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- All HBS Web
(2,834)
- News (448)
- Research (2,171)
- Events (39)
- Multimedia (14)
- Faculty Publications (1,383)
Show Results For
- All HBS Web
(2,834)
- News (448)
- Research (2,171)
- Events (39)
- Multimedia (14)
- Faculty Publications (1,383)
- Article
Learning and Equilibrium as Useful Approximations: Accuracy of Prediction on Randomly Selected Constant Sum Games
By: Ido Erev, Alvin E. Roth, R. Slonim and Greg Barron
Erev, Ido, Alvin E. Roth, R. Slonim, and Greg Barron. "Learning and Equilibrium as Useful Approximations: Accuracy of Prediction on Randomly Selected Constant Sum Games." Special Issue on Behavioral Game Theory. Economic Theory 33, no. 1 (October 2007): 29–51.
- September 1998
- Article
Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria
By: Ido Erev and A. E. Roth
Erev, Ido, and A. E. Roth. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria." American Economic Review 88, no. 4 (September 1998): 848–881.
- 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
- 2021
- Working Paper
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
By: Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage and Himabindu Lakkaraju
McGrath, Sean, Parth Mehta, Alexandra Zytek, Isaac Lage, and Himabindu Lakkaraju. "When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making." Working Paper, January 2021.
- September–October 2020
- Article
Managing Churn to Maximize Profits
By: Aurelie Lemmens and Sunil Gupta
Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability or their responsiveness to a... View Details
Keywords: Churn Management; Defection Prediction; Loss Function; Stochastic Gradient Boosting; Customer Relationship Management; Consumer Behavior; Profit
Lemmens, Aurelie, and Sunil Gupta. "Managing Churn to Maximize Profits." Marketing Science 39, no. 5 (September–October 2020): 956–973.
- 15 Jun 2004
- Conference Presentation
What is the Best Measure of Adiposity for Predicting Testosterone? Results from the Massachusetts Male Aging Study
By: William B. Simpson, Frances J. Hayes, Andre B. Araujo and John B. McKinlay
Keywords: Measurement and Metrics; Health Testing and Trials; Age Characteristics; Gender Characteristics; Massachusetts
Simpson, William B., Frances J. Hayes, Andre B. Araujo, and John B. McKinlay. "What is the Best Measure of Adiposity for Predicting Testosterone? Results from the Massachusetts Male Aging Study." Paper presented at the Enocrine Society Annual Meeting, New Orleans, LA, June 15, 2004.
- 2019
- Working Paper
Managing Churn to Maximize Profits
By: Aurelie Lemmens and Sunil Gupta
Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability, or their responsiveness to a... View Details
Keywords: Churn Management; Defection Prediction; Loss Function; Stochastic Gradient Boosting; Customer Relationship Management; Consumer Behavior; Profit
Lemmens, Aurelie, and Sunil Gupta. "Managing Churn to Maximize Profits." Harvard Business School Working Paper, No. 14-020, September 2013. (Revised December 2019. Forthcoming at Marketing Science.)
- July 2019
- Article
I Know Why You Voted for Trump: (Over)inferring Motives Based on Choice
By: Kate Barasz, Tami Kim and Ioannis Evangelidis
People often speculate about why others make the choices they do. This paper investigates how such inferences are formed as a function of what is chosen. Specifically, when observers encounter someone else's choice (e.g., of political candidate), they use the chosen... View Details
Keywords: Self-other Difference; Social Perception; Inference-making; Preferences; Consumer Behavior; Prediction; Prediction Error; Decision Choices and Conditions; Perception; Behavior; Forecasting and Prediction
Barasz, Kate, Tami Kim, and Ioannis Evangelidis. "I Know Why You Voted for Trump: (Over)inferring Motives Based on Choice." Special Issue on The Cognitive Science of Political Thought. Cognition 188 (July 2019): 85–97.
- Article
Algorithms Need Managers, Too
By: Michael Luca, Jon Kleinberg and Sendhil Mullainathan
Algorithms are powerful predictive tools, but they can run amok when not applied properly. Consider what often happens with social media sites. Today many use algorithms to decide which ads and links to show users. But when these algorithms focus too narrowly on... View Details
Keywords: Machine Learning; Algorithms; Predictive Analytics; Management; Big Data; Analytics and Data Science
Luca, Michael, Jon Kleinberg, and Sendhil Mullainathan. "Algorithms Need Managers, Too." Harvard Business Review 94, nos. 1/2 (January–February 2016): 96–101.
