DAS/SDP Joint Webinar

Technical Implementation at Capital One

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

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

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

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

A Replication Study of Operations Management Experiments in Management Science

Between-prospects comparisons: Regret Theory and Probability Dominance

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

SDP-DAS Webinar - Thinking about Process, Elicitation and Modelling in 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?  

Abstract:

Keeney & Raiffa’s (1976) multiattribute utility aggregates U(X₁,…,X_n). Here X_j 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

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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Utility over Risky Payoff Streams: Normative and Descriptive Approaches

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

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.