Assistant Professor of Technology and Operations Management
Ville Satopää is an Assistant Professor of Technology and Operations Management at INSEAD. Before joining INSEAD, Ville received his MA and Ph.D. degrees in Statistics from the Wharton School of the University of Pennsylvania. He also holds a BA in Mathematics and Computer Sciences from Williams College.
In terms of research, Ville is an applied Bayesian statistician. His research explores different areas of forecasting: judgmental and statistical forecasting, modeling crowdsourced predictions, combining and evaluating different predictions, and information elicitation. This involves developing general theory and methodology but also specific projects that analyse real-world data, such as hospital mortality rates, domestic tourism, or urban crime.
At INSEAD Ville teaches Business Model Analysis & Innovation (MBA), Bayesian Analysis (Ph.D), Discrete Stochastic Processes (Ph.D.), and Artificial Intelligence in Business (Executive Ed.). He has also co-developed an online course called Transforming Business with Artificial Intelligence (Executive Ed.).
Ville has several research papers published in the top statistics journals (e.g., the Journal of American Statistical Association and Annals of Applied Statistics), top management journals (e.g., Management Science and Operations Research), and top field journals (e.g., International Journal of Forecasting and Health Services Research). His research has been acknowledged with various awards, including winning the Section on Bayesian Statistical Science Student Paper Competition in 2015 and being selected as a runner-up for Decision Analysis Society (DAS) 2020 Student Paper Award and as a finalist for the Best Paper Award by the 2020 INFORMS Workshop on Data Mining and Decision Analytics. In the MBA programme, he has received the Deans' Commendation for Excellence in Teaching multiple times and has also won the Best Teacher Award.
Powell, B., Satopää, V. A., MacKay, N., and Tetlock, P. (2022) "Skew-Adjusted Extremized-Mean: A Simple Method for Identifying and Learning From Contrarian Minorities in Groups of Forecasters."
Decision, Special issue in judgment and decision research on the wisdom of the crowds (Paper, SSRN).
- Palley, A., and Satopää, V. A. (2022) "Boosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions"
Accepted to Management Science (SSRN, R-Package)
- Satopää, V. A. (2021) “Improving the Wisdom of Crowds with Analysis of Variance of Predictions of Related Outcomes.”
International Journal of Forecasting (Paper, SSRN)
- Satopää, V. A., Salikhov, M., Tetlock, P., and Mellers, B. (2021) "Bias, Information, Noise: The BIN Model of Forecasting"
Management Science (Paper, SSRN, R-Package)
- Satopää, V. A., Jensen, S. T., Pemantle, R., and Ungar, L. H. (2017) “Partial Information Framework: Aggregating Estimates from Diverse Information Sources.”
The Electronic Journal of Statistics (Paper)
- George, E., Rockova, V., Rosenbaum, P. R., Satopää, V. A., Silber, J. H. (2017) “Mortality Rate Estimation and Standardization for Public Reporting: Medicare’s Hospital Compare.”
Journal of the American Statistical Association (Paper)
- Silber, J. H., Satopää, V. A., Rockova, V., Wang, W., Hill, A., Even-Shoshan, O., George, E., and Rosenbaum, P. R. (2016) “Improving Medicare’s Hospital Compare Mortality Model.”
Health Services Research (Paper)
- Ernst, P., Pemantle, R., Satopää, V. A., and Ungar, L. H. (2016) “Bayesian Aggregation of Two Forecasts in the Partial Information Framework.”
Statistics & Probability Letters (Paper)
- Satopää, V. A., Pemantle, R., and Ungar, L. H. (2016) “Modeling Probability Forecasts via Information Diversity.”
Journal of the American Statistical Association (Paper, Supplementary Material, Code)
- Satopää, V. A. (2016) Invited discussion of “Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations and Forecast Rankings” by Werner Ehm, Tilmann Gneiting, Alexander Jordan, and Fabian Krüger.
The Journal of the Royal Statistical Society: Series B
- Satopää, V. A., Jensen, S. T., Mellers, B. A., Tetlock, P. E., and Ungar, L. H. (2014). “Probability Aggregation in Time-Series: Dynamic Hierarchical Modeling of Sparse Expert Beliefs.”
