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- August 2018 (Revised September 2018)
- Supplement
LendingClub (B): Decision Trees & Random Forests
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) case. In this case students follow Emily Figel as she builds two tree-based models using historical LendingClub data to predict, with some probability, whether borrower will repay or default on his loan.
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Keywords: Data Science; Data Analytics; Decision Trees; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (B): Decision Trees & Random Forests." Harvard Business School Supplement 119-021, August 2018. (Revised September 2018.)
- December 2004 (Revised March 2006)
- Background Note
Decision Trees
By: Robin Greenwood and Lucy White
This case introduces decision analysis. Using a simple example, it illustrates the use of probability trees and decision trees as tools for solving business problems. View Details
Keywords: Decision Making
Greenwood, Robin, and Lucy White. "Decision Trees." Harvard Business School Background Note 205-060, December 2004. (Revised March 2006.)
- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market... View Details
Keywords: Decision Trees; Computational Advertising; Market Segmentation; Analytics and Data Science; E-commerce; Consumer Behavior; Marketplace Matching; Marketing Channels; Digital Marketing
Aouad, Ali, Adam Elmachtoub, Kris J. Ferreira, and Ryan McNellis. "Market Segmentation Trees." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 648–667.
- February 2017
- Background Note
Decision Analysis
By: George Wu and Kathleen McGinn
Describes decision analysis, a systematic approach for analyzing decision problems. A running example illustrates problem structuring (decision trees), probability assessment, endpoint evaluation, “folding back the tree” as a method of analysis, and sensitivity... View Details
Wu, George, and Kathleen McGinn. "Decision Analysis." Harvard Business School Background Note 917-018, February 2017.
- October 2000
- Case
Tree Values
By: Richard S. Ruback and Kathleen Luchs
Describes two alternative tree cutting strategies. The first is to cut all trees that are at least 12 inches in diameter at breast height. The second is to thin the forest by cutting less desirable trees immediately and harvesting the crop trees later. The case... View Details
Keywords: Strategy; Decision Making; Cash Flow; Decision Choices and Conditions; Management Practices and Processes; Value Creation; Forestry Industry
Ruback, Richard S., and Kathleen Luchs. "Tree Values." Harvard Business School Case 201-031, October 2000.
- September 2003 (Revised October 2020)
- Exercise
RetailMax: Role for Cam Archer
By: Kathleen McGinn and Dina Witter
This exercise requires students to enact an internal salary negotiation, taking on the roles of Cam Archer, a star employee, and Regan Kessel, a VP trying to attract the MBA into his department. The exercise presents a one-issue, distributive negotiation that... View Details
Keywords: BATNA; Decision Trees; Negotiation; Compensation and Benefits; Personal Development and Career; Retail Industry
McGinn, Kathleen, and Dina Witter. "RetailMax: Role for Cam Archer." Harvard Business School Exercise 904-024, September 2003. (Revised October 2020.)
- August 1993 (Revised December 1997)
- Background Note
Decision Analysis
Describes decision analysis, a systemic approach for analyzing decision problems. A running example illustrates problem structuring (decision trees), probability assessment and endpoint evaluation, folding back the tree as a method of analysis, and sensitivity... View Details
Wu, George. "Decision Analysis." Harvard Business School Background Note 894-004, August 1993. (Revised December 1997.)
- October 2006 (Revised March 2009)
- Case
Clifford Chance: Repotting the Tree
By: Arthur I Segel, A. Eugene Kohn and Nhat Minh Nguyen
Clifford Chance, LLP, a global law firm headquartered in London, needs to make a decision whether to stay in the central business district of London or move to a redeveloped business park at Canary Wharf, three miles outside of central London. Peter Charleton, head of... View Details
Keywords: Buildings and Facilities; Business Headquarters; Decision Choices and Conditions; Geographic Location; Logistics; London
Segel, Arthur I., A. Eugene Kohn, and Nhat Minh Nguyen. "Clifford Chance: Repotting the Tree." Harvard Business School Case 207-073, October 2006. (Revised March 2009.)
