Journal ArticleDOI
Learning Preferences Under Noise and Loss Aversion: An Optimization Approach
TLDR
This work uses robust and integer optimization in an adaptive and dynamic way to determine preferences from data that are consistent with human behavior to address human inconsistency.Abstract:
Preference learning has been a topic of research in many fields, including operations research, marketing, machine learning, and behavioral economics. In this work, we strive to combine the ideas from these different fields into a single methodology to learn preferences and make decisions. We use robust and integer optimization in an adaptive and dynamic way to determine preferences from data that are consistent with human behavior. We use integer optimization to address human inconsistency, robust optimization and conditional value at risk (CVaR) to address loss aversion, and adaptive conjoint analysis and linear optimization to frame the questions to learn preferences. The paper makes the following methodological contributions: to the robust optimization literature by proposing a method to derive uncertainty sets from adaptive questionnaires, to the marketing literature by using the analytic center of discrete sets (as opposed to polyhedra) to capture errors and inconsistencies, and to the risk modeling...read more
Citations
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Journal Article
Digital Nudging: Altering User Behavior in Digital Environments
TL;DR: A systematic literature review is conducted and a comprehensive overview of relevant psychological effects and exemplary nudges in the physical and digital sphere are provided to provide a valuable basis for researchers and practitioners that aim to study or design information systems and interventions that assist user decision making on screens.
Journal ArticleDOI
Decision Making Under Uncertainty When Preference Information Is Incomplete
Benjamin Armbruster,Erick Delage +1 more
TL;DR: This work considers the problem of optimal decision making under uncertainty but assumes that the decision maker's utility function is not completely known, and forms tractable formulations for such decision-making problems as robust utility maximization problems, as optimization problems with stochastic dominance constraints, and as robust certainty equivalent maximizations problems.
Journal ArticleDOI
Feature-Based Dynamic Pricing
TL;DR: This work considers the problem faced by a firm that receives highly differentiated products in an online fashion and needs to price these products to sell them to its customer base.
Journal ArticleDOI
Feature-Based Dynamic Pricing
TL;DR: This work considers the problem faced by a firm that receives highly differentiated products in an online fashion and needs to price them in order to sell them to its customer base, and proposes a modification of the prior algorithm where uncertainty sets are replaced by their Lowner-John ellipsoids.
Proceedings ArticleDOI
Feature-based Dynamic Pricing
TL;DR: In this paper, the authors consider the problem of a firm that receives highly differentiated products in an online fashion and needs to price them in order to sell them to its customer base, where products are described by vectors of features and the market value of each product is linear in the values of features.
References
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Book ChapterDOI
Prospect theory: an analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
TL;DR: In this paper, the authors present a critique of expected utility theory as a descriptive model of decision making under risk, and develop an alternative model, called prospect theory, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights.
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Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Journal ArticleDOI
Prospect theory: analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
Book
Theory of Games and Economic Behavior
TL;DR: Theory of games and economic behavior as mentioned in this paper is the classic work upon which modern-day game theory is based, and it has been widely used to analyze a host of real-world phenomena from arms races to optimal policy choices of presidential candidates, from vaccination policy to major league baseball salary negotiations.
Journal ArticleDOI
Coherent Measures of Risk
TL;DR: In this paper, the authors present and justify a set of four desirable properties for measures of risk, and call the measures satisfying these properties "coherent", and demonstrate the universality of scenario-based methods for providing coherent measures.
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