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- 2024
- Working Paper
Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions
We investigate whether corporate officers should grant managers discretion to override AI-driven demand forecasts and labor scheduling tools. Analyzing five years of administrative data from a large grocery retailer using such an AI tool, encompassing over 500 stores,...
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Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, April 2024.
- 2024
- Working Paper
The Fading Light of Democratic Capitalism: How Pervasive Cronyism and Restricted Suffrage Are Destroying Democratic Capitalism as a National Ideal…and What to Do about It
What are we to do about declining public trust and confidence in democratic capitalism, which many citizens consider a cornerstone of our national ideology and identity? While the answer is not entirely clear, I argue in this essay that any effort aimed at restoring...
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Salter, Malcolm S. "The Fading Light of Democratic Capitalism: How Pervasive Cronyism and Restricted Suffrage Are Destroying Democratic Capitalism as a National Ideal…and What to Do about It." Harvard Business School Working Paper, No. 24-062, March 2024.
- 2023
- Working Paper
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
By: Biyonka Liang and Iavor I. Bojinov
Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the...
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Liang, Biyonka, and Iavor I. Bojinov. "An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits." Harvard Business School Working Paper, No. 24-057, March 2024.
- 2024
- Working Paper
Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference
By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov
Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to...
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Lindon, Michael, Dae Woong Ham, Martin Tingley, and Iavor I. Bojinov. "Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference." Harvard Business School Working Paper, No. 24-060, March 2024.
- 2023
- Working Paper
Design-Based Inference for Multi-arm Bandits
By: Dae Woong Ham, Iavor I. Bojinov, Michael Lindon and Martin Tingley
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire to use the available...
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Ham, Dae Woong, Iavor I. Bojinov, Michael Lindon, and Martin Tingley. "Design-Based Inference for Multi-arm Bandits." Harvard Business School Working Paper, No. 24-056, March 2024.
- February 9, 2024
- Article
Addressing Climate Change with Behavioral Science: A Global Intervention Tournament in 63 Countries
By: Madalina Vlasceanu, Kimberly C. Doell, Joseph B. Bak-Coleman, Boryana Todorova, Michael M. Berkebile-Weinberg, Amit Goldenberg, Eric Shuman and et al.
Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate...
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Keywords:
Climate Change;
Motivation and Incentives;
Behavior;
Policy;
Knowledge Sharing;
Values and Beliefs
Vlasceanu, Madalina, Kimberly C. Doell, Joseph B. Bak-Coleman, Boryana Todorova, Michael M. Berkebile-Weinberg, Amit Goldenberg, Eric Shuman, and et al. "Addressing Climate Change with Behavioral Science: A Global Intervention Tournament in 63 Countries." Science Advances 10, no. 6 (February 9, 2024).
- 2024
- Working Paper
Content Moderation with Opaque Policies
By: Scott Duke Kominers and Jesse M. Shapiro
A sender sends a signal about a state to a receiver who takes an action that determines a payoff. A moderator can block some or all of the sender's signal before it reaches the receiver. When the moderator's policy is transparent to the receiver, the moderator can...
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Kominers, Scott Duke, and Jesse M. Shapiro. "Content Moderation with Opaque Policies." NBER Working Paper Series, No. 32156, February 2024.
- 2024
- Working Paper
Do Information Frictions and Corruption Perceptions Kill Competition? A Field Experiment on Public Procurement in Uganda
By: Emanuele Colonnelli, Francesco Loiacono, Edwin Muhumuza and Edoardo Teso
We study whether information frictions and corruption perceptions deter firms from doing business with the government. We conduct two nationwide randomized controlled trials (RCTs) in collaboration with the national public procurement supervisory and anti-corruption...
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Keywords:
Knowledge Use and Leverage;
Government and Politics;
Crime and Corruption;
Trust;
Perception;
Business and Government Relations
Colonnelli, Emanuele, Francesco Loiacono, Edwin Muhumuza, and Edoardo Teso. "Do Information Frictions and Corruption Perceptions Kill Competition? A Field Experiment on Public Procurement in Uganda." NBER Working Paper Series, No. 32170, February 2024.
- Winter 2024
- Article
Is Pay Transparency Good?
By: Zoë B. Cullen
Countries around the world are enacting pay transparency policies to combat pay discrimination. Since 2000, 71 percent of OECD countries have done so. Most are enacting transparency horizontally, revealing pay between coworkers doing similar work within a firm. While...
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Keywords:
Policy;
Wages;
Knowledge Sharing;
Job Design and Levels;
Negotiation;
Performance Productivity;
Compensation and Benefits;
Motivation and Incentives
Cullen, Zoë B. "Is Pay Transparency Good?" Journal of Economic Perspectives 38, no. 1 (Winter 2024): 153–180.
- 2024
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift
By: Matthew DosSantos DiSorbo and Kris Ferreira
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). These outliers often originate from covariate shift,...
