Webinars

SDP & DAS Joint Webinar——Strategic Decision Making: New Frontiers in Research and Practice
 

Event Info

icon_calendar.jpgMay 21st, 2025
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at 8AM PT

icon_stopwatch.jpgDuration: 1 hour
REGISTER HERE

Speaker

Mark Healey is Professor of Strategic Management and Head of the Innovation Management and Policy Division at Alliance Manchester Business School, University of Manchester, UK.  His research focuses on managerial and organizational cognition, particularly the role of cognition and emotion in strategic adaptation.  Mark's research has appeared in some of the world's leading management journals, including Academy of Management Journal, Academy of Management Review and Strategic Management Journal.  His research has featured regularly in the business press, including The Financial Times, Forbes, and Harvard Business Review.  He has advised senior leaders and executive teams in various sectors, from financial services to advanced manufacturing, on the challenges of thinking and acting strategically in a changing world.  He is an Associate Editor of the Journal of Management Studies and has served on the editorial boards of other leading journals, winning several awards for his services to the scholarly community.

Meet the Speaker Link: 

https://us06web.zoom.us/meeting/register/VvhmC0MxQPOvzd4edtUjuA

Abstract:

The strategic decisions that business leaders make today will shape the innovations, economies, and societies we all live with tomorrow. And yet environmental shocks, geopolitical turbulence, and technological disruptions have made it more difficult than ever to make good strategic decisions. In this talk, I will reflect on advances over the past fifteen years in understanding what makes strategic decisions unique and how managers can make better ones. Key advances in research include greater appreciation of how organizational context, framing and mental representations, and emotional factors shape strategic decisions. In the world of practice, executives face challenges of collective decision making, harnessing digital technologies, and dealing with the strains of external disruptions. An interest in tools and techniques for aiding strategic decision making unites researchers and practitioners. I will conclude by outlining critical questions for the future of strategic decision making.

Two-Stage Evaluation Model for Decision Making under Ambiguity 

Event Info

icon_calendar.jpgMar 26th, 2025
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from 01:00pm to 02:00pm (EST)

icon_stopwatch.jpgDuration: 1 hour
REGISTER HERE

Speaker

ying

Ying He is an associate professor in the Department of Business and Management at the University of Southern Denmark. He obtained his Ph.D. in Information, Risk, and Operations Management from the University of Texas at Austin in 2013. His research focuses on two major areas: decision theory and decision analysis, primarily from an axiomatic perspective, and behavioral operations management. In decision theory and decision analysis, he is particularly interested in decision-making under ambiguity in both static and dynamic settings. Beyond developing theoretical models, he applies these models to various business problems, including those in operations management and finance.

Over the years, he has received several prestigious awards, including the Best Student Paper Award from the Decision Analysis Society (DAS) of INFORMS in 2012, the “Jaffray Lecture” at the Risk, Uncertainty, and Decision (RUD) conference at the Paris School of Economics in 2019, the Best Publication Award from DAS in 2023, and the Decision Analysis Practice Award in 2024. He also served as an elected council member of the Decision Analysis Society of INFORMS from 2021 to 2023.

Abstract: The Two-Stage Evaluation (TSE) model is motivated by revisiting Ellsberg's paradox that has been challenging the Savage's Subjective Expected Utility (SEU) model for decades. Besides accommodating preferences described in Ellsberg's paradox, it also describes Machina's paradoxes in more recent decision theory literature. The TSE differs from other existing descriptive models for decision making under ambiguity by treating uncertainties of risk and ambiguity differently in SEU framework, which belongs to the family of source-dependent preferences documented extensively in behavioral economics literature. Beyond its application in static settings, the TSE can also be extended to dynamic setting while maintaining dynamic consistency. Therefore, one can apply the standard "rolling back the decision tree" approach to solve a problem if the preference is described by such a model.

Join Us for a Fireside Chat on Navigating Conflict and Drama: Removing Barriers to Good Decision-Making

Event Info

icon_calendar.jpgFeb 5th, 2025
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from 08:00 to 09:00 (PT)

icon_stopwatch.jpgDuration: 1 hour

When:   Feb 5, 2025 from 08:00 to 09:00 (PT)

Contact:  Hilda Cherekdjian  info@decisionprofessionals.com

Register here

Speaker

Tutti Taygerly, Executive Coach & Author

Tutti Taygerly coaches tech founders and leaders to break free from cookie-cutter leadership molds and embrace their own intuitive, high-impact style. With a Silicon Valley design mindset, she empowers leaders to dream big, run iterative experiments, and harmonize their inner and outer worlds to achieve transformational results.

A fierce advocate for “others,” Tutti specializes in guiding women, people of color, immigrants, and the neurodiverse to own their stories, amplify their voices, and lead with unapologetic confidence. Before becoming a coach, she spent 22 years as a design leader at Meta, Disney, startups, and top-tier design firms where she learned firsthand the power of creativity and adaptability in fast-paced, high-stakes environments.Tutti is also a writer whose bylines appear in Harvard Business Review, Business Insider, and Fast Company. Tutti holds a Symbolic Systems design degree from Stanford University, which shaped her unique blend of data-driven analysis and human-centered leadership strategies.

Discover how the Karpman Drama Triangle can turn workplace conflicts into powerful opportunities for collaboration and growth. Renowned leadership coach Tutti Taygerly will offer actionable strategies to navigate interpersonal challenges that often hinder effective decision-making. Examples will showcase strategies for addressing workplace challenges, including advocating for the organizational value of decision science, resolving peer and inter-departmental conflicts, and facilitating discussions on the adoption of AI.

This webinar, moderated by Julia Varshavsky, a decision science expert and transformation coach, will show how understanding interpersonal dynamics can drive alignment and deliver impactful outcomes for decision professionals. 

Don’t miss this opportunity to gain fresh perspectives and actionable tools for success!

Reflections on a Decision Analysis Career by 2024 Frank P. Ramsey Medal Winner

Event Info

icon_calendar.jpgNov 13th, 
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9am PST / noon EST / 6pm CET

icon_stopwatch.jpgDuration: 1 hour

Watch replay here!
 
