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      • Faculty Publications  (172)

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      • 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
      Keywords: Recalls; Governing Rules, Regulations, and Reforms; Medical Devices and Supplies Industry
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      Everhart, Alexander O., Soumya Sen, Ariel D. Stern, Yi Zhu, and Pinar Karaca-Mandic. "Association Between Regulatory Submission Characteristics and Recalls of Medical Devices Receiving 510(k) Clearance." JAMA, the Journal of the American Medical Association 329, no. 2 (2023): 144–156.
      • 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
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      Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." International Conference on Artificial Intelligence and Statistics (AISTATS) (2023).
      • 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
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      Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. "Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse." International Conference on Learning Representations (ICLR) (2023).
      • 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
      Keywords: Social Media; Entrepreneurship; Advertising; Digital Marketing; Product Launch; Social Marketing; Failure; Sports; Applications and Software; Technology Industry; United States
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      Hyde, Lindsay N., Thomas R. Eisenmann, and Tom Quinn. "Proday: Calling the Right Play." Harvard Business School Case 823-005, January 2023.
      • 2023
      • Working Paper

      When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions

      By: Himabindu Lakkaraju and Chiara Farronato
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      Lakkaraju, Himabindu, and Chiara Farronato. "When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions." Working Paper, 2023.
      • 2022
      • Working Paper

      Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions

      By: Caleb Kwon, Ananth Raman and Jorge Tamayo
      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
      Keywords: Employees; Human Capital; Performance; Applications and Software; Management Skills; Management Practices and Processes; Retail Industry
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      Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, December 2022.
      • 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
      Keywords: Cognitive Biases; Algorithm Transparency; Forecasting and Prediction; Behavior; AI and Machine Learning; Analytics and Data Science; Cognition and Thinking
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      Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence." Working Paper, December 2022.
      • 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
      Keywords: Data-driven Decision-making; Decisions; Negotiation; Bids and Bidding; Valuation; Consumer Behavior; Real Estate Industry
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      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....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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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....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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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....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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      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....  View Details
      Keywords: Decision Choices and Conditions; Decision Making; Market Timing; Measurement and Metrics
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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....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
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      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....  View Details
      Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Market Timing; Measurement and Metrics
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      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
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      Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for iBuyer." Harvard Business School Exercise 923-023, October 2022.
      • 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
      Keywords: Algorithmic Pricing; Ecommerce; Price Monitoring; Price; Competition; Retail Industry
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      Israeli, Ayelet, and Eric Anderson. "Price Monitoring and Response: A Supply Side Characterization." Working Paper, October 2022.
      • 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
      Keywords: Algorithms; E-commerce; Sales; Digital Marketing; Internet and the Web; Customer Satisfaction
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      Gallino, Santiago, Nil Karacaoglu, and Antonio Moreno. "Algorithmic Assortment Curation: An Empirical Study of Buybox in Online Marketplaces." Working Paper, September 2022.
      • 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
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      MacKay, Alexander, and Samuel N. Weinstein. "Dynamic Pricing Algorithms, Consumer Harm, and Regulatory Response." Washington University Law Review 100, no. 1 (2022): 111–174. (Direct download.)
      • 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
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      Israeli, Ayelet. "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion." Harvard Business School Teaching Note 523-020, September 2022. (Revised November 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...  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
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      Israeli, Ayelet. "Zalora: Data-Driven Pricing Recommendations." Harvard Business School Supplement 523-032, August 2022.
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