Ranking Alternatives on the Basis of a Dominance Intensity Measure

The additive multi-attribute utility model is widely used within MultiAttribute Utility Theory (MAUT), demanding all the information describing the decision-making situation. However, these information requirements can obviously be far too strict in many practical situations. Consequently, incomplete information about input parameters has been incorporated into the decisionmaking process. We propose an approach based on a dominance intensity measure to deal with such situations. The approach is based on the dominance values between pairs of alternatives that can be computed by linear programming. These dominance values are transformed into dominance intensities from which a dominance intensity measure is derived. It is used to analyze the robustness of a ranking of technologies for the disposition of surplus weapons-grade plutonium by the Department of Energy in the USA, and compared with other dominance measuring methods

Additive utility in prospect theory

Prospect theory is currently the main descriptive theory of decision under uncertainty. It generalizes expected utility by introducing nonlinear decision weighting and loss aversion. A difficulty in the study of multiattribute utility under prospect theory is to determine when an attribute yields a gain or a loss. One possibility, adopted in the theoretical literature on multiattribute utility under prospect theory, is to assume that a decision maker determines whether the complete outcome is a gain or a loss. In this holistic evaluation, decision weighting and loss aversion are general and attribute-independent. Another possibility, more common in the empirical literature, is to assume that a decision maker has a reference point for each attribute. We give preference foundations for this attribute-specific evaluation where decision weighting and loss aversion are depending on the attributes

Nuclear Waste Management Decision-Making Support with MCDA

The paper proposes a multicriteria decision analysis (MCDA) framework for a comparative evaluation of nuclear waste management strategies taking into account different local perspectives (expert and stakeholder opinions). Of note, a novel approach is taken using a multiple-criteria formulation that is methodologically adapted to tackle various conflicting criteria and a large number of expert/stakeholder groups involved in the decision-making process. The purpose is to develop a framework and to show its application to qualitative comparison and ranking of options in a hypothetical case of three waste management alternatives: interim storage at and/or away from the reactor site for the next 100 years, interim decay storage followed in midterm by disposal in a national repository, and disposal in a multinational repository. Additionally, major aspects of a decision-making aid are identified and discussed in separate paper sections dedicated to application context, decision supporting process, in particular problem structuring, objective hierarchy, performance evaluation modeling, sensitivity/robustness analyses, and interpretation of results (practical impact). The aim of the paper is to demonstrate the application of the MCDA framework developed to a generic hypothetical case and indicate how MCDA could support a decision on nuclear waste management policies in a “small” newcomer country embarking on nuclear technology in the future

Sensor Acquisition for Water Utilities: Survey, Down Selection Process, and Technology List

The early detection of the biological and chemical contamination of water distribution systems is a necessary capability for securing the nation's water supply. Current and emerging early-detection technology capabilities and shortcomings need to be identified and assessed to provide government agencies and water utilities with an improved methodology for assessing the value of installing these technologies. The Department of Homeland Security (DHS) has tasked a multi-laboratory team to evaluate current and future needs to protect the nation's water distribution infrastructure by supporting an objective evaluation of current and new technologies. The LLNL deliverable from this Operational Technology Demonstration (OTD) was to assist the development of a technology acquisition process for a water distribution early warning system. The technology survey includes a review of previous sensor surveys and current test programs and a compiled database of relevant technologies. In the survey paper we discuss previous efforts by governmental agencies, research organizations, and private companies. We provide a survey of previous sensor studies with regard to the use of Early Warning Systems (EWS) that includes earlier surveys, testing programs, and response studies. The list of sensor technologies was ultimately developed to assist in the recommendation of candidate technologies for laboratory and field testing. A set of recommendations for future sensor selection efforts has been appended to this document, as has a down selection example for a hypothetical water utility

Multicriteria portfolio decision analysis for project selection

Multicriteria Portfolio Analysis spans several methods which typically employ build on MCDA to guide the selection of a subset (i.e.,portfolio) of available objects, with the aim of maximising the performance of the resulting portfolio with regard to multiple criteria, subject to the requirement that the selected portfolio does not consume of resources consumed by the portfolio does not exceed the availability of resources and, moreover, satisfies other relevant constraints as well. In this chapter, we present a formal model of this selection problem and describe how this model can present both challenges (e.g. portfolio value may, due to the interactions of elements, depend on project-level decisions in complex and non-additive ways) and opportunities (e.g.triage rules can be used to focus elicitation on projects which are critical) for value assessment. We also survey the application of Portfolio Decision Analysis in several domains, such as allocation of R&D expenditure, military procurement, prioritisation of healthcare projects, and environment and energy planning, and conclude by outlining possible future research directions

Multiobjective simulation-based methodologies for medical decision making.

A variety of methodologies have been employed for decision making related to the treatment of diseases/injury. Decision trees are a functional way in which to examine problems under uncertainty by providing a method to analyze decisions under risk (Detsky, 1996, 97). However, conventional decision trees do not completely represent the real world since they cannot investigate problems that are cyclic in nature (Jaafari, 2003). The stochastic tree that developed Hazen during 1992-to-1996 is one of the most relevant methods and techniques related to decision analyses that append more incorporation for medical intervention related to recurring diseases/injuries. The approach combines features of continuous-time Markov chains with those of decision trees and that enable time to be modeled as a range where health state transitions can occur at any instant (Hazen 1992-to-96). It can also accommodate patients\u27 preferences regarding risk and quality of life. In this research we enhance Hazen\u27s stochastic tree by developing an analytical model, and we extend its capabilities more by developing multi-objective simulation based methodologists for medical decision making. First, with our enhancement on the Hazen\u27s stochastic tree, the model is improved by utilizing the Weibull Accelerated Failure Time model. This new technique will fill the gap between the experimental circumstances and the corresponding circumstances or conditions of standard/current treatment. Second, as simulation can be a final alternative for problems that are mathematically intractable for other techniques (Banks 1996), our multi-objective simulation based model for medical decision making extends the capabilities of Hazen stochastic tree. It adds more flexibility with the use of survival distributions for health states sojourn, and combines two sound theories: multi attribute utility (MAU) theory, and Ranking-Selection procedures. Indeed, our simulation model (considering patient\u27s profile/preferences and health states survival/quality/cost, QALY) presents an investigation of the use of simulation on the stochastic tree, with associated techniques related to ranking and selection, and multi-objectives decision analysis

Analysis of Organizational Architectures for the Air Force Tuition Assistance Program

The consideration of restructuring through a change in organizational architecture is often a fiercely debated issue within an organization. The argument for restructuring to improve quality, customer service, and financial management is pitted against perceived lack of job security and historically poor results from previous restructuring initiatives. To balance all sides when considering a change in organizational architecture, the organization should use a method of evaluating potential architectures that assists in determining the best new architecture and generates support from those involved. The objective of this research is to provide the Air Force Education Division with a defendable methodology for evaluating and selecting an organizational architecture. This thesis effort utilizes Value-Focused Thinking to develop a model that identifies the values associated with the management and execution of the Tuition Assistance (TA) program