 

Speaker

Samuel E. Bodily is the John Tyler Professor Emeritus of Business Administration at the University of Virginia’s Darden School, He has published textbooks and articles in journals ranging from Harvard Business Review to Management Science. His publications relate to decision and risk analysis, multiattribute utility, forecasting, strategy modeling, revenue management, and eStrategy. He has received several best paper and case-writing awards. He is a past winner of the Decision Sciences International Instructional Award and served as chair of the INFORMS Decision Analysis Society. He has taught numerous executive education programs for Darden and private companies and has consulted widely for business and government entities, He has Ph.D. and S.M. degrees from Massachusetts Institute of Technology and a B.S. in Physics from Brigham Young University. email: bodilys@virginia.edu.

Abstract:  The Medal honors Ramsey, who introduced decision theory, where probability, or degree of belief, was made operationally meaningful as willingness to act. This year’s awardee found this broadly applicable. He will talk about his career research, manifested in scholarly articles; his work in practice, exemplified by consulting and applied articles for business and government audiences; and his teaching, exemplified by cases and technical notes for students, articles on teaching effectiveness, and building a teaching team.  We’ll move into a discussion of the future of Decision Analysis, including what kind of person and record will receive the medal 30 years from now.

A Simplified Method for Value of Information using Constructed Scales

Event Info

icon_calendar.jpgOct 16th, 
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11am EST

icon_stopwatch.jpgDuration: 1 hour
Watch replay here!
 

Speaker

Michael C. Runge is a senior scientist and research ecologist at the U.S. Geological Survey’s Eastern Ecological Science Center at the Patuxent Research Refuge. He earned a Ph.D. in wildlife science in 1999 from Cornell University. His research focuses on the use of decision theory, quantitative analysis, predictive modeling, and optimization to inform wildlife, natural resource, and public health management. He has worked on projects with migratory birds, National Wildlife Refuges, endangered species, marine mammals, and SARS-CoV-2. With a handful of colleagues, he co-designed the decision science curriculum for the U.S. Fish and Wildlife Service’s National Conservation Training Center.

Abstract:

The value of information is a central concept in decision analysis, used to quantify how much the expected outcome of a decision would be improved if epistemic uncertainty could be resolved prior to committing to a course of action. One of the challenges, however, in quantitative analysis of the value of information is that the calculations are demanding, especially in requiring predictions of outcomes as a function of alternative actions and sources of uncertainty. But the concept of value of information is important in early framing of some decisions, before such predictive tools are available. In this presentation, I will describe a constructed measure of the value of information (CVOI), grounded in the algebra of the expected value of perfect information (EVPI), but requiring less of experts and analysts. The CVOI calculation decomposes EVPI into a contribution representing the relevance of the uncertainty to the decision and a contribution representing the magnitude of uncertainty; constructed scales are then proposed for each contribution. I will show some examples of the use of CVOI in natural resource management, discuss some of the subtleties of implementation, and close with some open questions.

**MEMBER ONLY INVITATION**

 

Meet the Speaker: Michael Runge

 

Right after the webinar, we will have a Meet the Speaker Session exclusively for Members of the Society of Decision Professionals.

·     Engage directly with Michael.

·     Network and Meet fellow members and experts in the field in a lively discussion.

·     Ask Questions: Pose your questions directly to our speaker.

·     Share Insights: Offer your perspectives and experiences related to the “A Simplified Method for Value of Information using Constructed Scales”.

Your voice and input are valuable to us, and this Meet the Speaker Session aims to foster a collaborative environment where ideas can be exchanged, and new connections can be made.

 

 Date: October 16, 2024

 Time: 9:00am - 9:30am PT (Immediately following the webinar)

 

Please Note: This session is exclusive to SDP & DAS Members. Membership will be verified ahead of time.

  Click here to RSVP

Abstract:

The value of information is a central concept in decision analysis, used to quantify how much the expected outcome of a decision would be improved if epistemic uncertainty could be resolved prior to committing to a course of action. One of the challenges, however, in quantitative analysis of the value of information is that the calculations are demanding, especially in requiring predictions of outcomes as a function of alternative actions and sources of uncertainty. But the concept of value of information is important in early framing of some decisions, before such predictive tools are available. In this presentation, I will describe a constructed measure of the value of information (CVOI), grounded in the algebra of the expected value of perfect information (EVPI), but requiring less of experts and analysts. The CVOI calculation decomposes EVPI into a contribution representing the relevance of the uncertainty to the decision and a contribution representing the magnitude of uncertainty; constructed scales are then proposed for each contribution. I will show some examples of the use of CVOI in natural resource management, discuss some of the subtleties of implementation, and close with some open questions.

 Toward More Objective Causal Modeling for Decision Support

Event Info

icon_calendar.jpg1pm EST
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Sept 26th, 

icon_stopwatch.jpgDuration: 1 hour
REGISTER HERE!
 

Speaker

Louis Anthony (Tony) Cox Jr., Ph.D., is an Associate Professor of Business Analytics at the University of Colorado Denver, where he has previously held positions as Honorary Full Professor of Mathematics and Clinical Professor of Biostatistics and Informatics. Dr. Cox is the Editor-in-Chief of Risk Analysis: An International Journal and serves on the editorial boards of Decision Analysis and the Journal of Heuristics. He is a Fellow and Edelman Laureate of INFORMS and a member of the National Academy of Engineering. Dr. Cox’s research focuses on applying AI/ML and decision science to risk analysis, with a particular interest in causal inference and decision optimization under uncertainty. He has authored over 300 publications and holds numerous U.S. patents on applications of AI and operations research in telecommunications. His recent work includes the book AI-ML for Decision and Risk Analysis (Springer, 2023).

