GENERIC MULTIATTRIBUTE ANALYSIS (GMAA)
The Generic Multi-Attribute
Analysis (GMAA) System is a Decision Support System (DSS)
based on an additive multi-attribute utility model that accounts for incomplete
information concerning the inputs. The system is intended to allay many
of the operational difficulties involved in the Decision Analysis
(DA) cycle, which can be divided into four steps: structuring the problem
(which includes specifying objectives, building a value hierarchy and establishing
attributes for the lowest-level objectives); identifying the feasible alternatives,
their impact and uncertainty (if necessary); quantifying preferences (which
includes the assessment of the component attribute utilities as well as
the value trade-offs); evaluating strategies and performing Sensitivity
The user can interactively create or delete nodes and branches to build or modify the objectives hierarchy. A name, label and description must be entered for each objective, as well as the respective attribute units and ranges for the lowest level objective. The system also accounts for attributes with a subjective scale.
Alternatives and their consequences, in terms of the attributes associated with the lowest-level objectives, can be easily entered by hand or loaded from file. The system admits uncertainty about consequences, which leads to uniformly distributed ranges for each attribute instead of single values. Note that both endpoints being equal would be equivalent to the case under certainty, where the policy effects for an alternative in an attribute are precisely known. Alternatives with missing consequences, that is, alternatives that do not provide values or consequences for some attributes in the hierarchy, can be represented by the respective attribute range. The user can add or delete alternatives and modify their respective consequences.
The next step in DA consists of preference
quantification, which involves assessing the DM's component utilities for
the attributes and the relative importance of criteria. In both cases,
the system admits incomplete information through value intervals as responses
to the probability questions the DM is asked, which leads to classes of
utility functions and weight intervals, respectively. This is less demanding
for a single DM and also makes the system suitable for group decision support,
because conflicting individual views or judgments in a group of DMs
can easily be captured through imprecise responses.
With respect to component utilities assessment, the system provides three procedures for building an imprecise piecewise linear utility function (providing up to three intermediate points, which can be dragged by the mouse to achieve the right shape), providing imprecise utilities for discrete attribute values, or using a method based on the combination of two slightly modified standard procedures for utility assessment, the Fractile Method and the Extreme Gambles Method.
The system provides two weight elicitation procedures, a direct assignment, which is perhaps more suitable for upper level objectives, which could be more political, and a method based on trade-offs, which is more suitable for the low-level objectives in the hierarchy, because the weight assessment involves a more specific area of knowledge.
A complete free version of the GMAA system is available. Two complex decision-making problems are included in the Examples folder (within the installation folder), especifically, the restoration of a radionuclide contaminated aquatic ecosystem, lake Ovre Heimdalsvatn, and the selection of a technology for the disposition of surplus weapons-grade plutonium by the Department of Energy in the USA. They can be used to get a better understanding of the system.
Installation package (.zip) (3,95 MB)
User's Guide (.pdf) (1,267 MB)
** It is strongly recommended to set normal fonts, 1024*768 pixels for
area and 96ppp for a correct use of the system in Windows XP.
Ríos-Insua S., Gallego E., Jiménez A., Mateos A.: A Multi-Attribute Decision Support System for Selecting Environmental Intervention Strategies, Ecological Modelling 196, 1-2, 2006, pp. 195-208 (on line).
Jiménez A., Ríos-Insua S. and Mateos A.: A Generic Multi-Attribute Analysis System, Computers and Operations Research 33, 4, 2006, pp. 1081-1101. (on line).
Jiménez A., Ríos-Insua S. and Mateos A.: Monte-Carlo Simulation Techniques in a Multi-Attribute Decision Support System, Proceedings of the 12th IASTED International Conference on Applied Simulation and Modelling, M.H. Hamza (ed.), ACTA Press, ISBN 0-88986-384-9, 2003, pp. 85-90. (on line)
Jiménez A., Ríos-Insua S. and Mateos A.: A Decision Support System for Multiattribute Utility Evaluation based on Imprecise Assignments, Decision Support Systems 36, 1, 2003, pp. 65-79. (on line)
Mateos A., Jiménez A. and Ríos-Insua S.: Solving Dominance and Potential Optimality in Imprecise Multi-Attribute Additive Problems, Reliability Engineering and System Safety 79, 2, 2003, pp. 253-262. (on line)
Mateos A., Jiménez A. and Ríos-Insua S.: Modelling Individual and Global Comparisons for Multi-Attribute Preferences, Journal of Multicriteria Decision Analysis 13, 2003, pp. 1-14. (on line)
Ríos-Insua S., Jiménez A. and Mateos A.: Sensitivity Analysis in a Generic Multi-Attribute Decision Support System, in: Advances in Decision Technology and Intelligent Information Systems, Vol. IV, K.J. Engemann y G.E. Lasker (eds.), The International Institute for Advanced Studies in Systems Research and Cybernetics, ISBN 1-894613-24-4, Canada, 2003, pp. 31-35.
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SIXTO RÍOS INSUA
Full Professor of Statistics and Operations Research
Dr. Ríos-Insua is professor of Statistics and Operations Research at Madrid Technical University and member of the Spanish Academy of Doctors. His research interests are decision analysis and statistical decision theory, and he is mainly concerned with the development of knowledge-based systems supported by influence diagrams and multiattribute utility theory with applications to medicine, e-business and the environment.
ALFONSO MATEOS CABALLERO
Associate Professor of Statistics and Operations Research
Dr. Mateos is associate professor of Statistics and Operations Research at the School of Computer Science (Madrid Technical University). His research interest is decision analysis and he is currently involved in the development of intelligent decision support systems based on influence diagrams and multiattribute utility theory with applications to the environment and medicine.
ANTONIO JIMÉNEZ MARTÍN
Associate Professor of Statistics and Operations Research
Dr. Jiménez gained degree in Computer Science from Technical University of Madrid. He is Associate Professor of Operations Research and Simulation Methods. His research interest is decision analysis and is involved in the development and implementation of decision support systems based on multiattribute utility theory. His articles have appeared in various academic journals including: Computers & Operations Research, DSS, EJOR, JMDA, Group Decision and Negotiation, Reliability Engineering and System Safety,...
Grupo de Análisis de Decisiones y Estadística Facultad de Informática Departamento de Inteligencia Artificial Campus de Montegancedo S/N, Boadilla del Monte 28660 Madrid (SPAIN)
Generic MultiAttribute Analysis (GMAA)
Copyright © 2003. Universidad Politécnica de Madrid
Last modified: Sat November 4 2005 by A. Jiménez.