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Show Results For
- All HBS Web
(700)
- People (2)
- News (78)
- Research (504)
- Events (10)
- Multimedia (8)
- Faculty Publications (371)
- Research Summary
Overview
By: Jorge Tamayo
Professor Tamayo’s research focuses on theoretical modeling and structural estimation of firm decision-making and productivity.
Professor Tamayo studies dynamic competition for customer membership. Generally, firms that implement a membership model charge a... View Details
Professor Tamayo studies dynamic competition for customer membership. Generally, firms that implement a membership model charge a... View Details
Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation.
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. In addition to augmenting managers' decision-making, experimentation mitigates risk by limiting the proportion of customers exposed to... View Details
Eliminating unintended bias in personalized policies using Bias Eliminating Adapted Trees (BEAT) - PNAS
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those... View Details
- Research Summary
Overview
Professor Huang examines the micro-foundations of entrepreneurship: the individual-level decision-making processes that influence entrepreneurs’ ability to acquire resources that they need, yet lack, especially financial capital. Deploying a variety of methods from... View Details
- 2024
- Working Paper
The Cram Method for Efficient Simultaneous Learning and Evaluation
By: Zeyang Jia, Kosuke Imai and Michael Lingzhi Li
We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and tests its empirical... View Details
Keywords: AI and Machine Learning
Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li. "The Cram Method for Efficient Simultaneous Learning and Evaluation." Working Paper, March 2024.
- 2023
- Working Paper
Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation
By: Dae Woong Ham, Michael Lindon, Martin Tingley and Iavor Bojinov
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. In addition to augmenting managers’ decision-making, experimentation mitigates risk by limiting the proportion of customers exposed to... View Details
Keywords: Performance Evaluation; Research and Development; Analytics and Data Science; Consumer Behavior
Ham, Dae Woong, Michael Lindon, Martin Tingley, and Iavor Bojinov. "Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation." Harvard Business School Working Paper, No. 23-070, May 2023.
- August 2018
- Article
Creative Sparks or Paralysis Traps? The Effects of Contradictions on Creative Processing and Creative Products
By: Goran Calic and Sébastien Hélie
Paradoxes are an unavoidable part of work life. The unusualness of attempting to simultaneously satisfy contradictory imperatives can result in creative outcomes that simultaneously satisfy both imperatives by inducing search for, and selection of, novel and useful... View Details
Calic, Goran, and Sébastien Hélie. "Creative Sparks or Paralysis Traps? The Effects of Contradictions on Creative Processing and Creative Products." Art. 1489. Frontiers in Psychology 9 (August 2018).
- 2011
- Article
Scalable Detection of Anomalous Patterns With Connectivity Constraints
By: Skyler Speakman, Edward McFowland III and Daniel B. Neill
We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details
- March 2022
- Article
Where to Locate COVID-19 Mass Vaccination Facilities?
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li and Alessandro Previero
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in particular, the new... View Details
Keywords: Vaccines; COVID-19; Health Care and Treatment; Health Pandemics; Performance Effectiveness; Analytics and Data Science; Mathematical Methods
Bertsimas, Dimitris, Vassilis Digalakis Jr, Alexander Jacquillat, Michael Lingzhi Li, and Alessandro Previero. "Where to Locate COVID-19 Mass Vaccination Facilities?" Naval Research Logistics Quarterly 69, no. 2 (March 2022): 179–200.
- Article
Matriarch: A Python Library for Materials Architecture
By: Tristan Giesa, Ravi Jagadeesan, David I. Spivak and Markus J. Buehler
Biological materials, such as proteins, often have a hierarchical structure ranging from basic building blocks at the nanoscale (e.g., amino acids) to assembled structures at the macroscale (e.g., fibers). Current software for materials engineering allows the user to... View Details
Keywords: Building Block; Category Theory; Hierarchical Protein Materials; Molecular Design; Open-Source Software; Structure Creation
Giesa, Tristan, Ravi Jagadeesan, David I. Spivak, and Markus J. Buehler. "Matriarch: A Python Library for Materials Architecture." ACS Biomaterials Science & Engineering 1, no. 10 (October 2015): 1009–1015.