The work of this talk is published, which is available online below.

https://www.tandfonline.com/doi/full/10.1080/10408444.2024.2399856#abstract 

Abstract:

In recent years, disputed risk management and policy decisions in public health and other complex domains have highlighted the need for more reliable and transparent causal models. Traditional approaches to causal inference often rely on untestable modeling assumptions, which can undermine the credibility and robustness of the resulting models. This webinar will introduce an alternative, data-driven framework for causal modeling that emphasizes objectivity by focusing on empirically testable assumptions. We will explore how Causal Bayesian Networks (CBNs) and influence diagrams can in principle (i.e., if relevant data are collected) be learned and validated from observational data, without the need for speculative counterfactuals or subjective judgments. Using a principle of Invariant Causal Prediction (ICP) and advanced machine learning algorithms, this approach provides a more robust foundation for decision optimization, ensuring that the supporting causal models can be independently verified – and, if necessary, refined and corrected – based on statistical mismatches between predictions and observations. Practical applications of this objective causal modeling approach are illustrated with examples from health risk analysis and decision support. The webinar will also address practical challenges such as dealing with residual confounding, missing data, and measurement errors, offering strategies to improve the reliability and validity of causal inferences and models in complex decision-making.

DAS/SDP Joint Webinar

Technical Implementation at Capital One

Event Info

icon_clock.jpg11am EST
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March 13th, 

icon_stopwatch.jpgDuration: 1 hour
Watch Replay Here!
 

Speaker

Jason R. W. Merrick Professor, Virginia Commonwealth University 

Jason Merrick has worked on projects assessing oil spill and counter-terrorism risk, as well as supply chain agility, design, and sustainability. His research has been published in leading journals like Production and Operations Management, the Journal of Operations Management, Management Science, and the Journal of the American Statistical Association, and he has received grants from the National Science Foundation, the Federal Aviation Administration, the United States Coast Guard, the American Bureau of Shipping, British Petroleum, Booz Allen Hamilton, Deloitte Consulting, and the Makah Tribal Council, amongst others. He has also worked on projects with Proctor & Gamble, Cummins, McKesson, McCormick, Bush Brothers, Takeda, Afton, and an NFL team. He is a former President of the Decision Analysis Society and has a bachelor's in mathematics and computation from Oxford University and a doctorate in operations research from George Washington University.

Moderator

Yael Grushka-Cockayne

President, Decision Analysis Society, INFORMS

Professor Yael Grushka-Cockayne excels in decision analysis, data science, business analytics, and project management. As an acclaimed Darden teacher, her global courses, like "Fundamentals of Project Planning and Management," and "Data Science for Business," boast 300,000+ participants across 200 countries. Recognized among "21 Thought-Leader Professors" in Data Science, Yael's editorial contributions to journals like Management Science underscore her expertise. Previously a marketing director in San Francisco, she now consults for international firms in ed-tech, aerospace, and pharma, including Merck Serono, Pfizer, Eli Lilly, and Heathrow Airport.

 

Abstract: The implementation of new technology can give strategic advantages to companies facing competitive pressures, but such projects are often plagued with delays and dissatisfaction among customers and employees. Using decision analysis, we developed a new strategy for rolling out new technology at Capital One, avoiding the delays experienced by other banks implementing the same technology and saving approximately $30 million while improving customer and employee satisfaction.

 

Using decision-making models to improve the wisdom of the crowd

Event Info

icon_calendar.jpg12pm EST
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January 24th,

icon_stopwatch.jpgDuration: 1 hour
Watch replay here!
Webinar slides 

Speaker

Michael Lee is a Professor of Cognitive Sciences at the University of California, Irvine. He is a former President of the Society of Mathematical Psychology, and winner of the William K Estes Award from that Society. His research involves the development, evaluation, and application of models of cognition including representation, memory, learning, and decision making, with a special focus on individual differences and collective cognition. His research emphasizes the use of naturally occurring behavioral data, and tries to pursue a solution-oriented approach to empirical science, in which the research questions are generated from real-world problems. His research methods focus on probabilistic generative modeling and Bayesian methods of computational analysis.

 

Abstract: The wisdom of the crowd is the idea that aggregated group decisions can outperform most or even all of the individuals in the group. We argue that cognitive models, built on an understanding of people's judgment and decision making, can further improve the wisdom of the crowd in four ways. The first way is that they can infer and upweight expertise among individuals. The second is that they can be used to debias cognitive processes by inferring what people know from how they behave. The third is that they can provide a representational scaffolding for combining knowledge that is multidimensional in nature and distributed across individuals. Finally, cognitive models can maintain the diversity of a crowd by producing predictions that act as surrogates for unavailable behavioral data. We demonstrate these ideas in a range of decision-making settings including probability estimation, ranking, spatial knowledge, competitive bidding, and sequential decision making. We also highlight how studying these applied knowledge aggregation problems helps identify new creative directions in the development of basic theories and models of decision making.

 

DAS/SDP Joint SEminar: 

Ethical Decision Quality: Building an Ethical Decision Culture

Event Info

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December 13th, 

icon_clock.jpg8:00 am PT/ 11:00 am ET
icon_stopwatch.jpgDuration: 1 hour
Register here!
 

Speaker

 

Bio: Dr. Ali E. Abbas is Professor of Industrial and Systems Engineering and Professor of Public Policy, a joint appointment between the USC Viterbi School of Engineering and the USC Price School of Public Policy. He also served as the Director of the Neely Center for Ethical Leadership and Decision-Making, and the Center for Risk and Economic Analysis of Terrorism Events (CREATE).

Before joining USC, Dr. Abbas was the Art Davis Faculty Scholar in the Department of Industrial and Enterprise Systems Engineering, College of Engineering, University of Illinois at Urbana-Champaign. Prior to his time at the University of Illinois, Dr. Abbas was a lecturer in the Department of Management Science and Engineering, Stanford University, where he also earned his Ph.D. in Management Science and Engineering, Ph.D. minor in Electrical Engineering, M.S. in Engineering Economic Systems and Operations Research, and M.S. in Electrical Engineering.

The recipient of multiple awards from the National Science Foundation for his work, Dr. Abbas's research focuses on decision analysis, risk analysis, multiattribute utility theory, and data-based decision-making. He is widely published in books, journals, and conference publications, and has shared his expertise through television appearances, TEDx and other invited talks. He is also associate editor and editor for a wide range of journals including Operations Research, Decision Analysis, IISE Transactions, Decision, and Entropy.

