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- All HBS Web (972)
- Faculty Publications (319)
Show Results For
- All HBS Web (972)
- Faculty Publications (319)
- 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...
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- June 2023
- Article
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
As machine learning (ML) models are increasingly being employed to assist human decision
makers, it becomes critical to provide these decision makers with relevant inputs which can
help them decide if and how to incorporate model predictions into their decision...
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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." Transactions on Machine Learning Research (TMLR) (June 2023).
- 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...
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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.)
- 2011
- Article
A Choice Prediction Competition for Social Preferences in Simple Extensive Form Games: An Introduction
By: Eyal Ert, Ido Erev and Alvin E. Roth
Two independent, but related, choice prediction competitions are organized that focus on behavior in simple two-person extensive form games: one focuses on predicting the choices of the first mover and the other on predicting the choices of the second mover. The...
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Keywords:
Forecasting and Prediction;
Behavior;
Decision Choices and Conditions;
Competition;
Motivation and Incentives;
Game Theory;
Fairness
Ert, Eyal, Ido Erev, and Alvin E. Roth. "A Choice Prediction Competition for Social Preferences in Simple Extensive Form Games: An Introduction." Special Issue on Predicting Behavior in Games. Games 2, no. 3 (September 2011): 257–276.
- April 2024
- Article
A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification
By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),...
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Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
A Neurocomputational Model of Altruism and Its Implications
In this paper, we propose a neurocomputational model of altruistic choice and test it using behavioral and fMRI data from a task in which subjects make choices between real monetary prizes for themselves and another. Our model captures key patterns of choice,...
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- 30 May 2023
- Research & Ideas
Can AI Predict Whether Shoppers Would Pick Crest or Colgate?
but large language models like generative pre-trained transformers (GPTs) may allow companies to rely on AI to uncover consumers’ tastes, according to new research from Harvard Business School and Microsoft. Ayelet Israeli, an associate...
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Keywords:
by Kristen Senz
- January 1986 (Revised April 1987)
- Background Note
Models for Updating Demand Forecasts
Keywords:
Forecasting and Prediction
Schleifer, Arthur, Jr. "Models for Updating Demand Forecasts." Harvard Business School Background Note 186-180, January 1986. (Revised April 1987.)
- 2009
- Article
Modeling Expert Opinions on Food Healthfulness: A Nutrition Metric
By: Jolie M. Martin, John Beshears, Katherine L. Milkman, Max H. Bazerman and Lisa Sutherland
Research over the last several decades indicates the failure of existing nutritional labels to substantially improve the healthiness of consumers' food and beverage choices. The difficulty for policy-makers is to encapsulate a wide body of scientific knowledge in a... View Details
Keywords:
Judgments;
Food;
Nutrition;
Labels;
Knowledge Use and Leverage;
Demand and Consumers;
Measurement and Metrics;
Mathematical Methods
Martin, Jolie M., John Beshears, Katherine L. Milkman, Max H. Bazerman, and Lisa Sutherland. "Modeling Expert Opinions on Food Healthfulness: A Nutrition Metric." Journal of the American Dietetic Association 109, no. 6 (June 2009): 1088–1091.
- 2023
- Working Paper
Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach
By: Ta-Wei Huang and Eva Ascarza
Data-driven targeted interventions have become a powerful tool for organizations to optimize business outcomes
by utilizing individual-level data from experiments. A key element of this process is the estimation
of Conditional Average Treatment Effects (CATE), which...
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Huang, Ta-Wei, and Eva Ascarza. "Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach." Harvard Business School Working Paper, No. 24-034, December 2023.
- July 2023
- Article
Takahashi-Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations
By: Dawson Beutler, Alex Billias, Sam Holt, Josh Lerner and TzuHwan Seet
In 2001, Dean Takahashi and Seth Alexander of the Yale University Investments Office developed a deterministic model for estimating future cash flows and valuations for the Yale endowment’s private equity portfolio. Their model, which is simple and intuitive, is still...
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Beutler, Dawson, Alex Billias, Sam Holt, Josh Lerner, and TzuHwan Seet. "Takahashi-Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations." Journal of Portfolio Management 49, no. 7 (July 2023): 144–158.
- 2023
- Working Paper
Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models
By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
Algorithmic assignment of refugees and asylum seekers to locations within host
countries has gained attention in recent years, with implementations in the U.S.
and Switzerland. These approaches use data on past arrivals to generate machine
learning models that can...
