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Preference elicitation

About: Preference elicitation is a research topic. Over the lifetime, 1052 publications have been published within this topic receiving 39135 citations.


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Book
01 Jan 1976
TL;DR: In this article, a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe his true feelings in order to make those critically important, vexing trade-offs between incommensurable objectives.
Abstract: Many of the complex problems faced by decision makers involve multiple conflicting objectives. This book describes how a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe his true feelings in order to make those critically important, vexing trade-offs between incommensurable objectives. The theory is illustrated by many real concrete examples taken from a host of disciplinary settings. The standard approach in decision theory or decision analysis specifies a simplified single objective like monetary return to maximise. By generalising from the single objective case to the multiple objective case, this book considerably widens the range of applicability of decision analysis.

8,895 citations

Journal ArticleDOI
TL;DR: If appropriately designed, implemented, analysed and interpreted, DCEs offer several advantages in the health sector, the most important of which is that they provide rich data sources for economic evaluation and decision making, allowing investigation of many types of questions, some of which otherwise would be intractable analytically.
Abstract: Discrete choice experiments (DCEs) are regularly used in health economics to elicit preferences for healthcare products and programmes. There is growing recognition that DCEs can provide more than information on preferences and, in particular, they have the potential to contribute more directly to outcome measurement for use in economic evaluation. Almost uniquely, DCEs could potentially contribute to outcome measurement for use in both cost-benefit and cost-utility analysis. Within this expanding remit, our intention is to provide a resource for current practitioners as well as those considering undertaking a DCE, using DCE results in a policy/commercial context, or reviewing a DCE. We present the fundamental principles and theory underlying DCEs. To aid in undertaking and assessing the quality of DCEs, we discuss the process of carrying out a choice study and have developed a checklist covering conceptualizing the choice process, selecting attributes and levels, experimental design, questionnaire design, pilot testing, sampling and sample size, data collection, coding of data, econometric analysis, validity, interpretation and welfare and policy analysis. In this fast-moving area, a number of issues remain on the research frontier. We therefore outline potentially fruitful areas for future research associated both with DCEs in general, and with health applications specifically, paying attention to how the results of DCEs can be used in economic evaluation. We also discuss emerging research trends. We conclude that if appropriately designed, implemented, analysed and interpreted, DCEs offer several advantages in the health sector, the most important of which is that they provide rich data sources for economic evaluation and decision making, allowing investigation of many types of questions, some of which otherwise would be intractable analytically. Thus, they offer viable alternatives and complements to existing methods of valuation and preference elicitation.

1,170 citations

Journal ArticleDOI
TL;DR: In this paper, the behavior of decision makers to the dynamics of an industry or the macroeconomy is studied. But, despite the success of experimental techniques in the domain of the individual and small group, there has been comparatively little work relating the behaviour of decision-makers to the dynamic dynamics of larger organizations such as an industry.

671 citations

Proceedings Article
06 Jan 2007
TL;DR: This paper shows how to combine prior knowledge and evidence from the expert's actions to derive a probability distribution over the space of reward functions and presents efficient algorithms that find solutions for the reward learning and apprenticeship learning tasks that generalize well over these distributions.
Abstract: Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an expert IRL is motivated by situations where knowledge of the rewards is a goal by itself (as in preference elicitation) and by the task of apprenticeship learning (learning policies from an expert) In this paper we show how to combine prior knowledge and evidence from the expert's actions to derive a probability distribution over the space of reward functions We present efficient algorithms that find solutions for the reward learning and apprenticeship learning tasks that generalize well over these distributions Experimental results show strong improvement for our methods over previous heuristic-based approaches

663 citations

Journal ArticleDOI
TL;DR: This paper proposes a framework that takes a user-centric approach to recommender system evaluation that links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively).
Abstract: Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively). Furthermore, it incorporates the influence of personal and situational characteristics on the user experience. This paper reviews how current literature maps to the framework and identifies several gaps in existing work. Consequently, the framework is validated with four field trials and two controlled experiments and analyzed using Structural Equation Modeling. The results of these studies show that subjective system aspects and experience variables are invaluable in explaining why and how the user experience of recommender systems comes about. In all studies we observe that perceptions of recommendation quality and/or variety are important mediators in predicting the effects of objective system aspects on the three components of user experience: process (e.g. perceived effort, difficulty), system (e.g. perceived system effectiveness) and outcome (e.g. choice satisfaction). Furthermore, we find that these subjective aspects have strong and sometimes interesting behavioral correlates (e.g. reduced browsing indicates higher system effectiveness). They also show several tradeoffs between system aspects and personal and situational characteristics (e.g. the amount of preference feedback users provide is a tradeoff between perceived system usefulness and privacy concerns). These results, as well as the validated framework itself, provide a platform for future research on the user-centric evaluation of recommender systems.

651 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202328
202250
202176
202063
201973
201861