ABSTRACT: How do you assess the ethical quality of decisions? What are the impediments to ethical decision quality in an organization? And how do you build an organization’s ethical decision culture? This book will address these questions by drawing conclusions and insights based on numerous media articles, documentaries, movies, and case studies. Organizational decision-making and ethics have often been treated as different topics. This separation impacts both the ethical quality of decisions and the quality of ethical decisions. The field of decision analysis provides a wealth of tools to help decision-makers achieve clarity of action.

Why We Under-prepare for Disasters: The Enduring Legacy of Howard Kunreuther

Event Info

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Thursday Nov. 9th, 

icon_clock.jpg10am ET/7am PT
icon_stopwatch.jpgDuration: 1 hour
Watch replay here!
 

Speaker

Paul Slovic

(Decision Research & University of Oregon), 

Robert Meyer 

(Wharton)

Erwann Michel-Kerjan

(McKinsey)

Moderator

Robin Dillon-Merrill (Georgetown University)

Abstract: Disasters, both natural and man-made, are inevitable in our world, yet societies often find themselves inadequately prepared to mitigate their impact. Paul Slovic, Bob Meyer and Erwann Michel-Kerjan, long-time colleagues of Howard Kunruether, will discuss the intricate web of human psychology and public policy, exploring the enduring legacy of Howard and his pioneering work in understanding why we consistently under-prepare for disasters.

Howard Kunreuther, a distinguished scholar in the field of risk analysis and decision-making, dedicated his career to uncovering the cognitive biases and behavioral patterns that lead individuals, organizations, and governments to underestimate the importance of disaster preparedness. Drawing on decades of research, this presentation will discuss key factors that contribute to our collective failure in disaster readiness.

We will save time at the end of the discussion for questions but also importantly for others from the audience who may want to share their own reflections on the impact of Howard's work for them.

Finalist presentations of the DAS - SDP Practice Award

Event Info

icon_calendar.jpgWednesday, October 11th
icon_clock.jpg8:00am to 9:30am Pacific Time
icon_stopwatch.jpgDuration: 1.5 hour
Link to the video presentation by Monica Oliveira
https://www.dropbox.com/t/CUu0n0cG1s2IV07y [dropbox.com]

The Decision Analysis Practice Award is sponsored jointly by the Decision Analysis Society and the Society of Decision Professionals. It is given annually to the best decision analysis application, as judged by a panel of members of both Societies. The winner(s) will be announced during the 2023 INFORMS conference, October 15 – 18 in Phoenix, Arizona. This year we are running this session via an open webinar to reflect the dual-society award, and to further promulgate this prestigious prize with our Decision Analysis Society (DAS) and Society of Decision Professionals (SDP) communities

Fostering participation and knowledge construction processes in real settings through decision analysis and collaborative value modelling

Presenter: Mónica Duarte Oliveira

Team Members:

Carlos António Bana e Costa and Ana Vieira

To address the challenge of effectively engaging many stakeholders and experts in real-world decision analysis processes, both for knowledge construction and for stakeholder engagement, we have developed the Collaborative Value Modeling (CVM) framework. The CVM combines large-scale participatory Web-Delphi processes with smaller-scale decision conferencing or workshop processes to promote agreement in different modeling stages of multicriteria decision analysis (MCDA).

A Decision Support for Portfolio Capital Allocation for Internal Capital Market at Dow Chemicals

Presenter: Saurabh Bansal

This project involved a decision analysis application of portfolio management for the $400 annual Internal Capital Allocation Program at Dow Chemicals. The total amount requested across all projects exceeded $600 million, which led Dow to make the project allocation decisions based on cost estimates provided by managers for individual projects. However, this motivated managers to systematically provide low estimates for costs. There was also no incentive for managers to return surplus amount if projects finished with a surplus. We developed a data-driven near optimal mechanism that addressed these issues. It (i) provided a quantification for the variability in actual project costs, (ii) This variability provided a basis to determine a strategic reserve for potential cost overages, (iii) encouraged managers to truthfully report cost estimates. The mechanism was implemented with success at the firm.   

A Multiattribute Decision Model to Evaluate Potential Investments in Near-Earth Object Detection Technologies

Presenter: Ralph Keeney

Team Members: Thomas S. Palley, Victor Richmond R. Jose, Asa Palley and Mario Juric

Asteroids and other near-earth objects (NEOs) pose a significant threat to our planet. Advance detection is essential to respond to any object on a collision course, but detection and tracking technologies require substantial investments. We provide a multiattribute utility framework to analyze which NEO detection technologies offer the best decision alternatives using a stylized model of the uncertainties, objectives, and tradeoffs inherent to decisions involving low-probability, high-consequence events.

A Replication Study of Operations Management Experiments in Management Science

Event Info

icon_calendar.jpgThursday, September 28th, 2023 
icon_clock.jpg12:00 PM EST
icon_stopwatch.jpgDuration: 1 hour
WATCH REPLAY HERE!

Speaker


 

Blair Flicker is an assistant professor of management science at the Moore School. He holds a doctoral degree in management science and an MBA from the University of Texas, Dallas. Flicker’s research is focused on understanding how to best integrate human and algorithmic decision making. Specific interests include behavioral operations management, judgmental forecasting, human-computer interaction and machine learning.

Kyle B. Hyndman is a professor at the Naveen Jindal School of Management at the University of Texas at Dallas. His main research interests are behavioural and experimental economics, though he has done research in industrial organisation, auctions, bargaining and behavioural operations management.