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- January 2024
- Supplement
Buurtzorg
By: Ethan Bernstein and Tatiana Sandino
As co-founders of home nursing company Buurtzorg, Jos de Blok and Gonnie Kronenberg prized both self-management and organizational learning. Buurtzorg’s 10,000 nurses across 950 neighborhood nursing teams in the Netherlands were empowered to manage themselves, both in...
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Keywords:
Organizational Design;
Management Style;
Business Model;
Knowledge Dissemination;
Learning;
Organizational Culture;
Health Industry;
Netherlands
Bernstein, Ethan, and Tatiana Sandino. "Buurtzorg." Harvard Business School Multimedia/Video Supplement 424-705, January 2024.
- 2024
- Report
Overcoming Barriers to Resolving Gaza and Beyond
As of early January 2024, discussion of the Gaza war heavily focuses on its humanitarian costs, cease fire possibilities, hostage prospects, and “day after” options. Yet what longer-term strategy guides actions on these vital issues while offering a more positive...
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Sebenius, James K. "Overcoming Barriers to Resolving Gaza and Beyond." Report, Belfer Center for Science and International Affairs, January 2024.
- 2024
- Working Paper
Lost in Transmission
By: Thomas Graeber, Shakked Noy and Christopher Roth
For many decisions, people rely on information received from others by word of mouth. How does the process of verbal transmission distort economic information? In our experiments, participants listen to audio recordings containing economic forecasts and are paid to...
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Keywords:
Information Trnasmission;
Word Of Mouth;
Word-of-Mouth;
Narratives;
Reliability;
Knowledge Sharing;
Spoken Communication;
Cognition and Thinking
Graeber, Thomas, Shakked Noy, and Christopher Roth. "Lost in Transmission." Harvard Business School Working Paper, No. 24-047, January 2024.
- 2024
- Conference Paper
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
Large Language Models (LLMs) are increasingly used as powerful tools for several
high-stakes natural language processing (NLP) applications. Recent prompting
works claim to elicit intermediate reasoning steps and key tokens that serve as
proxy explanations for LLM...
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Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
- December 2023
- Teaching Note
Buurtzorg
By: Ethan Bernstein and Tatiana Sandino
Teaching Note for HBS Case No. 122-101. As co-founders of home nursing company Buurtzorg, Jos de Blok and Gonnie Kronenberg prized both self-management and organizational learning. Buurtzorg’s 10,000 nurses across 950 neighborhood nursing teams in the Netherlands were...
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- December 2023
- Background Note
Organizational Learning
By: Willy Shih
This is a background note that surveys part of the extensive literature on organizational learning. The focus is on learning from experiences, how those learnings get translated into organizational routines and processes, and how that can also lead to getting stuck in...
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- November 2023
- Article
Knowledge About the Source of Emotion Predicts Emotion-Regulation Attempts, Strategies, and Perceived Emotion-Regulation Success
By: Yael Millgram, Matthew K. Nock, David D. Bailey and Amit Goldenberg
People’s ability to regulate emotions is crucial to healthy emotional functioning. One overlooked aspect in emotion-regulation research is that knowledge about the source of emotions can vary across situations and individuals, which could impact people’s ability to...
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Millgram, Yael, Matthew K. Nock, David D. Bailey, and Amit Goldenberg. "Knowledge About the Source of Emotion Predicts Emotion-Regulation Attempts, Strategies, and Perceived Emotion-Regulation Success." Psychological Science 34, no. 11 (November 2023): 1244–1255.
- 2023
- Article
MoPe: Model Perturbation-based Privacy Attacks on Language Models
By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training...
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Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.
- December 2023
- Article
Recover, Explore, Practice: The Transformative Potential of Sabbaticals
By: Kira Schabram, Matt Bloom and DJ DiDonna
Sabbaticals have seen an exponential growth in adoption over the last two decades and are ascribed extensive benefits by employers and employees alike. Little is known, however, about how individuals spend their time or how their experiences impact them after they...
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Schabram, Kira, Matt Bloom, and DJ DiDonna. "Recover, Explore, Practice: The Transformative Potential of Sabbaticals." Academy of Management Discoveries 9, no. 4 (December 2023): 441–468.
- December 2023
- Article
Save More Today or Tomorrow: The Role of Urgency in Precommitment Design
By: Joseph Reiff, Hengchen Dai, John Beshears, Katherine L. Milkman and Shlomo Benartzi
To encourage farsighted behaviors, past research suggests that marketers may be wise to invite consumers to pre-commit to adopt them “later.” However, the authors propose that people will draw different inferences from different types of pre-commitment offers, and that...
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Reiff, Joseph, Hengchen Dai, John Beshears, Katherine L. Milkman, and Shlomo Benartzi. "Save More Today or Tomorrow: The Role of Urgency in Precommitment Design." Journal of Marketing Research (JMR) 60, no. 6 (December 2023): 1095–1113.