- 01 Oct 2009
- Working Paper Summaries
Systemic Risk and the Refinancing Ratchet Effect
- 30 Apr 2024
- Book
When Managers Set Unrealistic Expectations, Employees Cut Ethical Corners
set aside their ethical qualms in deference to perceived authority figures (Milgram, 1963). Similarly, the 1971 “prison” experiments by Stanford Professor Philip Zimbardo had demonstrated the power of context to alter people’s ethical orientation; after only a few days... View Details
Keywords: by Dina Gerdeman
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
- 2010
- Article
Estimating the Attributable Cost of Physician Burnout in the United States
By: Shasha Han, Tait D. Shanafelt, Christine A. Sinsky, Karim M. Awad, Liselotte N. Dyrbye, Lynne C. Fiscus, Mickey Trockel and Joel Goh
Background: Although physician burnout is associated with negative clinical and organizational outcomes, its economic costs are poorly understood. As a result, leaders in health care cannot properly assess the financial benefits of initiatives to remediate... View Details
Keywords: Physicians; Burnout; Health; Health Care and Treatment; Employees; Cost; Programs; Policy; Health Industry
Han, Shasha, Tait D. Shanafelt, Christine A. Sinsky, Karim M. Awad, Liselotte N. Dyrbye, Lynne C. Fiscus, Mickey Trockel, and Joel Goh. "Estimating the Attributable Cost of Physician Burnout in the United States." Annals of Internal Medicine 170, no. 11 (June 4, 2019): 784–790.
- March 2016
- Article
Environmental Demands and the Emergence of Social Structure: Technological Dynamism and Interorganizational Network Forms
By: Adam Tatarynowicz, Maxim Sytch and Ranjay Gulati
This study investigates the origins of variation in the structures of interorganizational networks across industries. We combine empirical analyses of existing interorganizational networks in six industries with an agent-based simulation model of network emergence.... View Details
Keywords: Interorganizatonal Relationships; Social Networks; Network Emergence; Interorganizational Networks; Information Technology; Networks; Organizational Structure; Social and Collaborative Networks; Social Media
Tatarynowicz, Adam, Maxim Sytch, and Ranjay Gulati. "Environmental Demands and the Emergence of Social Structure: Technological Dynamism and Interorganizational Network Forms." Administrative Science Quarterly 61, no. 1 (March 2016): 52–86.
- 09 Nov 2016
- HBS Seminar
Robert A. Miller, Tepper School of Business, Carnegie Mellon University
- Article
Spending Variation Among ACOs in the Medicare Shared Savings Program
By: Michael Anne Kyle, J. Michael McWilliams, Mary Beth Landrum, Bruce E. Landon, Paul Trompke, David J. Nyweide and Michael E. Chernew
OBJECTIVES: Understanding variation in spending across organizations, rather than across geographic areas, is important because care is delivered by organizations and interventions increasingly focus on organizations. Accountable care organizations (ACOs) are... View Details
Keywords: Medicare; Accountable Care Organizations; ACOs; Health Care and Treatment; Spending; Analysis
Kyle, Michael Anne, J. Michael McWilliams, Mary Beth Landrum, Bruce E. Landon, Paul Trompke, David J. Nyweide, and Michael E. Chernew. "Spending Variation Among ACOs in the Medicare Shared Savings Program." American Journal of Managed Care 26, no. 4 (April 2020): 170–175.
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data,... View Details
- October–December 2022
- Article
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed... View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- 2021
- Working Paper
Supply- and Demand-Side Effects in Performance Appraisals: The Role of Gender and Race
By: Iris Bohnet, Oliver P. Hauser and Ariella Kristal
Performance reviews in firms are common but controversial. Managers’ subjective appraisals of their employees’ performance and employees’ self-evaluations might be affected by demographic characteristics, interact with each other as self-evaluations are typically... View Details
Bohnet, Iris, Oliver P. Hauser, and Ariella Kristal. "Supply- and Demand-Side Effects in Performance Appraisals: The Role of Gender and Race." HKS Faculty Research Working Paper Series, No. RWP21-016, May 2021.