Topic
Decision analysis cycle
About: Decision analysis cycle is a research topic. Over the lifetime, 334 publications have been published within this topic receiving 7608 citations.
Papers published on a yearly basis
Papers
TL;DR: Decision analysis has emerged from theory to practice to form a discipline for balancing the many factors that bear upon a decision as discussed by the authors, which can be visualized in a graphical problem space.
Abstract: Decision analysis has emerged from theory to practice to form a discipline for balancing the many factors that bear upon a decision. Unusual features of the discipline are the treatment of uncertainty through subjective probability and of attitude toward risk through utility theory. Capturing the structure of problem relationships occupies a central position; the process can be visualized in a graphical problem space. These features are combined with other preference measures to produce a useful conceptual model for analyzing decisions, the decision analysis cycle. In its three phases?deterministic, probabilistic, and informational?the cycle progressively determines the importance of variables in deterministic, probabilistic, and economic environments. The ability to assign an economic value to the complete or partial elimination of uncertainty through experimentation is a particularly important characteristic. Recent applications in business and government indicate that the increased logical scope afforded by decision analysis offers new opportunities for rationality to those who wish it.
416 citations
1 Jan 2010
TL;DR: This book discusses Dynamic MCDM, Habitual Domains and Competence Set Analysis for Effective Decision Making in Changeable Spaces, and the need for and possible methods of Objective Ranking.
Abstract: Dynamic MCDM, Habitual Domains and Competence Set Analysis for Effective Decision Making in Changeable Spaces.- The Need for and Possible Methods of Objective Ranking.- Preference Function Modelling: The Mathematical Foundations of Decision Theory.- Robustness in Multi-criteria Decision Aiding.- Preference Modelling, a Matter of Degree.- Fuzzy Sets and Fuzzy Logic-Based Methods in Multicriteria Decision Analysis.- Argumentation Theory and Decision Aiding.- Problem Structuring and Multiple Criteria Decision Analysis.- Robust Ordinal Regression.- Stochastic Multicriteria Acceptability Analysis (SMAA).- Multiple Criteria Approaches to Group Decision and Negotiation.- Recent Developments in Evolutionary Multi-Objective Optimization.- Multiple Criteria Decision Analysis and Geographic Information Systems.
373 citations
1 Nov 2001
TL;DR: The preference ratios in multiattribute evaluation (PRIME) method which supports the analysis of incomplete information in multi attribute weighting models is presented and a re-analysis of an earlier case study on international oil tanker negotiations is illustrated.
Abstract: This paper presents the preference ratios in multiattribute evaluation (PRIME) method which supports the analysis of incomplete information in multiattribute weighting models. In PRIME, preference elicitation and synthesis is based on 1) the conversion of possibly imprecise ratio judgments into an imprecisely specified preference model, 2) the use of dominance structures and decision rules in deriving decision recommendations, and 3) the sequencing of the elicitation process into a series of elicitation tasks. This process may be continued until the most preferred alternative is identified or, alternatively, stopped with a decision recommendation if the decision maker is prepared to accept the possibility that the value of some other alternative is higher. An extensive simulation study on the computational properties of PRIME is presented. The method is illustrated with a re-analysis of an earlier case study on international oil tanker negotiations.
246 citations
1 Dec 2011
TL;DR: A flexible framework for dynamic MCDM, based on the classic model, is introduced that can be applied to any dynamic decision process and illustrated by means of a small helicopter landing example to highlight its versatility.
Abstract: The classic multiple-criteria decision making (MCDM) model assumes that, when taking a decision, the decision maker has defined a fixed set of criteria and is presented with a clear picture of all available alternatives. The task then reduces to computing the score of each alternative, thus producing a ranking, and choosing the one that maximizes this value. However, most real-world decisions take place in a dynamic environment, where the final decision is only taken at the end of some exploratory process. Exploration of the problem is often beneficial, in that it may unveil previously unconsidered alternatives or criteria, as well as render some of them unnecessary. In this paper we introduce a flexible framework for dynamic MCDM, based on the classic model, that can be applied to any dynamic decision process and which is illustrated by means of a small helicopter landing example. In addition, we outline a number of possible applications in very diverse fields, to highlight its versatility.
183 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2018 | 1 |
| 2017 | 7 |
| 2016 | 11 |
| 2015 | 9 |
| 2014 | 12 |
| 2013 | 18 |