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Show Results For
- All HBS Web
(645)
- News (145)
- Research (421)
- Events (15)
- Multimedia (11)
- Faculty Publications (295)
- Research Summary
Overview
Professor Ferreira's research primarily focuses on how retailers can use algorithms to make better revenue management decisions, including pricing, product display, and assortment planning. In the retail industry, anticipating consumer demand is arguably one of the... View Details
- 08 May 2018
- First Look
First Look at New Research and Ideas, May 8, 2018
https://www.hbs.edu/faculty/Pages/item.aspx?num=52570 Algorithm Appreciation: People Prefer Algorithmic to Human Judgment By: Logg, Jennifer M., Julia A. Minson, and Don A. Moore Abstract—Even though... View Details
Keywords: Sean Silverthorne
- March 2022
- Article
Learning to Rank an Assortment of Products
By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar
We consider the product ranking challenge that online retailers face when their customers typically behave as “window shoppers”: they form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue... View Details
Keywords: Online Learning; Product Ranking; Assortment Optimization; Learning; Internet and the Web; Product Marketing; Consumer Behavior; E-commerce
Ferreira, Kris, Sunanda Parthasarathy, and Shreyas Sekar. "Learning to Rank an Assortment of Products." Management Science 68, no. 3 (March 2022): 1828–1848.
- November–December 2018
- Article
Online Network Revenue Management Using Thompson Sampling
By: Kris J. Ferreira, David Simchi-Levi and He Wang
We consider a network revenue management problem where an online retailer aims to maximize revenue from multiple products with limited inventory constraints. As common in practice, the retailer does not know the consumer's purchase probability at each price and must... View Details
Keywords: Online Marketing; Revenue Management; Revenue; Management; Marketing; Internet and the Web; Price; Mathematical Methods
Ferreira, Kris J., David Simchi-Levi, and He Wang. "Online Network Revenue Management Using Thompson Sampling." Operations Research 66, no. 6 (November–December 2018): 1586–1602.
- 17 Sep 2021
- News
AI Can Help Address Inequity — If Companies Earn Users’ Trust
- Teaching Interest
Big Data Analytics and Machine Learning
Big data in the context of marketing, management, and innovation strategy. Machine Learning algorithms and tools.
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- November–December 2024
- Article
Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing
By: Kirk Bansak and Elisabeth Paulson
This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment... View Details
Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Operations Research 72, no. 6 (November–December 2024): 2375–2390.
- 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
Kwon, Caleb, Ananth Raman, and Jorge Tamayo. "Human-Computer Interactions in Demand Forecasting and Labor Scheduling Decisions." Working Paper, December 2022. (R&R Management Science.)
- 27 May 2015
- Blog Post
What is an HBS Section?
10 sections of 90+ people in your first year. You find out during your first week at HBS which section you’ll be in (A-J), and they use an algorithm to make sure each section has a diverse cross-section of people from different countries,... View Details
- September 2020 (Revised July 2022)
- Supplement
Spreadsheet Supplement to Artea (B) and (C)
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting" View Details
- 17 May 2022
- Cold Call Podcast
Delivering a Personalized Shopping Experience with AI
Keywords: Re: Jill J. Avery
- 14 Jun 2017
- Working Paper Summaries
Minimizing Justified Envy in School Choice: The Design of New Orleans' OneApp
- 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... View Details
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.
- Awards
John D. C. Little Award
By: Shunyuan Zhang
Nominated for the 2022 John D. C. Little Award for “Frontiers: Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb” (Marketing Science, September–October 2021) with Nitin Mehta, Param Singh, and Kannan Srinivasan. View Details
- 14 Nov 2018
- HBS Seminar
Lindsey Cameron, University of Michigan Ross School of Business
- 2019
- Article
An Empirical Study of Rich Subgroup Fairness for Machine Learning
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.
- Article
Conversational Receptiveness: Expressing Engagement with Opposing Views
By: M. Yeomans, J. Minson, H. Collins, H. Chen and F. Gino
We examine “conversational receptiveness”—the use of language to communicate one’s willingness to thoughtfully engage with opposing views. We develop an interpretable machine-learning algorithm to identify the linguistic profile of receptiveness (Studies 1A-B). We then... View Details
Keywords: Receptiveness; Natural Language Processing; Disagreement; Interpersonal Communication; Relationships; Conflict Management
Yeomans, M., J. Minson, H. Collins, H. Chen, and F. Gino. "Conversational Receptiveness: Expressing Engagement with Opposing Views." Organizational Behavior and Human Decision Processes 160 (September 2020): 131–148.
- 16 Apr 2019
- News
Fewer small businesses expected to hire new employees in 2019
- 28 Mar 2018
- HBS Seminar
Tim O’Reilly, O’Reilly Media
Elisabeth C. Paulson
Elisabeth Paulson is an Assistant Professor of Business Administration in the Technology and Operations Management Unit at Harvard Business School. She teaches the first year course on Technology and Operations Management in the required curriculum.
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