Abstract: Over the last two decades, researchers in operations management have increasingly leveraged laboratory experiments to identify key behavioral insights. These experiments inform behavioral theories of operations management, impacting domains including inventory, supply chain management, queuing, forecasting, and sourcing. Yet, until now, the replicability of most behavioral insights from these laboratory experiments has been untested. We remedy this with the first large-scale replication study in operations management. With the input of the wider operations management community, we identify 10 prominent experimental operations management papers published in Management Science, which span a variety of domains, to be the focus of our replication effort. For each paper, we conduct a high-powered replication study of the main results across multiple locations using original materials (when available and suitable). In addition, our study tests replicability in multiple modalities (in-person and online) due to laboratory closures during the COVID-19 pandemic. Our replication study contributes new knowledge about the robustness of several key behavioral theories in operations management and contributes more broadly to efforts in the operations management field to improve research transparency and reliability.
Between-prospects comparisons: Regret Theory and Probability Dominance

Event Info

icon_calendar.jpgJune 16, 2023 
icon_clock.jpg12:00 PM EST
icon_stopwatch.jpgDuration: 1 hour
Watch replay here.

Speaker

Enrico Diecidue is a Professor of Decision Sciences at INSEAD. In 1996 he obtained his degree in Economics, with a specialisation in Mathematical Economics, from Bocconi University, Italy. He then joined the CentER (Center for Economic Research), Tilburg University, the Netherlands, where he received his PhD in 2001.

His research focuses on decision making under uncertainty. He is interested in the role of regret, aspiration levels, and time in individual decisions. His current research is also addressing the role of groups in complex decisions. Enrico’s research has appeared in leading journals including Decision Analysis, International Economic Review, Journal of Economic Theory, Journal of Mathematical Psychology, Journal of Risk and Uncertainty, Management Science, Mathematical Social Sciences, Theory and Decision. He serves on the Editorial Board of Decision and of Journal of Risk and Uncertainty, and is an Associate Editor for Decision Analysis and for Management Science.

Enrico Diecidue is directing the International Directors Programme and is director for the Strategic Decision Making for Leaders Program Strategic Decision Making for Leaders Program.

Abstract: The most popular models of decision under uncertainty such as expected utility (EU, von Neumann and Morgenstern 1947), and cumulative prospect theory (CPT; Tversky and Kahneman 1992) – assume that the decision maker evaluates each prospect separately and then chooses the prospect with the highest EU (or the highest CPT value). Regret theory (Bell 1982; Fishburn 1982; Loomes and Sugden 1982) is a bold exception in that the forgone alternative does influence the decision maker; hence this theory allows also for between-prospects interactions.

 

In this talk: I present a review of some of the advancements in regret theory (behavioral foundation, quantitative measurement, and empirical performance); I then introduce Probability Dominance. This is an heuristic (choosing the alternative that maximizes the probability of being ahead) that also captures the effects of the forgone alternative, yet considers between-prospects interactions in a way that differs from regret theory. The final part of the talk focuses on evaluating the empirical performance of probability dominance relative to EU, CPT, and regret theory.

 SDP-DAS Webinar-The Multivariate Metalog Distributions: An Introduction With Application to Strategic Decision-Making in Golf

Event Info

icon_calendar.jpgMay 17, 2023 
icon_clock.jpg8:00 AM PT, 11:00 AM ET
icon_stopwatch.jpgDuration: 1 hour
Watch replay here

Link here to the paper mentioned by Tom Keelin during the webinar. 

Speaker

TOM KEELIN | Managing Partner, Keelin Reeds Partners
Tom Keelin is a founder and Managing Partner of Keelin Reeds Partners, a firm that provides strategy, decision analysis, and education services. Previously, over two decades with the Strategic Decisions Group (SDG), Tom co-developed the definition of "decision quality”, led dozens of strategy engagements for leading companies, founded SDG’s life sciences practice, and served as Board Member and Worldwide Managing Director. Later, Tom invented the metalog distributions. Tom is a Fellow and Board Member of the Society of Decision Professionals and a founder and Director of the Decision Education Foundation. He holds three Stanford degrees: BS in Economics and MS and PhD in Engineering-Economic Systems. 
 
Tom has also been an avid amateur golfer since childhood. He was Captain of the Stanford Golf Team as an undergraduate and has maintained a longstanding interest in golf strategy.
Abstract: Continuous multivariate probability distributions are highly relevant to many important decisions, such as how to best allocate resources across a portfolio of correlated financial or R&D assets; which of multiple oil fields to develop; and choice of aim point in sports, military, or space-exploration applications. Traditionally, however, many of us have avoided such distributions because they have been impractical to parameterize, validate, and/or simulate. Here we introduce new practical methods and apply them in a new application area: strategic decision-making in golf.

Because of their desirable properties, quantile-parameterized distributions (QPDs), which include the metalogs, are being used increasingly instead of traditional distributions (normal, student-t, beta, etc.) for practical applications of probability. Application areas include not only decision analysis but diverse fields ranging from environmental conservation to astrophysics.
The metalog distributions and other QPDs give rise to improved multivariate methods -- enabling such distributions to be more accurately specified, easier to elicit, easier to determine from data, easier to validate, and easier to simulate. We illustrate these features with an easy-to-understand practical application: in golf, choosing the best aimpoint can make the difference between winning and losing. 
SDP-DAS Webinar - Thinking about Process, Elicitation and Modelling in Decision Analysis 

Event Info

icon_calendar.jpgApril 26, 2023 
icon_clock.jpg8:00 AM PT, 11:00 AM ET
icon_stopwatch.jpgDuration: 1 hour
Register  here

Listen to replay here

Speaker

Simon French joined the Department of Statistics in August 2011. He moved here from Manchester Business School, where he was Professor of Information and Decision Sciences to take up the Directorship of RISCU, the department's Risk Initiative and Statistical Consultancy Unit. At the beginning of 2017, he handed over the Directorship to Martine Barons and at the same time RISCU was renamed, the Applied Statistics and Risk Unit (AS&RU), of which he remains a member.

Simon's research career began in Bayesian statistics and he was one of the first to apply hierarchical modelling, particularly in the domain of protein crystallography. Nowadays he is better known for his work on decision making, which began with his early work on decision theory and saw several papers on the mathematical foundations of rational decision making and the publication of his 1986 text on Decision Theory. That strand of work still continues in the background: e.g. his book on Statistical Decision Theory, co-authored with David Rios Insua. However, his work has generally become more applied; looking at ways of supporting real decision makers facing major strategic and risk issues.

