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- Faculty Publications (119)
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected...
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Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 206 (April 2023).
- 2023
- Working Paper
PRIMO: Private Regression in Multiple Outcomes
By: Seth Neel
We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates...
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Neel, Seth. "PRIMO: Private Regression in Multiple Outcomes." Working Paper, March 2023.
- March 2023
- Teaching Note
VideaHealth: Building the AI Factory
By: Karim R. Lakhani
Teaching Note for HBS Case No. 621-021. The case “VideaHealth: Building the AI Factory” examines the creation of dental startup VideaHealth (Videa) and the development of its artificial intelligence (AI)-led business strategy through the eyes of founder and CEO Florian...
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- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market...
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Keywords:
Decision Trees;
Computational Advertising;
Market Segmentation;
Analytics and Data Science;
E-commerce;
Consumer Behavior;
Marketplace Matching;
Marketing Channels;
Digital Marketing
Aouad, Ali, Adam Elmachtoub, Kris J. Ferreira, and Ryan McNellis. "Market Segmentation Trees." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 648–667.
- February 2023 (Revised March 2024)
- Supplement
Shanty Real Estate: Teaching Note Supplement
By: Michael Luca and Jesse M. Shapiro
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....
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- Working Paper
Group Fairness in Dynamic Refugee Assignment
By: Daniel Freund, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt and Wentao Weng
Ensuring that refugees and asylum seekers thrive (e.g., find employment) in their host countries is a profound humanitarian goal, and a primary driver of employment is the geographic
location within a host country to which the refugee or asylum seeker is...
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Freund, Daniel, Thodoris Lykouris, Elisabeth Paulson, Bradley Sturt, and Wentao Weng. "Group Fairness in Dynamic Refugee Assignment." Harvard Business School Working Paper, No. 23-047, February 2023.
- 2022
- Working Paper
Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions
We empirically analyze how managerial overrides to a commercial algorithm that forecasts demand and schedules labor affect store performance. We analyze administrative data from a large grocery retailer that utilizes a commercial algorithm to forecast demand and...
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Keywords:
Employees;
Human Capital;
Performance;
Applications and Software;
Management Skills;
Management Practices and Processes;
Retail Industry
Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, December 2022.
- 2024
- Working Paper
Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information
which an algorithm does not have access to that can improve performance. How can we help humans effectively use
and adjust recommendations made by...
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Keywords:
Cognitive Biases;
Algorithm Transparency;
Forecasting and Prediction;
Behavior;
AI and Machine Learning;
Analytics and Data Science;
Cognition and Thinking
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence." Working Paper, February 2024.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for Homebuyer 1
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....
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Keywords:
Data-driven Decision-making;
Decisions;
Negotiation;
Bids and Bidding;
Valuation;
Consumer Behavior;
Real Estate Industry
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 1." Harvard Business School Exercise 923-016, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for Homebuyer 2
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....
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Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 2." Harvard Business School Exercise 923-017, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for Homebuyer 3
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....
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Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 3." Harvard Business School Exercise 923-018, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for iBuyer 1
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....
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Keywords:
Algorithm;
Decision Choices and Conditions;
Decision Making;
Measurement and Metrics;
Market Timing
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 1." Harvard Business School Exercise 923-019, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for iBuyer 2
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....
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Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 2." Harvard Business School Exercise 923-020, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for iBuyer 3
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....
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Keywords:
Algorithm;
Decision Choices and Conditions;
Decision Making;
Measurement and Metrics;
Market Timing
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 3." Harvard Business School Exercise 923-021, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Updated Confidential Information for Homebuyer
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....
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Keywords:
Algorithm;
Decision Choices and Conditions;
Decision Making;
Market Timing;
Measurement and Metrics
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for Homebuyer." Harvard Business School Exercise 923-022, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Updated Confidential Information for iBuyer
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....
View Details
Keywords:
Algorithm;
Decision Choices and Conditions;
Measurement and Metrics;
Market Timing;
Decision Making
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for iBuyer." Harvard Business School Exercise 923-023, October 2022.
- 2023
- Working Paper
Learning About Demand in the Long-tail: The Role of Competitive Monitoring
By: Ayelet Israeli and Eric Anderson
With the growth of e-commerce and increasing price transparency, there has been tremendous interest in the adoption and competitive consequences of algorithmic pricing. A premise of algorithmic pricing is that firms monitor competitors' prices and then their pricing...
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- September 2022 (Revised November 2022)
- Teaching Note
PittaRosso: Artificial Intelligence-Driven Pricing and Promotion
By: Ayelet Israeli
Teaching Note for HBS Case No. 522-046.
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Keywords:
Artificial Intelligence;
Pricing;
Pricing Algorithm;
Pricing Decisions;
Pricing Strategy;
Pricing Structure;
Promotion;
Promotions;
Online Marketing;
Data-driven Decision-making;
Data-driven Management;
Retail;
Retail Analytics;
Price;
Advertising Campaigns;
Analytics and Data Science;
Analysis;
Digital Marketing;
Budgets and Budgeting;
Marketing Strategy;
Transformation;
Decision Making;
AI and Machine Learning;
Retail Industry;
Italy
- 2022
- Working Paper
Slowly Varying Regression under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem...
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Keywords:
Mathematical Methods
Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression under Sparsity." Working Paper, September 2022.
- August 2022
- Supplement
Zalora: Data-Driven Pricing Recommendations
By: Ayelet Israeli
This exercise can be used in conjunction with the main case "Zalora: Data-Driven Pricing" to facilitate class discussion without requiring data analysis from the students. Instead, the exercise presents reports that were created by the data science team to answer the...
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Keywords:
Pricing;
Pricing Algorithms;
Dynamic Pricing;
Ecommerce;
Pricing Strategy;
Pricing And Revenue Management;
Apparel;
Singapore;
Startup;
Demand Estimation;
Data Analysis;
Data Analytics;
Exercise;
Price;
Internet and the Web;
Apparel and Accessories Industry;
Retail Industry;
Fashion Industry;
Singapore
Israeli, Ayelet. "Zalora: Data-Driven Pricing Recommendations." Harvard Business School Supplement 523-032, August 2022.