Webinars

How to “Improve” Prediction Using Behavior Modification

Event Info

icon_calendar.jpgOctober 5, 2021
icon_clock.jpg9 am Eastern time  
icon_stopwatch.jpgDuration: 1 hour

Speaker

DAS

Galit Shmueli is Tsing Hua Distinguished Professor and Institute Director at the Institute of Service Science, College of Technology Management, National Tsing Hua University, Taiwan. Earlier she was Associate Professor at University of Maryland's Smith School of Business, and then the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics & Information Systems at the Indian School of Business.

Prof. Shmueli’s research focuses on statistical and machine learning methodology with applications in information systems and healthcare, and an emphasis on human behavior. Since her 2010 Statistical Science paper “To Explain or To Predict?” (2000+ citations), she's been investigating how predictive methodology can enhance causal explanatory goals, and how causal explanatory methodology can enhance predictive goals. Prof. Shmueli authors multiple books, including the popular textbook Data Mining for Business Analytics and has over 100 publications in peer-reviewed journals and books. 

Prof. Shmueli teaches courses on data mining, forecasting analytics, interactive visualization, research methods, and other business analytics topics. Her online teaching videos are highly subscribed, and she has won multiple teaching awards.

Prof. Shmueli is the inaugural Editor-in-Chief of the INFORMS Journal on Data Science, and has served on editorial boards of top journals in statistics and information systems. She is an IMS Fellow and ISI elected member.

Many internet platforms that collect behavioral big data use it to predict user behavior for internal purposes and for their business customers (e.g., advertisers, insurers, security forces, governments, political consulting firms) who utilize the predictions for personalization, targeting, and other decision-making. Improving predictive accuracy is therefore extremely valuable. Data science re- searchers design algorithms, models, and approaches to improve prediction. Prediction is also improved with larger and richer data. Beyond improving algorithms and data, platforms can stealthily achieve better prediction accuracy by “pushing” users’ behaviors towards their predicted values, using behavior modification techniques, thereby demonstrating more certain predictions. Such apparent “improved” prediction can unintentionally result from employing reinforcement learning algorithms that combine prediction and behavior modification. This strategy is absent from the machine learning and statistics literature. Investigating its properties requires integrating causal with predictive notation. To this end, we incorporate Pearl’s causal do(.) operator into the predictive vocabulary. We then decompose the expected prediction error given behavior modification, and identify the components impacting predictive power. Our derivation elucidates implications of such behavior modification to data scientists, platforms, their customers, and the humans whose behavior is manipulated. Behavior modification can make users’ behavior more predictable and even more homogeneous; yet this apparent predictability might not generalize when customers use predictions in practice. Outcomes pushed towards their predictions can be at odds with customers’ intentions, and harmful to manipulated users.

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Event Info

icon_calendar.jpg May 20, 2021
icon_clock.jpg12noon ET
icon_stopwatch.jpgDuration: 1 hour

Speaker

DAS Yan Chen is the Daniel Kahneman Collegiate Professor in the School of Information at University of Michigan, and Distinguished Visiting Professor of Economics at Tsinghua University. Her research interests are in behavioral and experimental economics, market and mechanism design, information economics, and public economics. She conducts both theoretical and experimental research. She is a former president of the Economic Science Association, an international organization of experimental economists. Chen has published in leading economics and management journals, such as the American Economic Review, Journal of Political Economy, Journal of Economic Theory, and Management Science, and general interest journals such as the Proceedings of the National Academy of Sciences. She serves as a Department Editor of Management Science

Utility over Risky Payoff Streams: Normative and Descriptive Approaches

Event Info

icon_calendar.jpg March 23, 2021
icon_clock.jpg12noon ET
icon_stopwatch.jpgDuration: 1 hour

Speaker

DAS Manel Baucells teaches Quantitative Analysis courses in Darden’s MBA and Executive Education programs. Manel research focuses on incorporating psychological realism into consumer behavior models by considering factors such as anticipation, reference price comparison, mental accounting, range effects, and satiation. He serves as department editor for the journal Management Science and associate editor for Operations Research.

Co-authors: Manel Baucells, Michal Lewandowski, and Krzysztof Kontek

 Abstract: We provide behavioral foundations of a preference model for risky payoff streams, a broad domain having timed lotteries, income streams under certainty, and repeated lotteries as special cases. Following expected utility and the notion that time is perceived as inherently uncertain, we inadvertently rediscover Bell's (1974) model. To this bedrock, we add the notion that preferences are affected by range effects. The result is a behavioral model with a broad domain and consistent with a plethora of phenomena (bias towards short payback periods, the four-fold patterns for risk and time, preference reversals for risk and time, temporal patterns of decreasing or increasing impatience, and magnitude effects).



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Properties of Utility Functions for Money

Event Info

icon_calendar.jpg January 29, 2021
icon_clock.jpg12noon EST
icon_stopwatch.jpgDuration: 1 hour

Speaker

David Bell Professor David Bell

David E. Bell is a Baker Foundation Professor at Harvard Business School.  He has a BA degree from Oxford University in Mathematics and a PhD from MIT in Operations Research.

In 2001 he was awarded the Ramsey Medal, the highest distinction of the Decision Analysis Society of INFORMS.

This talk is intended as the first of many in which decision analysts give casual talks on topics of interest to them. I will review, in a non-detailed way, three papers that I have written (during my career, i.e. not recently, and for the most part uncelebrated) that concern utility functions for money. An appropriate audience would be decision analysts with some research interest in the properties of utility functions. Though most of my published work has concerned multiattribute utility, this talk will not, except tangentially. It will also not be a general review of decision analysis.

Listen to a replay here