- December 2013
- Article
Legislating Stock Prices
By: Lauren Cohen, Karl Diether and Christopher Malloy
We demonstrate that legislation has a simple, yet previously undetected impact on stock prices. Exploiting the voting record of legislators whose constituents are the affected industries, we show that the votes of these "interested" legislators capture important... View Details
Keywords: Legislator Incentives; Voting; Return Predictability; Lobbying; Motivation and Incentives; Government Legislation; Stocks
Cohen, Lauren, Karl Diether, and Christopher Malloy. "Legislating Stock Prices." Journal of Financial Economics 110, no. 3 (December 2013): 574–595. (Winner of Fama-DFA Prize for the Best Paper Published in the Journal of Financial Economics in Asset Pricing (Distinguished Paper) 2013.)
- August 2021
- Case
Precision Paint Co.
Describes a marketing director about to launch a new process for demand forecasting. Provides data that allow students to do a multivariable regression analysis. A rewritten version of an earlier case. View Details
Bojinov, Iavor I., Chiara Farronato, Janice H. Hammond, Michael Parzen, and Paul Hamilton. "Precision Paint Co." Harvard Business School Case 622-055, August 2021.
- Article
Inflation-Indexed Bonds and the Expectations Hypothesis
By: Carolin E. Pflueger and Luis M. Viceira
This paper empirically analyzes the Expectations Hypothesis (EH) in inflation-indexed (or real) bonds and in nominal bonds in the U.S. and in the U.K. We strongly reject the EH in inflation-indexed bonds and also confirm and update the existing evidence rejecting the... View Details
Keywords: TIPS; Breakeven Inflation; Return Predictability; Bond Risk Premia; Risk Management; Bonds; Financial Liquidity; Inflation and Deflation; United Kingdom; United States
Pflueger, Carolin E., and Luis M. Viceira. "Inflation-Indexed Bonds and the Expectations Hypothesis." Annual Review of Financial Economics 3 (2011): 139–158.
- June 2024
- Article
Going Digital: Implications for Firm Value and Performance
By: Wilbur Chen and Suraj Srinivasan
We examine firm value and performance implications of the growing trend of non-technology companies engaging in digital transformation. We measure digital activities in firms based on the disclosure of digital words in the business description section of 10-Ks. Digital... View Details
Keywords: Digital Technologies; Valuation; Return Predictability; Financial Statement Analysis; Performance; Value; Information Technology
Chen, Wilbur, and Suraj Srinivasan. "Going Digital: Implications for Firm Value and Performance." Review of Accounting Studies 29, no. 2 (June 2024): 1619–1665.
- 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. View Details
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.
- Article
Consumer Neuroscience: Advances in Understanding Consumer Psychology
By: Uma R. Karmarkar and Carolyn Yoon
While the study of consumer behavior has been enriched by improved abilities to generate new insights, many of the mechanisms underlying judgments and decision making remain difficult to investigate. In this review, we highlight some of the ways in which our... View Details
Keywords: Consumer Neuroscience; Neuroscience; Neuroeconomics; Consumer Psychology; Customer Behavior; Predictive Analytics; Neural Prediction; Neuroimaging; fMRI; Eye-tracking; Consumer Behavior; Marketing
Karmarkar, Uma R., and Carolyn Yoon. "Consumer Neuroscience: Advances in Understanding Consumer Psychology." Current Opinion in Psychology 10 (August 2016): 160–165.
- Article
Sadness, Identity, and Plastic in Over-shopping: The Interplay of Materialism, Poor Credit Management, and Emotional Buying Motives in Predicting Compulsive Buying
By: Grant Edward Donnelly, Masha Ksendzova and Ryan Howell
A comprehensive study is currently lacking to explain why material values strongly influence compulsive buying. The goal of the current study is to test if money management, buying motivations for improving mood and identity, and self-transformation expectations... View Details
Donnelly, Grant Edward, Masha Ksendzova, and Ryan Howell. "Sadness, Identity, and Plastic in Over-shopping: The Interplay of Materialism, Poor Credit Management, and Emotional Buying Motives in Predicting Compulsive Buying." Journal of Economic Psychology 39 (December 2013): 113–125.