Filter Results:
(429)
Show Results For
- All HBS Web
(854)
- Faculty Publications (429)
Show Results For
- All HBS Web
(854)
- Faculty Publications (429)
- September 2019
- Article
Optimizing Reserves in School Choice: A Dynamic Programming Approach
By: Franklyn Wang, Ravi Jagadeesan and Scott Duke Kominers
We introduce a new model of school choice with reserves in which a social planner is constrained by a limited supply of reserve seats and tries to find an optimal matching according to a social welfare function. We construct the optimal distribution of reserves via a... View Details
Wang, Franklyn, Ravi Jagadeesan, and Scott Duke Kominers. "Optimizing Reserves in School Choice: A Dynamic Programming Approach." Operations Research Letters 47, no. 5 (September 2019): 438–446.
- August 2019
- Article
When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation
By: Yicheng Song, Nachiketa Sahoo and Elie Ofek
Sometimes we desire change, a break from the same or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However,... View Details
Keywords: Recommender Systems; Personalization; Recommendation Diversity; Variety Seeking; Collaborative Filtering; Consumer Utility Models; Digital Media; Clickstream Analysis; Learning-to-rank; Consumer Behavior; Media; Customization and Personalization; Strategy; Mathematical Methods
Song, Yicheng, Nachiketa Sahoo, and Elie Ofek. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation." Management Science 65, no. 8 (August 2019): 3737–3757.
- July 2019
- Article
Optimal Capital Structure and Bankruptcy Choice: Dynamic Bargaining vs Liquidation
By: Samuel Antill and Steven R. Grenadier
We model a firm’s optimal capital structure decision in a framework in which it may later choose to enter either Chapter 11 reorganization or Chapter 7 liquidation. Creditors anticipate equityholders’ ex-post reorganization incentives and price them into the ex-ante... View Details
Keywords: Default; Dynamic Bargaining; Capital Structure; Insolvency and Bankruptcy; Mathematical Methods
Antill, Samuel, and Steven R. Grenadier. "Optimal Capital Structure and Bankruptcy Choice: Dynamic Bargaining vs Liquidation." Journal of Financial Economics 133, no. 1 (July 2019): 198–224.
- 2019
- Article
Ridesharing with Driver Location Preferences
By: Duncan Rheingans-Yoo, Scott Duke Kominers, Hongyao Ma and David C. Parkes
We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations. If a platform ignores drivers' location preferences, it may make inefficient trip dispatches; moreover, drivers may strategize... View Details
Keywords: Ridesharing; Pricing; Compensation and Benefits; Geographic Location; Market Design; Mathematical Methods
Rheingans-Yoo, Duncan, Scott Duke Kominers, Hongyao Ma, and David C. Parkes. "Ridesharing with Driver Location Preferences." Proceedings of the International Joint Conference on Artificial Intelligence (2019): 557–564.
- March 2019 (Revised May 2019)
- Case
Fetchr: A New Way of Last Mile Delivery
By: V.G. Narayanan and Eren Kuzucu
By mid-2016, five years of aggressive growth had transformed Fetchr from a small logistics startup to a 1,000-employee, full-fledged last-mile delivery company operating across four countries in the Middle East and North Africa (MENA). Already beneficiaries of the... View Details
Keywords: Startup; Decision; Financial Strategy; UAE; KSA; MENA; Cost Accounting; Business Model; Business Startups; Transformation; Cost Management; Strategy; Disruptive Innovation; Technological Innovation; Growth and Development Strategy; Growth Management; Logistics; Service Delivery; Supply Chain Management; Performance Evaluation; Mathematical Methods; Mobile and Wireless Technology; Transportation Networks; Middle East; United Arab Emirates; Dubai; Bahrain; Egypt; Saudi Arabia; North Africa
Narayanan, V.G., and Eren Kuzucu. "Fetchr: A New Way of Last Mile Delivery." Harvard Business School Case 119-018, March 2019. (Revised May 2019.)
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
- 2018
- Working Paper
Diagnostic Bubbles
By: Pedro Bordalo, Nicola Gennaioli, Spencer Yongwook Kwon and Andrei Shleifer
We introduce diagnostic expectations into a standard setting of price formation in which investors learn about the fundamental value of an asset and trade it. We study the interaction of diagnostic expectations with two well-known mechanisms: learning from prices and... View Details
Bordalo, Pedro, Nicola Gennaioli, Spencer Yongwook Kwon, and Andrei Shleifer. "Diagnostic Bubbles." NBER Working Paper Series, No. 25399, December 2018.
- 2018
- Article
Insight into Gender Differences in STEM: Evidence from Peer Reviews in an Engineering Class
By: Jacqueline N. Lane, Bruce Ankenman and Seyed Iravani
As the service industry moves toward self-service, peer feedback serves a critical role in this shift for educational services. Peer feedback is a process by which students provide feedback to each other. One of its major benefits is that it enables students to become... View Details
Keywords: Peer Review; Peer Feedback; STEM Education; Anonymity; Education; Gender; Education Industry
Lane, Jacqueline N., Bruce Ankenman, and Seyed Iravani. "Insight into Gender Differences in STEM: Evidence from Peer Reviews in an Engineering Class." Service Science 10, no. 4 (2018): 442–456.
- 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.
