Hawk, and Goncalo Pacheco-de-Almeida. "Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific Coefficients, and Estimating Strategy Trade-Offs." Harvard Business School Working Paper, No. 14-022, September 2013.
- March 2022 (Revised January 2025)
- Technical Note
Prediction & Machine Learning
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional... View Details
Keywords: Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation; AI and Machine Learning
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised January 2025.)
- 2009
- Case
What People Want (and How to Predict It)
By: Thomas H. Davenport and Jeanne G. Harris
Historically, neither the creators nor the distributors of cultural products such as books or movies have used analytics -- data, statistics, predictive modeling -- to determine the likely success of their offerings. Instead, companies relied on the brilliance of... View Details
Keywords: Product Development; Creativity; Customer Satisfaction; Forecasting and Prediction; Markets; Business Model; Publishing Industry; Motion Pictures and Video Industry
Davenport, Thomas H., and Jeanne G. Harris. "What People Want (and How to Predict It)." 2009.
- August 2018 (Revised September 2018)
- Supplement
Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the... View Details
Keywords: Data Science; Analytics and Data Science; Analysis; Customers; Household; Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (B)." Harvard Business School Supplement 119-026, August 2018. (Revised September 2018.)
- June 2021
- Article
From Predictions to Prescriptions: A Data-driven Response to COVID-19
By: Dimitris Bertsimas, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg and Cynthia Zeng
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at... View Details
Keywords: COVID-19; Health Pandemics; AI and Machine Learning; Forecasting and Prediction; Analytics and Data Science
Bertsimas, Dimitris, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, and Cynthia Zeng. "From Predictions to Prescriptions: A Data-driven Response to COVID-19." Health Care Management Science 24, no. 2 (June 2021): 253–272.
- 2017
- Working Paper
Knowledge Flows within Multinationals—Estimating Relative Influence of Headquarters and Host Context Using a Gravity Model
By: Prithwiraj Choudhury, Mike Horia Teodorescu and Tarun Khanna
From the perspective of a multinational subsidiary, we employ the classic gravity equation in economics to model and compare knowledge flows to the subsidiary from the MNC headquarters and from the host country context. We also generalize traditional economics gravity... View Details
- 2023
- Article
Post Hoc Explanations of Language Models Can Improve Language Models
By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance... View Details
Krishna, Satyapriya, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, and Himabindu Lakkaraju. "Post Hoc Explanations of Language Models Can Improve Language Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- Article
A Choice Prediction Competition for Market Entry Games: An Introduction
By: Ido Erev, Eyal Ert and Alvin E. Roth
A choice prediction competition is organized that focuses on decisions from experience in market entry games (http://sites.google.com/site/gpredcomp/ and http://www.mdpi.com/si/games/predict-behavior/). The competition is based on two experiments: An estimation... View Details
Keywords: Experience and Expertise; Decision Choices and Conditions; Forecasting and Prediction; Learning; Market Entry and Exit; Game Theory; Behavior; Competition
Erev, Ido, Eyal Ert, and Alvin E. Roth. "A Choice Prediction Competition for Market Entry Games: An Introduction." Special Issue on Predicting Behavior in Games. Games 1, no. 2 (June 2010): 117–136.
- 18 Sep 2019
- Working Paper Summaries
Using Models to Persuade
Keywords: by Joshua Schwartzstein and Adi Sunderam
- January 2010
- Journal Article
A Choice Prediction Competition: Choices from Experience and from Description
By: Ido Erev, Eyal Ert, Alvin E. Roth, Ernan E. Haruvy, Stefan Herzog, Robin Hau, Ralph Hertwig, Terrence Steward, Robert West and Christian Lebiere
Erev, Ert, and Roth organized three choice prediction competitions focused on three related choice tasks: one-shot decisions from description (decisions under risk), one-shot decisions from experience, and repeated decisions from experience. Each competition was based... View Details
Keywords: Experience and Expertise; Decision Choices and Conditions; Forecasting and Prediction; Mathematical Methods; Risk and Uncertainty; Competition
Erev, Ido, Eyal Ert, Alvin E. Roth, Ernan E. Haruvy, Stefan Herzog, Robin Hau, Ralph Hertwig, Terrence Steward, Robert West, and Christian Lebiere. "A Choice Prediction Competition: Choices from Experience and from Description." Special Issue on Decisions from Experience. Journal of Behavioral Decision Making 23, no. 1 (January 2010).
- Research Summary
Making Machine Learning Models Interpretable
I work on developing various tools and methodologies which can help decision makers (e.g., doctors, managers) to better understand the predictions of machine learning models. View Details
- 26 Nov 2018
- Working Paper Summaries
Demand Estimation in Models of Imperfect Competition
Keywords: by Alexander MacKay and Nathan H. Miller
- 2022
- Article
A Human-Centric Take on Model Monitoring
By: Murtuza Shergadwala, Himabindu Lakkaraju and Krishnaram Kenthapadi
Predictive models are increasingly used to make various consequential decisions in high-stakes domains such as healthcare, finance, and policy. It becomes critical to ensure that these models make accurate predictions, are robust to shifts in the data, do not rely on... View Details
Shergadwala, Murtuza, Himabindu Lakkaraju, and Krishnaram Kenthapadi. "A Human-Centric Take on Model Monitoring." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (HCOMP) 10 (2022): 173–183.
- 2025
- Working Paper
Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach
By: Ta-Wei Huang and Eva Ascarza
As firms increasingly rely on customer data for personalization, concerns over privacy and regulatory compliance have grown. Local Differential Privacy (LDP) offers strong individual-level protection by injecting noise into data before collection. While... View Details
Keywords: Targeted Intervention; Conditional Average Treatment Effect Estimation; Differential Privacy; Honest Estimation; Post-processing; Analytics and Data Science; Consumer Behavior; Marketing
Huang, Ta-Wei, and Eva Ascarza. "Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach." Harvard Business School Working Paper, No. 24-034, December 2023. (Revised March 2025.)
- 29 Aug 2013
- Working Paper Summaries
X-CAPM: An Extrapolative Capital Asset Pricing Model
- Research Summary
Models of optimal experience (flow)
Flow is a state of profound task-absorption, involvement, and intrinsic enjoyment that makes the person feel one with the activity. Csikszentmihalyi's Flow Theory states that flow is more likely to occur in situations in which the person feels that the activity is very... View Details
- 2013
- Working Paper
Return Predictability in the Treasury Market: Real Rates, Inflation, and Liquidity
By: Carolin E. Pflueger and Luis M. Viceira
Estimating the liquidity differential between inflation-indexed and nominal bond yields, we separately test for time-varying real rate risk premia, inflation risk premia, and liquidity premia in U.S. and U.K. bond markets. We find strong, model independent evidence... View Details
Keywords: Expectations Hypothesis; Term Structure; Real Interest Rate Risk; Inflation Risk; Inflation-Indexed Bonds; Financial Crisis; Inflation and Deflation; Financial Liquidity; Bonds; Investment Return; Risk and Uncertainty; United Kingdom; United States
Pflueger, Carolin E., and Luis M. Viceira. "Return Predictability in the Treasury Market: Real Rates, Inflation, and Liquidity." Harvard Business School Working Paper, No. 11-094, March 2011. (Revised September 2013.)