In 2017 Simon was awarded the Ramsey Medal by the INFORMS Decision Analysis Society for his research in and applications of risk and decision analysis.

Abstract: Decision analysis is much more than maximising an expected utility or value function: we all know that. Nonetheless, we may not reflect as much as we should on the wider, more qualitative processes that surround the analytical and quantitative steps. For instance:

• How and where are different types of knowledge drawn into the process?
• How do we work with and elicit not just numerical subjective probabilities, values and utilities, but softer more structural issues such as the context, models, available data, and appropriate computational methods?
• How do we recognise and address all the uncertainties that relate to a decision and its context?
• How do we report and archive details so that necessary information is available for audit?
• How do we recognise and reflect the wider political and Political processes surrounding the decision?

In discussing these and related questions, I will draw on several other disciplines and schools of thought than usually associated with (Bayesian) decision analysis. It is through the softer, qualitative process steps that we recognise, address and communicate those issues and uncertainties that are not modelled in the quantitative decision analysis, but still need communicating to the decision-makers so that they have a full understanding of the issues when they take their decision.

Multi-Multi-Attribute Utility: the Prettiest Axiomatization of Expected Utility You ever Saw & the Million-$ Question: Row-First or Column-First Aggregation?  

Event Info

icon_calendar.jpgApril 14, 2023 
icon_clock.jpg12:00 PM ET  
icon_stopwatch.jpgDuration: 1 hour
Register here

Speaker

Peter Wakker is professor of decision under uncertainty at Behavioral Econ. of Erasmus School of Econ. (ESE). He works in behavioral economics and on risk/ambiguity. He has published in leading journals in economics, business, medicine, psychology, statistics, and mathematics. He was the best-publishing Dutch economist in 1994, 1998, 2003, and 2007, and collaborated with three Nobel-prize winners. He is the 157th most influential economist, and the 20463th most influential researcher over all disciplines (Ioannidis, Boyack, & Baas 2020 Table 6). Köbberling & Wakker (2005) is among the 50 most influential papers in Journal of Economic Theory (Shell, Borgers, & Pavan (2020). Wakker received a Medical Decision Making Career Achievement Award (2007), the Frank P. Ramsey Medal (2013; highest award of INFORMS Decision Analysis Society), and an Honorary doctorate in economics (University of St. Gallen 2016). He frequently gives advices on insurance in the media. He is a director of the research group Behavioral Economics. Personal website.

Abstract:

Keeney & Raiffa’s (1976) multiattribute utility aggregates U(X1,…,Xn). Here Xj can refer to persons (welfare), timepoints (time preference), events (decision under uncertainty), commodities (consumer theory), etc. In applications one often has to aggregate over two or more kinds of attributes: risk & time, persons & commodities, etc. In classical models with complete separability the order of aggregation then does not matter. This gives our first result: the prettiest axiomatization of discounted expected utility you ever saw. Paradox 1: this is too simple to be true?

Modern behavioral generalizations relax complete separability (= sure-thing principle = time separability etc.) but maintain weak separability (= stochastic dominance = Pareto optimality etc.). Paradox 2: for two or more kinds of attributes this is just impossible.

A century-old forgotten theorem from macro-economics resolves our two modern paradoxes: Nataf (1948). He provided diagnoses and remedies for many ongoing debates, on ex-ante/ex-post fairness, incentive compatibility of random incentives, hedging in ambiguity measurements, equity in Harsanyi’s veil of ignorance, monotonicity in Anscombe-Aumann’s framework, etc. They all amount to the same million-$ question: “row-first or column-first aggregation?” Nataf resolved! 

CO-AUTHORS: Chen Li & Kirsten Rohde

 

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SDP/DAS joint webinar
Decision Making for Enhanced Health Security

Event Info

icon_calendar.jpgFebruary 15, 2023 
icon_clock.jpg11:00 AM ET  
icon_stopwatch.jpgDuration: 1 hour

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Speaker

Gilberto Montibeller is a Professor of Management Science and Director of Executive Education at Loughborough University (UK). He is also a Senior Research Fellow at the Center for Risk and Economic Analysis of Threats and Emergencies at the University of Southern California (USA), an Associate Editor of the INFORMS Decision Analysis Journal, and a member of the SDP Executive Board. Dr. Montibeller is an expert in Behavioral Decision Analysis and Multi-Criteria Decision Analysis with an application focus on Health Decision Analysis and Health Operations. He provides decision consulting for private and public organizations, such as WHO, FAO, Roche, Novartis, DEFRA, the UK Department of Health, among others. In his spare time, he enjoys riding his motorbike along beautiful European roads.

SUMMARY:
Emerging and re-emerging health threats have always been a central theme of our history; the recent COVID-19 pandemic being the latest of these unfortunate events and certainly not the last. The pandemic once again reminded us of the importance of reasoned decision making and responsible leadership for health security. (In fact, did you know the very first decision analysis was conducted on a health security decision problem in the 18th century?)
In this talk I argue that decision analysis has much to contribute to this important category of decision problems, which has specific challenges that must be addressed to support high-quality decisions. I propose a framework for supporting health security decision making that can address competing priorities and key uncertainties, mitigate critical risks in a cost-effective manner and maximize societal value.
Join us to learn about the fascinating true story of the first health security decision analysis and to jointly reflect on how we can improve the decision quality of private and public organizations which are confronted by health threats. Together we, the decision professionals, can help to enhance the health security of our societies.

MODERATOR
EYAS RADDAD is a seasoned drug discovery and development researcher, strategist, and innovator. Since he joined Eli Lilly and Company in 2001, he applied sophisticated data analytics and strategic frameworks to improve decision making in drug discovery and development process.
Over his career, he implemented Decision Analytic techniques in drug development applications. In 2017, Eyas established and led Research Decision Analytics function in Lilly Research Laboratories, with a mission that includes portfolio management, decision consulting and decision education. More recently, he has been serving a COO of multiple early development programs at Eli Lilly, as well as portfolio management role in Diabetes Research.