- November 2018
- Case
Sportradar (A): From Data to Storytelling
By: Ramon Casadesus-Masanell, Karen Elterman and Oliver Gassmann
In 2013, the Swiss sports data company Sportradar debated whether to expand from its core business of data provision to bookmakers into sports media products. Sports data was becoming a commodity, and in the future, sports leagues might reduce their dependence on... View Details
Keywords: Sports Data; Data; Sport; Sportradar; Football; Soccer; Gambling; Betting; Betting Markets; Statistics; Odds; Live Data; Bookmakers; Betradar; Visualization; Integrity; Monitoring; Gaming; Streaming; 2013; St.Gallen; Algorithm; Mathematical Modeling; Carsten Koerl; Betandwin; Bwin; Wagering; Probability; Sports; Analytics and Data Science; Mathematical Methods; Games, Gaming, and Gambling; Transition; Strategy; Media; Sports Industry; Technology Industry; Information Technology Industry; Media and Broadcasting Industry; Europe; Switzerland; Asia; Austria; Germany; England
Casadesus-Masanell, Ramon, Karen Elterman, and Oliver Gassmann. "Sportradar (A): From Data to Storytelling." Harvard Business School Case 719-429, November 2018.
- 2018
- Working Paper
Full Substitutability
By: John William Hatfield, Scott Duke Kominers, Alexandru Nichifor, Michael Ostrovsky and Alexander Westkamp
Various forms of substitutability are essential for establishing the existence of equilibria and other useful properties in diverse settings such as matching, auctions, and exchange economies with indivisible goods. We extend earlier models’ definitions of... View Details
Hatfield, John William, Scott Duke Kominers, Alexandru Nichifor, Michael Ostrovsky, and Alexander Westkamp. "Full Substitutability." Harvard Business School Working Paper, No. 19-016.
- August 28, 2018
- Article
Maintaining Trust When Agents Can Engage in Self-deception
By: Andres Babino, Hernan A. Makse, Rafael Di Tella and Mariano Sigman
The coexistence of cooperation and selfish instincts is a remarkable characteristic of humans. Psychological research has unveiled the cognitive mechanisms behind self-deception. Two important findings are that a higher ambiguity about others’ social preferences leads... View Details
Keywords: Behavioral Economics; Cognitive Neuroscience; Corruption; Cooperation; Self-deception; Trust; Behavior
Babino, Andres, Hernan A. Makse, Rafael Di Tella, and Mariano Sigman. "Maintaining Trust When Agents Can Engage in Self-deception." Proceedings of the National Academy of Sciences 115, no. 35 (August 28, 2018): 8728–8733.
- 2018
- Working Paper
Bundling Incentives in (Many-to-Many) Matching with Contracts
By: Jonathan Ma and Scott Duke Kominers
In many-to-many matching with contracts, the way in which contracts are specified can affect the set of stable equilibrium outcomes. Consequently, agents may be incentivized to modify the set of contracts upfront. We consider one simple way in which agents may do so:... View Details
Keywords: Matching With Contracts; Contract Design; Bundling-proofness; Substitutability; Mathematical Methods
Ma, Jonathan, and Scott Duke Kominers. "Bundling Incentives in (Many-to-Many) Matching with Contracts." Harvard Business School Working Paper, No. 19-011, August 2018.
- August 2018
- Article
Extrapolation and Bubbles
By: Nicholas Barberis, Robin Greenwood, Lawrence Jin and Andrei Shleifer
We present an extrapolative model of bubbles. In the model, many investors form their demand for a risky asset by weighing two signals: an average of the asset’s past price changes and the asset’s degree of overvaluation. The two signals are in conflict, and investors... View Details
Barberis, Nicholas, Robin Greenwood, Lawrence Jin, and Andrei Shleifer. "Extrapolation and Bubbles." Journal of Financial Economics 129, no. 2 (August 2018): 203–227.
- May 2018
- Article
Selection and Market Reallocation: Productivity Gains from Multinational Production
By: Laura Alfaro and Maggie X. Chen
Assessing the productivity gains from multinational production has been a vital topic of economic research and policy debate. Positive aggregate productivity gains are often attributed to within-firm productivity improvement; however, an alternative, less emphasized... View Details
Keywords: Productivity Gains; Multinational Production; Selection; Market Reallocation; And Within-firm Productivity; Multinational Firms and Management; Production; Performance Productivity; Competition; Mathematical Methods
Alfaro, Laura, and Maggie X. Chen. "Selection and Market Reallocation: Productivity Gains from Multinational Production." American Economic Journal: Economic Policy 10, no. 2 (May 2018): 1–38. (Also NBER Working Paper 18207. See Harvard Business School Working Paper, No. 12–111, 2015 for longer version.)
- November 2021
- Article
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data
By: William Herlands, Edward McFowland III, Andrew Gordon Wilson and Daniel B. Neill
Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle,... View Details
Herlands, William, Edward McFowland III, Andrew Gordon Wilson, and Daniel B. Neill. "Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data." Proceedings of Machine Learning Research (PMLR) 84 (2018): 425–434. (Also presented at the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.)
- Article
Games of Threats
By: Elon Kohlberg and Abraham Neyman
A game of threats on a finite set of players, N, is a function d that assigns a real number to any coalition, S ⊆ N, such that d(S) = -d(N\S). A game of threats is not necessarily a coalitional game as it may fail to satisfy the condition d(Ø) = 0. We show that analogs... View Details
Kohlberg, Elon, and Abraham Neyman. "Games of Threats." Games and Economic Behavior 108 (March 2018): 139–145.
- January 2018
- Background Note
Math Tools for Strategists
By: Tarun Khanna and Jan W. Rivkin
Great strategists rely heavily on numbers as they go about their work. This note offers an overview of the highbrow and lowbrow quantitative tools that individuals commonly encounter during strategy courses and in actual strategy work. The note focuses especially on... View Details
Khanna, Tarun, and Jan W. Rivkin. "Math Tools for Strategists." Harvard Business School Background Note 718-477, January 2018.
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Neel, Seth, and Aaron Leon Roth. "Mitigating Bias in Adaptive Data Gathering via Differential Privacy." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).
- Article
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier (like classification rate or false positive rate) across these groups.... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).