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Bansak, Kirk, Elisabeth Paulson, and Dominik Rothenhäusler. "Random Distribution Shift in Refugee Placement: Strategies for Building Robust Models." Working Paper, June 2023.
- August 2023
- Article
Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel
By: Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju and Sameer Singh
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use...
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Slack, Dylan, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. "Explaining Machine Learning Models with Interactive Natural Language Conversations Using TalkToModel." Nature Machine Intelligence 5, no. 8 (August 2023): 873–883.
- 30 May 2023
- Research & Ideas
Can AI Predict Whether Shoppers Would Pick Crest Over Colgate?
but large language models like generative pre-trained transformers (GPTs) may allow companies to rely on AI to uncover consumers’ tastes, according to new research from Harvard Business School and Microsoft. Ayelet Israeli, an associate...
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- 07 Jul 2003
- Research & Ideas
The Organizational Model for Open Source
three projects: Debian, a complete non-commercial distribution of Linux; the GNU Object Model Environment (GNOME), which is a graphical user interface for Linux-based operating systems; and Apache, a public domain open source Web server....
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Keywords:
by Mallory Stark
- 1994
- Article
Three-dimensional Finite Element Modeling of a Cervical Vertebra: An Investigation of Burst Fracture Mechanism
By: Kevin J. Bozic, J H Keyak, H B Skinner, H U Bueff and David Bradford
Finite element modeling was used to study the mechanical behavior of a cervical vertebra under axial compressive loading. A three-dimensional (3-D) finite element (FE) model of a mid-cervical vertebra using inhomogeneous material properties was generated from...
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- 31 May 2023
- Research & Ideas
With Predictive Analytics, Companies Can Tap the Ultimate Opportunity: Customers’ Routines
used that information to predict how often and when a customer may request a car as part of their routine. The model could drill into specific kinds of routines, too: The model...
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- 15 Aug 2016
- Research & Ideas
Black Swans and Big Trends Can Ruin Anyone's Internet Prediction
investments are once again declining. Reasoning that today’s tech entrepreneurs and investors might value a history lesson, I’ve published Speed Trap as an ebook, which is downloadable for free in PDF format, and available in the iBooks Store for free and in the Kindle...
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- Article
Stereotype Content Model across Cultures: Universal Similarities and Some Differences
By: A.J.C. Cuddy, S.T. Fiske, V.S.Y. Kwan, P. Glick, S. Demoulin, J. Ph. Leyens and M.H. Bond
The stereotype content model (SCM; Fiske, Cuddy, Glick, & Xu, 2002) proposes potentially universal principles of societal stereotypes and their relation to social structure. Here, the SCM reveals theoretically grounded, cross-cultural, cross-groups' similarities and...
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Keywords:
Cross-Cultural and Cross-Border Issues;
Management Analysis, Tools, and Techniques;
Relationships;
Groups and Teams;
Prejudice and Bias;
Culture;
Societal Protocols;
East Asia;
Europe
Cuddy, A.J.C., S.T. Fiske, V.S.Y. Kwan, P. Glick, S. Demoulin, J. Ph. Leyens, and M.H. Bond. "Stereotype Content Model across Cultures: Universal Similarities and Some Differences." British Journal of Social Psychology 48, no. 1 (March 2009).
- 2013
- Article
Nations' Income Inequality Predicts Ambivalence in Stereotype Content: How Societies Mind the Gap
By: Federica Durante, S. T. Fiske, Nicolas Kervyn and Amy J.C. Cuddy
Income inequality undermines societies: the more inequality, the more health problems, social tensions, and the lower social mobility, trust, and life expectancy. Given people's tendency to legitimate existing social arrangements, the Stereotype Content Model (SCM)...
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Keywords:
Stereotypes;
Cross-cultural/cross-border;
Inequality;
Prejudice and Bias;
Equality and Inequality;
Income;
Cross-Cultural and Cross-Border Issues;
Power and Influence
Durante, Federica, S. T. Fiske, Nicolas Kervyn, and Amy J.C. Cuddy. "Nations' Income Inequality Predicts Ambivalence in Stereotype Content: How Societies Mind the Gap." British Journal of Social Psychology 52, no. 4 (December 2013): 726–746.