Annals of Applied Statistics (Paper)
--- Winner of the Section on Bayesian Statistical Science (SBSS) Student Paper Competition in 2015.
- Satopää, V. A., Baron, J., Foster, D. P., Mellers, B. A., Tetlock, P. E., and Ungar, L. H. (2014). “Combining Multiple Probability Predictions Using a Simple Logit Model.”
International Journal of Forecasting (Paper)
- Klingenberg, B. and Satopää, V. A. (2013). “Simultaneous Confidence Intervals for Comparing Margins of Multivariate Binary Data.”
Computational Statistics & Data Analysis (Paper; Supplementary Material, (Fake) Data Set A and B of AEs, R/C++ Code for Restricted MLE, R/C++ Code for restricted GEE)
- Satopää, V. A. and De Veaux, R. D. (2012). “A Robust Boosting Algorithm for Chemical Modeling.”
Current Analytical Chemistry (Paper)
- Ungar, L. H., Mellers, B., Satopää, V. A., Tetlock, P. E., and Baron, J. (2012). “The Good Judgment Project: A Large Scale Test of Different Methods of Combining Expert Predictions.”
In 2012 AAAI Fall Symposium Series (Paper)
- Satopää, V. A., Albrecht, J., Irwin, D., and Raghavan, B. (2011). “Finding a “Kneedle” in a Haystack: Detecting Knee Points in System Behavior.”
In Proceedings of the Third IEEE Workshop on Simplifying Complex Networks for Practitioners (Simplex) (Paper, Code)
Papers Under Review:
- Lim, F., Chun, S.Y., and Satopää, V. A. “Loyalty Currency and Mental Accounting: Do Consumers Treat Points Like Money?" (SSRN)
--- Major Revision in Manufacturing & Service Operations Management
- Bertani, N., Jensen, S., and Satopää, V. A. "Joint Bottom-Up Method for Grouped Time-Series: Application to Australian Tourism" (SSRN)
--- Best Paper Award Finalist for 2020 INFORMS Workshop on Data Mining and Decision Analytics
--- Major Revision in Operations Research
- Keppo, J., and Satopää, V. A. "Bayesian Herd Detection for Dynamic Data” (SSRN)
--- Major Revision in International Journal of Forecasting
- Bertani, N., Jensen S., and Satopää, V. A. "Spatiotemporal Modeling With Map Features and Socioeconomic Indicators: Application to Urban Crime in Philadelphia"
- Satopää, V. A., Palley, A., Grushka-Cockayne, Y., and Persinger, C. "20 Years of Judgmental Forecasting at Eli Lilly and Company: Using Base Rates to Improve Group Predictions in Drug Development."
- Wang, J., Mellers, B., Ungar, L. U., and Satopää, V. A. "Fair Skill Brier Score: Evaluating Probability Forecasts of Events with Different Numbers of Categorical Outcomes."
- Satopää, V. A., Bertani, N., and Van Wassenhove, L. “Estimating Burden of a Reversible Disease Based on Prevalence and Program Admission Data.”
- Jia, Y., Keppo, J., and Satopää, V. A. "Wisdom of Strategically Diverse Crowds"
- Ville Satopää and Marat Salikhov on the BIN Model of Forecasting. July 3, 2021. Podcast Interview with GlobalGuessing
- Making the Crowd Wiser. August 21, 2020. INSEAD Knowledge.
- Want Better Forecasting? Silence the Noise. November 26, 2019. Podcast Interview with [email protected]
- The Secret Ingredients of ‘Superforecasting’. November 8, 2019. INSEAD Knowledge.
- Disrupting business models is not enough. We need tech innovation too. March 15, 2018. World Economic Forum.
- Warning: Do Not Just Average Predictions. July 13, 2017. INSEAD knowledge.
- After the surprise of 2016, here’s how pollsters can do better in predicting election results. May 31, 2017. Washington Post.
- Improving the Accuracy of Hospital Rankings. May 12, 2017. INSEAD knowledge.
- New analysis finds Medicare program underestimates heart attack mortality rates. April 19, 2017. American Statistical Association.
See also my profile/interview at The Native Society.