On the convergence of multiattribute weighting methods
16 Mar 2001-European Journal of Operational Research (Elsevier Science B.V.)-Vol. 129, Iss: 3, pp 569-585
TL;DR: This is the first experiment where the subjects created the alternatives and attributes themselves and suggests that the resulting weights are different because the methods explicitly or implicitly lead the decision makers to choose their responses from a limited set of numbers.
About: This article is published in European Journal of Operational Research.The article was published on 2001-03-16 and is currently open access. It has received 327 citations till now. The article focuses on the topics: Weighting & Decision analysis.
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TL;DR: In this article, the authors reviewed the corresponding methods in different stages of multi-criteria decision-making for sustainable energy, i.e., criteria selection, criteria weighting, evaluation, and final aggregation.
Abstract: Multi-criteria decision analysis (MCDA) methods have become increasingly popular in decision-making for sustainable energy because of the multi-dimensionality of the sustainability goal and the complexity of socio-economic and biophysical systems. This article reviewed the corresponding methods in different stages of multi-criteria decision-making for sustainable energy, i.e., criteria selection, criteria weighting, evaluation, and final aggregation. The criteria of energy supply systems are summarized from technical, economic, environmental and social aspects. The weighting methods of criteria are classified into three categories: subjective weighting, objective weighting and combination weighting methods. Several methods based on weighted sum, priority setting, outranking, fuzzy set methodology and their combinations are employed for energy decision-making. It is observed that the investment cost locates the first place in all evaluation criteria and CO2 emission follows closely because of more focuses on environment protection, equal criteria weights are still the most popular weighting method, analytical hierarchy process is the most popular comprehensive MCDA method, and the aggregation methods are helpful to get the rational result in sustainable energy decision-making.
1,868 citations
TL;DR: The history of the areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) are extended and topics the authors believe to be important for the future of these fields are discussed.
Abstract: This paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields.
760 citations
TL;DR: This work first makes a survey of the existing main aggregation operators and then proposes some new aggregation operators such as the induced ordered weighted geometric averaging (IOWGA) operator, generalized inducedordered weighted averaging (GIOWA), hybrid weighted averaged (HWA), etc., and briefly classify all of these aggregation operators.
Abstract: In this work, we first make a survey of the existing main aggregation operators and then propose some new aggregation operators such as the induced ordered weighted geometric averaging (IOWGA) operator, generalized induced ordered weighted averaging (GIOWA) operator, hybrid weighted averaging (HWA) operator, etc., and study their desirable properties. Finally, we briefly classify all of these aggregation operators. © 2003 Wiley Periodicals, Inc.
752 citations
Book•
03 Aug 2007
TL;DR: In this paper, the authors introduce the concept of Measurement as a way to measure the value of information and the importance of information in human decision-making process, and propose a universal approach to measure.
Abstract: Preface. Acknowledgements. Section I. Measurement: The Solution Exists. Chapter 1. The Intangibles and the Challenge. Chapter 2. An Intuitive Measurement Habit: Eratosthenes, Enrico & Emily. How an Ancient Greek Measured the Size of the Earth. Estimating: Be like Fermi. Experiments: Not just for adults. Notes on What to Learn from Eratosthenes, Enrico and Emily. Chapter 3. The Illusion of Intangibles: Why Immeasurables Aren't. The Concept of Measurement. The Object of Measurement. The Methods of Measurement. Economic Objections to Measurement. The Broader Objection to the Usefulness of & "Statistics". Ethical Objections to Measurement. Toward A Universal Approach to Measurement. Section II. Before You Measure. Chapter 4. Clarifying the Measurement Problem. Getting the Language Right: What Uncertainty and Risk Really Mean. Examples of Clarification: Lessons for Business from, of all places, Government? Chapter 5. Calibrated Estimates: How Much Do You Know Now? Calibration Exercise. Further Improvements on Calibration. Conceptual Obstacles to Calibration. The Effects of Calibration. Chapter 6. Measuring Risk: Introduction to the Monte Carlo Simulation. An Example for Monte Carlo and Risk. Tools and other Resources for Monte Carlo Simulations. The Risk Paradox. Chapter 7. Measuring the Value of Information. The Chance of Being Wrong and The Cost of Being Wrong: Expected. Opportunity Loss. The Value of Information for Ranges. The Imperfect World: The Value of Partial Uncertainty Reduction. The Epiphany Equation: The Value of a Measurement Changes Everything. Summarizing Uncertainty, Risk and Information Value: The first measurements. Section III. Measurement Methods Chapter 8. The Transition: From What Measure to How to Measure. Tools of Observation: Introduction to the Instrument of Measurement. Decomposition. Secondary Research: Assuming You Weren't the First to Measure It. The Basic Methods of Observation: If One Doesn't Work, Try the Next. Measure Just Enough. Consider the Error. Choose and Design the Instrument Chapter 9. Sampling Reality: How Observing Some Things Tells Us about All Things. Building an Intuition for Random Sampling: The Jelly Bean Example. A Little About Little Samples: A Beer Brewers Approach. The Easiest Sample Statistics Ever. A Sample of Sampling Methods. Measure to the Threshold. Experiment. Seeing Relationships in the Data: An Introduction to Regression Modeling. Chapter 10. Bayes: Adding to What You Know Now. Simple Bayesian. Using Your Natural Bayesian Instinct. Heterogeneous Benchmarking: A "Brand Damage" Application. Getting a Bit More Technical: Bayesian Inversion for Ranges. Section IV. Beyond the Basics. Chapter 11. Preference & Attitudes - The Softer Side of Measurement. Observing Opinions, Values, and the Pursuit of Happiness: The Basics. A Willingness to Pay: Measuring Value via Trade Offs. Putting it all on the Line: Quantifying Risk Tolerance. Quantifying Subjective Tradeoffs: Dealing with Multiple Conflicting Preferences? Keeping the Big Picture in Mind: Profit Maximization vs. Subjective Tradeoffs. Chapter 12. The Ultimate Measurement Instrument - Human Judges. Homo Absurdus: The Weird Reasons Behind Our Decisions. Getting Organized: A Performance Evaluation Example. Surprisingly Simple Linear Models. How to Standardize Any Evaluation: Rasch Models. Removing Human Inconsistency: The Lens Model. Panacea or Placebo?: Questionable Methods of Measurement. Comparing the Methods. Chapter 13. New Measurement Instruments for Management. The 21st Century Tracker: Keeping Tabs with Technology. Measuring the World: The Internet as An Instrument. Prediction Markets: Wall Street Efficiency Applied to Measurements. Chapter 14. A Universal Measurement Method - Applied Information Economics. Bringing the Pieces Together. Case: The Value of The System That Monitors Your Drinking Water. Case: Forecasting Fuel for the Marine Corps. Ideas for Getting Started: A Few Final Examples. Summarizing the Philosophy. Appendix. Calibration Tests. Index.
