Publications
Publications
Show Results For
-
All HBS Web
(602)
- Faculty Publications (172)
Page 1 of
172
Results
→
- 2023
- Article
Association Between Regulatory Submission Characteristics and Recalls of Medical Devices Receiving 510(k) Clearance
By: Alexander O. Everhart, Soumya Sen, Ariel D. Stern, Yi Zhu and Pinar Karaca-Mandic
Importance: Most regulated medical devices enter the U.S. market via the 510(k) regulatory submission pathway, wherein manufacturers demonstrate that applicant devices are “substantially equivalent” to 1 or more “predicate” devices (legally marketed medical devices...
View Details
- 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...
View Details
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means...
View Details
- January 2023
- Case
Proday: Calling the Right Play
By: Lindsay N. Hyde, Thomas R. Eisenmann and Tom Quinn
Sarah Kunst knew the elements of a successful startup from her tenure at venture capital firms. In April 2018, however, her own app – Proday, a home fitness platform featuring exercises filmed by professional sports stars – was floundering. Kunst theorized that...
View Details
- 2023
- Working Paper
When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions
By: Himabindu Lakkaraju and Chiara Farronato
- 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...
View Details
- 2022
- 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...
View Details
- 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....
View Details
- 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....
View Details
- 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....
View Details
- 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....
View Details
- 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....
View Details
- 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....
View Details
- 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....
View Details
- 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
- 2022
- Working Paper
Price Monitoring and Response: A Supply Side Characterization
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...
View Details
- 2022
- Working Paper
Algorithmic Assortment Curation: An Empirical Study of Buybox in Online Marketplaces
By: Santiago Gallino, Nil Karacaoglu and Antonio Moreno
Most online sales worldwide take place in marketplaces that connect sellers and buyers. The presence of numerous third-party sellers leads to a proliferation of listings for each product, making it difficult for customers to choose between the available options. Online...
View Details
- 2022
- Article
Dynamic Pricing Algorithms, Consumer Harm, and Regulatory Response
By: Alexander MacKay and Samuel N. Weinstein
Pricing algorithms are rapidly transforming markets, from ride-sharing apps, to air travel, to online retail. Regulators and scholars have watched this development with a wary eye. Their focus so far has been on the potential for pricing algorithms to facilitate...
View Details
Keywords:
Competition Policy;
Regulation;
Algorithmic Pricing;
Dynamic Pricing;
Economics;
Law And Economics;
Law And Regulation;
Consumer Protection;
Antitrust Law;
Industrial Organization;
Antitrust Issues And Policies;
Technological Change: Choices And Consequences;
Competition;
Policy;
Price;
Governing Rules, Regulations, and Reforms;
Microeconomics;
Duopoly and Oligopoly;
Law
- 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.
View Details
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;
Marketing;
Transformation;
Decision Making;
AI and Machine Learning;
Retail Industry;
Italy
- 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...
View Details
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