DAS presents - Frank P. Ramsey Medal winner Professor Ahti Salo, from Aalto University

Addressing Uncertainties through Advances in Scenario and Decision Analysis

Event Info

icon_calendar.jpgFebruary 10, 2023 
icon_clock.jpg12:00 PM ET  
icon_stopwatch.jpgDuration: 1 hour

Speaker

Ahti Salo is a Professor of Systems Analysis at Aalto University, Finland. He received his MSc and DSc degrees in systems and operations research from the Helsinki University of Technology in 1987 and 1992, respectively. He has worked at the Nokia Research Center and the Technical Research Centre of Finland, and he has been a Visiting Professor at the London Business School, Université Paris-Dauphine, and the University of Vienna. His research interests include topics in decision analysis, risk management and foresight methodologies, with applications in energy, healthcare, and safety-critical systems. He has supervised 190 MSc Theses and 27 Doctoral Dissertations, and published more than 180 scientific publications, including 100 refereed papers in leading international journals.

Abstract: In this talk, I first summarize experiences based on my role as a member of the COVID-19 Science Panel which was appointed by the Prime Minister’s Office in Finland to provide science-based support for managing the pandemic. Second, I discuss uses of scenario analysis in setting the stage for strategic decisions, noting that it can be beneficial to employ quantitative approaches to build scenarios that are diverse and comprehensive. Third, I present advances in solving influence diagrams for problems in which the usual ‘no-forgetting’ assumption does not hold or in which cross-cutting logical, resource and risk constraints must be accounted for. Such problems can be solved through Decision Programming (Salo et al., Eur J Oper Res 299:2, 2022) by building the equivalent mixed-integer linear programming formulation which can be solved through standard solvers. 

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Innovations in the Science and Practice of Decision Analysis: The Role of Management Science

Event Info

icon_calendar.jpgApril 6, 2022
icon_clock.jpg12 PM ET  
icon_stopwatch.jpgDuration: 1 hour

Speaker

johannes

James S. Dyer is the Fondren Foundation Centennial Chair in Business in the Department of Information, Risk, and Operations Management at the McCombs School of Business at The University of Texas at Austin. His research and teaching interests include risk management, multiple criteria decision making, and capital budgeting. Dyer is the 2002 winner of the Frank P. Ramsey Medal for distinguished contributions in decision analysis.

johannes

James E. Smith is the Jack Byrne Distinguished Professor in Decision Science at the Tuck School of Business at Dartmouth College. His research and teaching interests include decision analysis and operations research, particularly related to dynamic decisions and dynamic programming. Smith is the 2008 winner of the Frank P. Ramsey Medal for distinguished contributions in decision analysis.

In this talk, we reflect on research in decision analysis that has appeared in Management Science and its impact on decision analysis practice and applications. We consider professional applications of decision analysis in business and government settings as well as everyday conversational applications. This is a reprise of a talk given in May 2019 at the Conference in celebration of the 65th Anniversary of Management Science. The corresponding paper (see link below) was published in the September 2021 issue of Management Science.

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Slides from presentation 
Innovations in the Science and Practice of Decision Analysis: The Role of Management Science

NUDGE YOURSELF TO MAKE BETTER DECISIONS: Developing the Necessary Foundation to Make A Quality Decision

Event Info

icon_calendar.jpgMarch 16, 2022
icon_clock.jpg12 PM ET  
icon_stopwatch.jpgDuration: 1 hour

Speaker

johannes

Johannes Siebert teaches Decision Sciences and Behavioral Economics at MCI | THE ENTREPREUNRIAL SCHOOL®, Innsbruck, Austria. His research objective is to contribute to better informed decision-making of individuals and organizations.

He has published papers in leading international journals such as Operations Research and European Journal of Operational Research. Johannes also works as a business consultant for both public and private organizations in the US and Europe. Three times, he was acknowledged as a finalist in the practice award of the Decision Analysis Society (INFORMS). More details can be found on his personal website JohannesSiebert.com.



The only way that you can purposefully influence anything in your life or business is by your decisions. Ralph Keeney and Johannes Siebert provide practical concepts and useful procedures to appropriately structure any decision that you want to face. This structure guides you to make some decisions worthy of thought directly and indicates the information needed to resolve more complex decisions. This empowers you to make better decisions and improve your life.

Followed by a PANEL DISCUSSION with Ralph Keeney
Ralph L. Keeney is a consultant on decision making and a speaker and author on making smart decisions. His main professional interest is helping individuals, organizations, and governments make smarter decisions.

Previously, he has been a professor at MIT, the University of Southern California, and Duke University and created and managed the decision and risk analysis group at a major consulting firm. He is a member of the U.S. National Academy of Engineering and an author of several books, including Give Yourself a Nudge, which is directly relevant to the seminar.

MODERATOR
Gilberto Montibeller is a Professor of Management Science and Director of Executive Education at Loughborough University (England) and a Visiting Senior Research Fellow at the University of Southern California. He is an expert on multi-criteria decision analysis and behavioral decision analysis. Dr. Montibeller has provided decision analysis consultancy to both private and public organizations in the USA, Britain, Continental Europe, and South America. He has published widely in decision science journals and is Associate Editor of the Informs Decision Analysis journal.

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Decision Analysis for Diversity Equity and Inclusion

Event Info

icon_calendar.jpgMarch 3, 2022
icon_clock.jpg10:30 ET  
icon_stopwatch.jpgDuration: 1 hour

Speaker

DeVries__002_.jpg

Catherine E. De Vries is a professor of political science at Bocconi University, a Research Associate at the Dondena Centre for Research on Social Dynamics and Public Policy, the CLEAN Unit for the Economic Analysis of Crime of the BAFFI-CAREFIN research center and the Bocconi COVID crisis lab. Catherine's work examines the key challenges facing the European continent today, such as Euroscepticism, political fragmentation, migration and corruption. Her my current project examines the conditions under which economic hardship affects support for socially conservative political agendas. This research project (LOSS) is funded through a Consolidator grant by the European Research Council.