580 citations
TL;DR: In this paper, a review of research contributions on forest management and planning using multi-criteria decision making (MCDM) based on an exhaustive literature survey is provided, focusing on the application aspects highlighting theoretical underpinnings and controversies.
403 citations
Cites background from "On the convergence of multiattribut..."
...7 See Shoemaker and Waid (1982), Beinat (1997), Hajkowicz et al. (2000b) and Pöyhönen and Hämäläinen (2001) for comparisons of weighting techniques. relative attractiveness of different kinds and levels of environmental impact were established along with the scale for each objective using numerical…...
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References
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01 Jan 1985
TL;DR: Analytic Hierarchy Process (AHP) as mentioned in this paper is a systematic procedure for representing the elements of any problem hierarchically, which organizes the basic rationality by breaking down a problem into its smaller constituent parts and then guides decision makers through a series of pairwise comparison judgments to express the relative strength or intensity of impact of the elements in the hierarchy.
Abstract: This chapter provides an overview of Analytic Hierarchy Process (AHP), which is a systematic procedure for representing the elements of any problem hierarchically. It organizes the basic rationality by breaking down a problem into its smaller constituent parts and then guides decision makers through a series of pair-wise comparison judgments to express the relative strength or intensity of impact of the elements in the hierarchy. These judgments are then translated to numbers. The AHP includes procedures and principles used to synthesize the many judgments to derive priorities among criteria and subsequently for alternative solutions. It is useful to note that the numbers thus obtained are ratio scale estimates and correspond to so-called hard numbers. Problem solving is a process of setting priorities in steps. One step decides on the most important elements of a problem, another on how best to repair, replace, test, and evaluate the elements, and another on how to implement the solution and measure performance.
16,547 citations
"On the convergence of multiattribut..." refers background or methods in this paper
...Originally (Saaty, 1980) the AHP was not developed in the realm of value theory....
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...The weighting of attributes in the AHP (Saaty, 1980, 1994; Salo and H am al ainen, 1997) is based on estimates of weight ratios similarly to SMART and SWING....
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...PII: S 0 3 7 7 - 2 2 1 7 ( 9 9 ) 0 0 4 6 7 - 1 The methods compared are four versions of the analytic hierachy process (AHP) (Saaty, 1980, 1994; Salo and H am al ainen, 1997), DIRECT point allocation, simple multiattribute rating technique (SMART) (Edwards, 1977; von Winterfeldt and Edwards, 1986),…...
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Book•
01 Jan 1976TL;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
Book•
01 Jan 1986
TL;DR: In this article, the authors present an integrative presentation of the principles of decision analysis in a behavioral context, including sensitivity analysis, value-utility distinction, multistage inference, attitudes toward risk, and attempt to make intuitive sense out of what have been treated in the literature as endemic biases and other errors of human judgement.
Abstract: Decision analysis is a technology designed to help individuals and organizations make wise inferences and decisions. It synthesises ideas from economics, statistics, psychology, operations research, and other disciplines. A great deal of behavioural research is relevant to decision analysis; behavioural scientists have both suggested easy and natural ways to describe and quantify problems and shown the kind of errors to which unaided intuitive judgements can lead. This long-awaited book offers the4first integrative presentation of the principles of decision analysis in a behavioural context. The authors break new ground on a variety of technical topics (sensitivity analysis, the value-utility distinction, multistage inference, attitudes toward risk), and attempt to make intuitive sense out of what have been treated in the literature as endemic biases and other errors of human judgement. Those interested in artificial intelligence will find it the easiest presentation of hierarchical Bayesian inference available.
2,616 citations
"On the convergence of multiattribut..." refers background or methods in this paper
...…hierachy process (AHP) (Saaty, 1980, 1994; Salo and H am al ainen, 1997), DIRECT point allocation, simple multiattribute rating technique (SMART) (Edwards, 1977; von Winterfeldt and Edwards, 1986), SWING weighting (von Winterfeldt and Edwards, 1986) and TRADEOFF weighting (Keeney and Rai a, 1976)....
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...SWING (von Winterfeldt and Edwards, 1986) explicitly incorporates the attribute ranges in the elicitation questions....
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...With SMART the weights are elicited in two steps (Edwards, 1977; von Winterfeldt and Edwards, 1986): 1....
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