Sixto Ríos-Insua

A decision support system for multiattribute utility evaluation based on imprecise assignments

This paper describes a decision support system based on an additive or multiplicative multiattribute utility model for identifying the optimal strategy. This is intended to allay many of the operational difficulties involved in assessing and using multiattribute utility functions. The system admits imprecise assignments for weights and utilities and uncertainty in the multiattribute strategies, which can be defined in terms of ranges for each attribute instead of single values. Inputs can be subjected to different sensitivity analyses, permitting users to test the robustness of the ranking of the strategies and gain insight into and confidence about the final solution. An application of the system to the restoration of a contaminated lake is illustrated throughout the paper.

Monte Carlo simulation techniques for group decision making with incomplete information

Abstract In this paper we deal with group decision-making problems where several decision makers elicit their own preferences separately. The decision makers’ preferences are quantified using a decision support system, which admits incomplete information concerning the decision makers’ responses to the questions they are asked. Consequently, each decision maker proposes classes of utility functions and attribute weight intervals for the different attributes. We introduce an approach based on Monte Carlo simulation techniques for aggregating decision maker preferences that could be the starting point for a negotiation process, if necessary. The negotiation process would basically involve the decision maker tightening the imprecise component utilities and weights to output more meaningful results and achieve a consensus alternative. We focus on how attribute weights and the component utilities associated with a consequence are randomly generated in the aggregation process taking into account the decision-makers’ preferences, i.e., their respective attribute weight intervals and classes of utility functions. Finally, an application to the evaluation of intervention strategies for restoring a radionuclide contaminated lake illustrates the usefulness and flexibility of this iterative process.

MOIRA: A decision support system for decision making on aquatic ecosystems contaminated by radioactive fallout

Interventions to restore radionuclide contaminated aquatic ecosystems may reduce individual and collective radiation doses, but may also result in detrimental ecological, social and economic effects. Decision makers must carefully evaluate possible impacts before choosing a countermeasure, hence decision analysis methods constitute an important aid to rank intervention strategies after the contamination of an aquatic ecosystem. We describe MOIRA, a decision support system for the identification of optimal remedial strategies to restore water systems after accidental introduction of radioactive substances. MOIRA includes an evaluation module based on a multi-attribute value model to rank alternatives and a module to perform multiparametric sensitivity analyses, both with respect to weights and values, to allow us to gain insights into the problem. The problem is under certainty since the validation of models used to quantify countermeasure impacts suggests little uncertainty in policy effects.The system is implemented in a PC based decision support system which allows the inclusion of all relevant information.

Dominance, potential optimality and alternative ranking in imprecise multi-attribute decision making

In this paper, we introduce a methodology based on an additive multiattribute utility function that does not call for precise estimations of the inputs, such as utilities, attribute weights and performances of decision alternatives. The information about such inputs is assumed to be in the form of ranges, which constitute model constraints and give rise to nonlinear programming problems. This has significant drawbacks for outputting the sets of non-dominated and potentially optimal alternatives for such problems, and we, therefore, propose their transformation into equivalent linear programming problems. The set of non-dominated and potentially optimal alternatives is a non-ranked set and can be very large, which makes the choice of the most preferred alternative very difficult. The above problem is solved by proposing several methods for alternative ranking. An application to the disposal of surplus weapons-grade plutonium is considered, showing the advantages of this approach.

The application of the lake ecosystem index in multi-attribute decision analysis in radioecology

This work gives a summary of multi-attribute analysis (MAA) and its use in decision support systems for radiological and environmental contamination problems and presents a modification of the lake ecosystem index (LEI) as a tool to give an holistic account for the environmental (and not just radiological) consequences of chemical remedial measures (lake and wet land liming, potash treatment and lake fertilisation) carried out to reduce radionuclide levels in water, sediments and biota. The first step in determining a LEI-value is to set normal or initial values of two important limnological state variables, pH and total-P. The second step involves predicting state indices describing the abundance of key functional groups (the fish yield and biomasses of phytoplankton and bottom fauna). The next step concerns the definition of a lake ecosystem index based on the state indices. The final step is the derivation of the utility function to be used in the multi-attribute analysis to compare environmental, economical and social attributes of different dimensions (ECU, kg, Bq/kg, etc.). The ecosystem index characterises the entire lake over longer periods of time (months), and not specific sites in lakes or specific sampling events.