Her first book Euroscepticism and the Future of European integration (Oxford University Press) received the European Union Studies Association Best Book in EU Studies Award, and was listed in the Financial Times top-5 books to read about Europe’s future. Her second book Political Entrepreneurs: The Rise of Challenger Parties in Europe, co-authored with Sara Hobolt (LSE), was published with Princeton University Press in 2020. It was featured in the Washington Post Monkey Cage Blog and Politico.

Next to books, Catherine has published numerous articles on topics spanning the fields of political behaviour, comparative European politics and political economy in journals including the American Political Science Review, Annual Review of Political Science, the Journal of Politics and International Organization. Together with Seth Jolly (Syracuse), Catherine hosts the European Politics Online Workshop: www.europw.com

For my societal impact, Catherine was selected a Young Global Leader in the World Economic Forum in 2013. In 2014, She was awarded the Emerging Scholar Award from the Elections, Voting Behavior and Public Opinion Section of the American Political Science Association. For more information see: www.catherinedevries.eu

The values of Diversity, Equity and Inclusion play a central role in academia and in society broadly. In December 2021, the Decision Analysis Society of INFORMS formed a permanent Diversity, Equity and Inclusion (DEI) Committee. The DEI Committee will champion the Diversity, Equity and Inclusion values through explorations on: 1) how to actively promote initiatives that afford an equal playing field for people from historically marginalized groups? 2) how to enhance cultural competence for DEI values to ensure equitable access to society opportunities? We invite all INFORMS community to join us  to learn from internationally renowned experts Catherine De Vries, Professor of Political Science at Bocconi University, Anahita Khojandi (University of Tennessee) and Susan Martonosi (Harvey Mudd College). INFORMS DEI Representatives and DAS-DEI committee members Allison Reilly (University of Maryland), Andrea Hupman (University of Missouri), Gül Okudan Kremer (Iowa State University), and Jun Zhuang (University of New York) will also partake as panelists in the session illustrating DEI values, and the INFORMS viewpoint and the first initiatives of the DAS.  

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Event Info

icon_calendar.jpgJanuary 27, 2022
icon_clock.jpg12pm ET  
icon_stopwatch.jpgDuration: 1 hour

Speaker

Professor Yael Grushka-Cockayne's research and teaching activities focus on data science, forecasting, project management and behavioral decision-making. Her research is published in numerous academic and professional journals, and she is a regular speaker at international conferences in the areas of decision analysis, project management and management science. She is also an award-winning teacher, winning the Darden Morton Leadership Faculty Award in 2011, the University of Virginia's Mead-Colley Award in 2012 and the Darden Outstanding Faculty Award and Faculty Diversity Award in 2013. In 2015, she won the University of Virginia All University Teaching Award. Grushka-Cockayne teaches the core "Decision Analysis" course, an elective she designed on project management and an elective on data science. She is the leader of the open enrollment courses "Project Management for Executives" and "The Women's Leadership Program."

Before starting her academic career, she worked in San Francisco as a marketing director of an Israeli ERP company. As an expert in the areas of project management, she has served as a consultant to international firms in the aerospace and pharma industries. She is a UVA Excellence in Diversity fellow and a member of INFORMS, the Decision Analysis Society, the Operational Research Society and the Project Management Institute (PMI). She is an associate editor at Management Science and Operation Research and the secretary/treasurer of the INFORMS Decision Analysis Society.

In 2014, Grushka-Cockayne was named one of "21 Thought-Leader Professors" in Data Science. Her recent "Fundamentals of Project Planning and Management" Coursera MOOC had over 100,000 enrolled, across 200 countries worldwide.

This webinar is co-sponsored with the Society of Decision Professionals

This webinar will present the story of the rescue of the soccer team of twelve boys and their coach from the Tham Luang Caves in Thailand in July 2018.  The A case focuses on the first 9 days of the search, from when the boys went missing to when they were found. The B case focuses on the planning and execution of the rescue mission. Through our discussion of the cases, we will debate the role of leadership and communications, decision making in moments of crisis, the efficiency of ad hoc teams, the project planning and execution strategies in high-risk situations with low chances of success. This unbelievable rescue story will showcase some key decision making tendencies and strategies, and demonstrate how cultural, experience, and language differences can be bridged when working to accomplish a mutual goal.

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Webinar slides


Multivariate almost stochastic dominance: review and current work

Event Info

icon_calendar.jpgDecember 2, 2021
icon_clock.jpg12pm ET  
icon_stopwatch.jpgDuration: 1 hour

Speaker

DAS

Ilia M. Tsetlin is a Professor of Decision Sciences at INSEAD. His teaching and research interests are in prescriptive decision making emerging from normative analysis. Two recent research focuses are generic properties of preferences (multiattribute utility and stochastic dominance) and search, deadlines, and the role of uncertainty. Other research streams are related to negotiation, auction theory and collective choice. His work has been published in a number of academic journals including Management Science, Operations Research, Journal of Risk and Uncertainty, Journal of Economic Theory, Psychological Review, Games and Economic Behavior, and Social Choice and Welfare. He currently serves as a Department Editor in Management Science.

Stochastic dominance (SD) is a useful concept, especially in a multivariate context, where assessing multiattribute utility is challenging and different stakeholders might have divergent views. However, applying multivariate SD is difficult for three reasons: First, often distributions, even if fully known, cannot be ranked (e.g., by first-order SD).  Second, easily verifiable integral conditions for multivariate SD often do not exist. Third, full information about multivariate distributions (including dependence structure) is difficult (and often impossible) to obtain.  We will discuss how the concept of almost SD helps to overcome these challenges.

Co-authors: Alfred Müller, Marco Scarsini, Robert Winkler

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Paper: Decisions with Several Objectives under Uncertainty: Sufficient Conditions for Multivariate Almost Stochastic Dominance

 
 
 

Event Info

icon_calendar.jpgOctober 5, 2021
icon_clock.jpg9am EDT  
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.