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- March 2021
- Article
Bayesian Signatures of Confidence and Central Tendency in Perceptual Judgment
By: Yang Xiang, Thomas Graeber, Benjamin Enke and Samuel Gershman
This paper theoretically and empirically investigates the role of Bayesian noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the... View Details
Xiang, Yang, Thomas Graeber, Benjamin Enke, and Samuel Gershman. "Bayesian Signatures of Confidence and Central Tendency in Perceptual Judgment." Attention, Perception, & Psychophysics (March 2021): 1–11.
- March 2022
- Article
Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless... View Details
Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods
Menchetti, Fiammetta, and Iavor Bojinov. "Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models." Annals of Applied Statistics 16, no. 1 (March 2022): 414–435.
- Article
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
By: Dylan Slack, Sophie Hilgard, Sameer Singh and Himabindu Lakkaraju
As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by... View Details
Keywords: Black Box Explanations; Bayesian Modeling; Decision Making; Risk and Uncertainty; Information Technology
Slack, Dylan, Sophie Hilgard, Sameer Singh, and Himabindu Lakkaraju. "Reliable Post hoc Explanations: Modeling Uncertainty in Explainability." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- 2015
- Article
A Bayesian Framework for Modeling Human Evaluations
By: Himabindu Lakkaraju, Jure Leskovec, Jon Kleinberg and Sendhil Mullainathan
Lakkaraju, Himabindu, Jure Leskovec, Jon Kleinberg, and Sendhil Mullainathan. "A Bayesian Framework for Modeling Human Evaluations." Proceedings of the SIAM International Conference on Data Mining (2015): 181–189.
- November 2007
- Background Note
Bayesian Estimation & Black-Litterman
By: Joshua D. Coval and Erik Stafford
Describes a practical method for asset allocation that is more robust to estimation errors than the traditional implementation of mean-variance optimization with sample means and covariances. The Bayesian inspired Black-Litterman model is described after introducing... View Details
Coval, Joshua D., and Erik Stafford. "Bayesian Estimation & Black-Litterman." Harvard Business School Background Note 208-085, November 2007.
- 2018
- Working Paper
Bayesian Ensembles of Binary-Event Forecasts: When Is It Appropriate to Extremize or Anti-Extremize?
By: Kenneth C. Lichtendahl Jr., Yael Grushka-Cockayne, Victor Richmond R. Jose and Robert L. Winkler
Many organizations face critical decisions that rely on forecasts of binary events. In these situations, organizations often gather forecasts from multiple experts or models and average those forecasts to produce a single aggregate forecast. Because the average... View Details
Keywords: Forecast Aggregation; Linear Opinion Pool; Generalized Additive Model; Generalized Linear Model; Stacking.; Forecasting and Prediction
Lichtendahl, Kenneth C., Jr., Yael Grushka-Cockayne, Victor Richmond R. Jose, and Robert L. Winkler. "Bayesian Ensembles of Binary-Event Forecasts: When Is It Appropriate to Extremize or Anti-Extremize?" Harvard Business School Working Paper, No. 19-041, October 2018.
- 2013
- Dissertation
Designing Freemium: A Model of Consumer Usage, Upgrade, and Referral Dynamics
By: Clarence Lee, Vineet Kumar and Sunil Gupta
Abstract. Over the past decade "freemium" (free + premium) has become the dominant business model among internet start-ups for its ability to acquire and monetize a large install-base with limited marketing resources. Freemium is a hybrid strategy where a firm offers... View Details
- 2016
- Article
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making
By: Himabindu Lakkaraju and Jure Leskovec
Lakkaraju, Himabindu, and Jure Leskovec. "Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making." Advances in Neural Information Processing Systems (NeurIPS) 29 (2016).
- Article
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
By: Wanqian Yang, Lars Lorch, Moritz Graule, Himabindu Lakkaraju and Finale Doshi-Velez
Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such constraints... View Details
Yang, Wanqian, Lars Lorch, Moritz Graule, Himabindu Lakkaraju, and Finale Doshi-Velez. "Incorporating Interpretable Output Constraints in Bayesian Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) 33 (2020).
- 22 Oct 2020
- Working Paper Summaries
Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models
Keywords: by Fiammetta Menchetti and Iavor Bojinov
- 2016
- Working Paper
Paying (for) Attention: The Impact of Information Processing Costs on Bayesian Inference
By: Scott Duke Kominers, Xiaosheng Mu and Alexander Peysakhovich
Human information processing is often modeled as costless Bayesian inference.
However, research in psychology shows that attention is a computationally costly and potentially limited resource. We study a Bayesian individual for whom computing posterior beliefs is... View Details
Kominers, Scott Duke, Xiaosheng Mu, and Alexander Peysakhovich. "Paying (for) Attention: The Impact of Information Processing Costs on Bayesian Inference." Working Paper, February 2016.