Structural elicitation and computational issues faced when solving complex decision making problems with influence diagrams

Abstract Influence diagrams have become a popular tool for representing and solving decision making problems under uncertainty (Shachter, Operations Research 1986;34:871–82). We show here some practical difficulties when using them to construct a medical decision support system. Specifically, it is hard to tackle issues related to the problem structuring, like the existence of constraints on the sequence of decisions, and the time evolution modeling; related to the knowledge-acquisition, like probability and utility assignment; and related to computational limitations, in memory storage and evaluation phases, as well as the explanation of results. We have recently developed a complex decision support system for neonatal jaundice management — a very common medical problem — , encountering all these difficulties. In this paper, we describe them and how they have been undertaken, providing insights into the community involved in the design and solution of decision models by means of influence diagrams. Scope and purpose Decision Analysis is a very well-known discipline that deals with the practice of Decision Theory (Clemen, Making hard decisions: an introduction to decision analysis, 2nd ed. Pacific Grove, CA: Duxbury, 1996). It comprises various steps usually implemented in a decision support system: definition of the alternatives and objectives, modelization of the structure of the decision problem, as well as the beliefs and preferences of the decision maker. The recommended alternative is the one with maximum expected utility, once all the assignments have been refined via sensitivity analyses. However, there are a number of difficulties faced in practice when solving large problems, that require an attentive study.

Experiments in Robust Decision Making

Many demonstration experiments have shown that expected utility theory is not sufficiently adequate from a descriptive point of view. We report on a project emphasizing on experiments dealing with the issue of imprecision in preferences. We try to quantify how several factors may influence inconsistencies in decision making experiments. Then, we study whether these inconsistencies may be due to imprecision in preferences.

A Graphical Decision-Theoretic Model for Neonatal Jaundice:

Background. Neonatal jaundice is treated daily at all hospitals. However, the routine, urgency, and case load of most doctors stop them from carefully analyzing all the factors that they would like to (and should) take into account. This article develops a complex decision support system for neonatal jaundice management. Methods. The problem is represented by means of an influence diagram, including admission and treatment decisions. The corresponding uncertainty model is built with the aid of both historical data and subjective judgments. Parents and doctors were interviewed to elicit a multiattribute utility function. The decision analysis cycle is completed with sensitivity analyses and explanations of the results. Results. The construction and use of this decision support system for jaundice management have induced a profound change in daily medical practice, avoiding aggressive treatments—there have been no exchange transfusions in the past 3 years—and reducing the lengths of stay at the hospital. More information is now taken into account to decide on treatments. Interestingly, after embarking on this modeling effort, physicians came to view jaundice as a much more difficult problem than they had initially thought. Comparisons between real cases and system proposals revealed that treatments by nonexpert doctors tend to be longer than what expert doctors would administer. Conclusion. The system is especially designed to help neonatologists in situations in which their lack of experience may lead to unnecessary treatments. Different points of view from several expert doctors and, more interestingly, from parents are taken into account. This knowledge gives a broader picture of the medical problem— incorporating new action criteria, new agents to intervene, more uncertainty variables—to get an insight into the suitability of each therapeutic decision for each patient situation. The benefits gained and the usefulness perceived by neonatologists are worth the increased and time-consuming effort of developing this complex system. Although specially designed for a specific hospital and for neonatal jaundice management, it can be easily adapted to other hospitals and problems.

Solving dominance and potential optimality in imprecise multi-attribute additive problems

Abstract We consider the multicriteria decision-making problem where there is partial information on decision maker preferences, represented by means of an imprecise multiattribute additive utility function, and where the consequences of the alternatives or strategies are also possibly imprecise. Under these circumstances we consider how useful problem-solving concepts, namely nondominated, potentially optimal, adjacent potentially optimal alternatives, can be analytically computed. Thus, the problem can be solved much more efficiently using the classical methodology of linear programming.

Postoptimal analysis in a multi-attribute decision model for restoring contaminated aquatic ecosystems

We describe the evaluation module of the MOIRA system, developed to identify optimal remedial strategies for restoring radionuclide contaminated aquatic ecosystems and drainage areas. This module includes a multiparametric sensitivity analysis, which is based on a multi-attribute additive value model, aimed at identifying optimal remedial strategies for restoring aquatic ecosystems contaminated by radionuclides. We introduce the sensitivity analysis to check the robustness of the conclusions on the inputs. This provides insights into the problem in the sense of making better use of the available information. This analysis is focused on judgemental inputs, imprecise value functions on attributes and imprecise scaling factors or weights for their aggregation. These are of utmost importance in determining the optimal countermeasures.