- TeachingInterests
Decision Making Under Uncertainty
By: David E. Bell
Many of the decisions we face are made complicated by having uncertain consequences: how should I set my inventory when I don’t know what demand will be, should I refinance my mortgage when rates might go lower, how big a bet shall I make in a new business, and so... View Details
- October 2012
- Supplement
Aqua Bounty Courseware
By: Lucy White and Steve Burn-Murdoch
Valuation of a pre-revenue biotech company at IPO using probability trees and real option techniques. Company is based in Massachusetts and lists in London on AIM. Products are genetically-modified fast-growing salmon for fish farmers and disease-prevention drugs and... View Details
- September 2012 (Revised January 2014)
- Case
Aqua Bounty
By: Lucy White and Stephen Burn-Murdoch
Valuation of a pre-revenue biotech company at IPO using probability trees and real option techniques. Company is based in Massachusetts and lists in London on AIM. Products are genetically-modified fast-growing salmon for fish farmers and disease-prevention drugs and... View Details
Keywords: IPO; Valuation; Real Options; Decision Tree; Biotech; Genetically Modified; Salmon; Entrepreneurship; Finance; Agriculture and Agribusiness Industry; Biotechnology Industry; North and Central America; Europe; South America
White, Lucy, and Stephen Burn-Murdoch. "Aqua Bounty." Harvard Business School Case 213-047, September 2012. (Revised January 2014.)
- 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).
- 2017
- Working Paper
Machine Learning Methods for Strategy Research
By: Mike Horia Teodorescu
Numerous applications of machine learning have gained acceptance in the field of strategy and management research only during the last few years. Established uses span such diverse problems as strategic foreign investments, strategic resource allocation, systemic risk... View Details
Keywords: Machine Learning; Natural Language Processing; Classification; Decision Trees; Strategic Decisions; Strategy; Research; Information Technology
Teodorescu, Mike Horia. "Machine Learning Methods for Strategy Research." Harvard Business School Working Paper, No. 18-011, August 2017. (Revised October 2017.)
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
- 2020
- Working Paper
Machine Learning for Pattern Discovery in Management Research
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a... View Details
Keywords: Machine Learning; Theory Building; Induction; Decision Trees; Random Forests; K-nearest Neighbors; Neural Network; P-hacking; Analytics and Data Science; Analysis
Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised June 2020.)
- 01 Sep 2008
- News
Raiffa Honored for Life’s Work in Decision Analysis
mathematician by training, Raiffa was an originator of the decision tree and did extensive research on negotiations and choice-making in complex and ambiguous situations. Raiffa was an adviser to the Kennedy... View Details
- 2018
- Working Paper
Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning
By: Xiaojia Guo, Yael Grushka-Cockayne and Bert De Reyck
Problem definition: In collaboration with Heathrow Airport, we develop a predictive system that generates quantile forecasts of transfer passengers’ connection times. Sampling from the distribution of individual passengers’ connection times, the system also produces... View Details
Keywords: Quantile Forecasts; Regression Tree; Copula; Passenger Flow Management; Data-driven Operations; Forecasting and Prediction; Data and Data Sets
Guo, Xiaojia, Yael Grushka-Cockayne, and Bert De Reyck. "Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning." Harvard Business School Working Paper, No. 19-040, October 2018.
- November 2015 (Revised October 2017)
- Case
Dollar General Bids for Family Dollar
By: Jonas Heese, Paula A. Price, Suraj Srinivasan and David Lane
In spring 2015, Dollar General's CEO Rick Dreiling was looking ahead to retiring at year's end but worried about ensuring continued growth for the company he had built since 2008 into a market leader in the U.S. discount retail world. Dollar General operated over... View Details
Keywords: Dollar General; Family Dollar; Dollar Tree; Antitrust; Board Of Directors; Activist Investors; Federal Trade Commission; Acquisition; Valuation; Corporate Strategy; Retail Industry; United States
Heese, Jonas, Paula A. Price, Suraj Srinivasan, and David Lane. "Dollar General Bids for Family Dollar." Harvard Business School Case 116-007, November 2015. (Revised October 2017.)
- April 2016 (Revised June 2017)
- Teaching Note
Dollar General Bids for Family Dollar
By: Jonas Heese, Paula A. Price and Suraj Srinivasan
In spring 2015, Dollar General CEO Rick Dreiling was looking ahead to retiring at year's end but worried about ensuring continued growth for the company he had built since 2008 into a market leader in the U.S. discount retail world. Dollar General operated over 11,500... View Details
- January 2021
- Article
Machine Learning for Pattern Discovery in Management Research
By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect... View Details
Keywords: Machine Learning; Supervised Machine Learning; Induction; Abduction; Exploratory Data Analysis; Pattern Discovery; Decision Trees; Random Forests; Neural Networks; ROC Curve; Confusion Matrix; Partial Dependence Plots; AI and Machine Learning
Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Strategic Management Journal 42, no. 1 (January 2021): 30–57.