- TeachingInterests
Marketing Models Doctoral Seminar
This course is a doctoral level course on Quantitative Marketing. We will cover methodological as well as substantive topics this semester. Methodological topics include: Choice models, Entry and Exit models, Dynamic structural models, Bayesian estimation methods... View Details
- March 2015
- Article
Signaling to Partially Informed Investors in the Newsvendor Model
By: William Schmidt, Vishal Gaur, Richard Lai and Ananth Raman
We investigate a puzzling phenomenon in which firms make investment decisions that purposefully do not maximize expected profits. Using an extension to the newsvendor model, we focus on a relatively common scenario in which the firm's investor has imperfect information... View Details
Schmidt, William, Vishal Gaur, Richard Lai, and Ananth Raman. "Signaling to Partially Informed Investors in the Newsvendor Model." Production and Operations Management 24, no. 3 (March 2015): 383–401.
- 2016
- Article
Does volunteering improve well-being?
By: A.V. Whillans, Scott C. Seider, Lihan Chen, Ryan J. Dwyer, Sarah Novick, Kathryn J. Gramigna, Brittany A. Mitchell, Victoria Savalei, Sally S. Dickerson and Elizabeth W. Dunn
Does volunteering causally improve well-being? To empirically test this question, we examined one instantiation of volunteering that is common at post-secondary institutions across North America: community service learning (CSL). CSL is a form of experiential learning... View Details
Whillans, A.V., Scott C. Seider, Lihan Chen, Ryan J. Dwyer, Sarah Novick, Kathryn J. Gramigna, Brittany A. Mitchell, Victoria Savalei, Sally S. Dickerson, and Elizabeth W. Dunn. "Does volunteering improve well-being?" Comprehensive Results in Social Psychology 1, nos. 1-3 (2016): 35–50.
- March 2019
- Article
A Structural Analysis of the Role of Superstars in Crowdsourcing Contests
By: Shunyuan Zhang, Param Singh and Anindya Ghose
We investigate the long-term impact of competing against superstars in crowdsourcing contests. Using a unique 50-month longitudinal panel data set on 1677 software design crowdsourcing contests, we illustrate a learning effect where participants are able to improve... View Details
Keywords: Crowdsourcing Contests; Superstar Effect; Bayesian Learning; Utility; Economics Of Information System; Dynamic Structural Model; Dynamic Programming; Markov Chain; Monte Carlo; Learning; Competition; Performance Improvement
Zhang, Shunyuan, Param Singh, and Anindya Ghose. "A Structural Analysis of the Role of Superstars in Crowdsourcing Contests." Information Systems Research 30, no. 1 (March 2019): 15–33.
- June 2018
- Article
Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged
By: Clarence Lee, Elie Ofek and Thomas Steenburgh
We study how digital service firms can develop an active customer base, focusing on two questions. First, how does the way that customers use the service postadoption to meet their own needs (personal usage) and to interact with one another (social usage) vary across... View Details
Keywords: Customer Engagement; Adoption Routes; Word-of-Mouth; Digital Marketing; Bayesian Estimation; Customers; Communication; Consumer Behavior; Marketing; Internet and the Web; Analytics and Data Science
Lee, Clarence, Elie Ofek, and Thomas Steenburgh. "Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged." Management Science 64, no. 6 (June 2018): 2473–2495. (Lead Article.)
- August 2021
- Article
Multiple Imputation Using Gaussian Copulas
By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use... View Details
Hollenbach, F.M., I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward, and A. Volfovsky. "Multiple Imputation Using Gaussian Copulas." Special Issue on New Quantitative Approaches to Studying Social Inequality. Sociological Methods & Research 50, no. 3 (August 2021): 1259–1283. (0049124118799381.)
- 2019
- Working Paper
Does Apple Anchor a Shopping Mall? The Effect of the Technology Stores on the Formation of Market Structure
By: Doug J. Chung, Kyoungwon Seo and Reo Song
This study examines the effect of technology stores—company-owned Apple and Microsoft retail stores—on mall configuration. We formulate a structural model that considers the endogenous location decisions of retail stores, taking into account both market characteristics... View Details
Keywords: Apple Store; New Anchor Store; Discrete Game; Complete Information; Multiple Equilibria; GPGPU Technology; Simulator; Bayesian Estimation; Shopping Mall; Spillover
Chung, Doug J., Kyoungwon Seo, and Reo Song. "Does Apple Anchor a Shopping Mall? The Effect of the Technology Stores on the Formation of Market Structure." Harvard Business School Working Paper, No. 20-066, December 2019.
- 01 Apr 2025
- HBS Seminar
Eric Bradlow, University of Pennsylvania
- June 2024
- Article
Stereotypes and Belief Updating
By: Katherine B. Coffman, Manuela Collis and Leena Kulkarni
We explore how feedback shapes, and perpetuates, gender gaps in self-assessments. Participants in our experiment take tests of their ability across different domains. We elicit their beliefs of their performance before and after feedback. We find that, even after the... View Details
Keywords: Beliefs; Stereotypes; Self-assessment; Performance Evaluation; Gender; Cognition and Thinking; Perception; Knowledge Sharing
Coffman, Katherine B., Manuela Collis, and Leena Kulkarni. "Stereotypes and Belief Updating." Journal of the European Economic Association 22, no. 3 (June 2024): 1011–1054.