Simplified models for multi-criteria decision analysis under uncertainty

Includes abstract.Includes bibliographical references.When facilitating decisions in which some performance evaluations are uncertain, a decision must be taken about how this uncertainty is to be modelled. This involves, in part, choosing an uncertainty format {a way of representing the possible outcomes that may occur. It seems reasonable to suggest {and is an aim of the thesis to show {that the choice of how uncertain quantities are represented will exert some influence over the decision-making process and the final decision taken. Many models exist for multi-criteria decision analysis (MCDA) under conditions of uncertainty; perhaps the most well-known are those based on multi-attribute utility theory [MAUT, e.g. 147], which uses probability distributions to represent uncertainty. The great strength of MAUT is its axiomatic foundation, but even in its simplest form its practical implementation is formidable, and although there are several practical applications of MAUT reported in the literature [e.g. 39, 270] the number is small relative to its theoretical standing. Practical applications often use simpler decision models to aid decision making under uncertainty, based on uncertainty formats that `simplify' the full probability distributions (e.g. using expected values, variances, quantiles, etc). The aim of this thesis is to identify decision models associated with these `simplified' uncertainty formats and to evaluate the potential usefulness of these models as decision aids for problems involving uncertainty. It is hoped that doing so provides some guidance to practitioners about the types of models that may be used for uncertain decision making. The performance of simplified models is evaluated using three distinct methodological approaches {computer simulation, `laboratory' choice experiments, and real-world applications of decision analysis {in the hope of providing an integrated assessment. Chapter 3 generates a number of hypothetical decision problems by simulation, and within each problem simulates the hypothetical application of MAUT and various simplified decision models. The findings allow one to assess how the simplification of MAUT models might impact results, but do not provide any general conclusions because they are based on hypothetical decision problems and cannot evaluate practical issues like ease-of-use or the ability to generate insight that are critical to good decision aid. Chapter 4 addresses some of these limitations by reporting an experimental study consisting of choice tasks presented to numerate but unfacilitated participants. Tasks involved subjects selecting one from a set of five alternatives with uncertain attribute evaluations, with the format used to represent uncertainty and the number of objectives for the choice varied as part of the experimental design. The study is limited by the focus on descriptive rather than real prescriptive decision making, but has implications for prescriptive decision making practice in that natural tendencies are identified which may need to be overcome in the course of a prescriptive analysis

A comprehensive approach to electricity investment planning for multiple objectives and uncertainty

Includes abstract.Includes bibliographical references.Appropriate Energy-Environment-Economic (E3) modelling provides key information for policy makers in the Electricity Supply Industry (ESI) faced with navigating a sustainable development path. Key challenges include engaging with stakeholder values and preferences, and exploring trade-offs between competing objectives in the face of underlying uncertainty. As such, a comprehensive framework is needed that integrates multiple objectives and uncertainty into a transparent methodology that policy makers and planners can use to analyse and plan for investment in the ESI, in a way which shapes decision outcomes, and enables confident choices to be made. This thesis is aimed at developing such a framework. As a case study the South African ESI was represented using a partial equilibrium (Energy-Economic-Environment) E3 modelling approach. This approach was extended to include multiple objectives under selected future uncertainties. This extension was achieved by assigning cost penalties (PGPs – Pareto Generation Parameters) to non-cost attributes to force the model’s least-cost objective function to better satisfy non-cost criteria. It was shown that using PGPs is an efficient method for extending the analysis to multiple objectives as the solutions generated are non-dominated and are generated from ranges of performances in the various criteria rather than from arbitrarily forcing the selection of particular technologies. Extensive sections of the non-dominated solution space can be generated and later screened to allow further, more detailed exploration of areas of the solution space. Aspects of flexibility to demand growth uncertainty were incorporated into each future expansion alternative (FEA) by introducing stochastic programming with recourse into the model. Technology lead times were taken into account by the inclusion of a decision node along the time horizon where aspects of real options theory were considered within the planning process by splitting power station investments into their Owner’s Development Cost (ODC) and Equipment and Procurement Cost (EPC) components. Hedging in the recourse programming was automatically translated from being purely financial, to include the other attributes that the cost penalties represented. The hedged solutions improved on the naïve solutions under the multiple criteria considered as well as better satisfying the non-cost objectives relative to the base case (least cost solution). From a retrospective analysis of the cost penalties, the correct market signals could be derived to meet policy goal, with due regard to demand uncertainty

Mariner Mars 1971 optical navigation demonstration

The feasibility of using a combination of spacecraft-based optical data and earth-based Doppler data to perform near-real-time approach navigation was demonstrated by the Mariner Mars 71 Project. The important findings, conclusions, and recommendations are documented. A summary along with publications and papers giving additional details on the objectives of the demonstration are provided. Instrument calibration and performance as well as navigation and science results are reported

Resúmenes de la XIV Reunión del Grupo Español de Decisión Multicriterio

Esta reunión es un foro en el que académicos e investigadores pueden intercambiar ideas y experiencias en el campo del multicriteri

Stochastic Multiattribute Acceptability Analysis: an application to the ranking of Italian regions

We consider the issue of ranking regions with respect to a range of economic and social variables. Departing from the current practice of aggregating different dimensions via an arithmetic mean, we instead use Stochastic Multiattribute Acceptability Analysis (SMAA). SMAA takes account of the “whole space” of weights for the considered dimensions. Thus, rather than considering an average person giving equal or fixed weights to all dimensions, SMAA explores how potential differences in individual preferences affect the outcome. In this sense, in contrast to the purported objectivity of the many rankings supplied by economic institutions and mass media, this proposal enhances, simplifies and renders transparent the ranking exercise. The methodology is applied to the ranking of Italian regions, unveiling patterns of similarity and dissimilarity even within the same broad regional economy. Many of these findings are neglected within the extant literature addressing the “Mezzogiorno” problem

Advancing sustainable nanotechnology with multiple criteria decision aiding

Nanotechnology is currently emerging as the next industrial revolution. It enables the production of goods (i.e. nanoproducts, NPs) with enhanced functionalities, which have nonetheless caused mounting concerns about the potential implications they can have on the environment, economy and society. This thesis employs Multiple Criteria Decision Aiding (MCDA), one form of decision support, to aid the sustainable development of nanotechnology. The first original contribution of this doctoral research is the development of a framework of sustainability assessment criteria for NPs, through a three-phase procedure based on the MCDA process, including a literature review, a pilot and a main survey. It lead to a comprehensive framework of 68 criteria, ranked according to their relative importance, allocated to six main domain areas: (i) economic performance; (ii) environmental impacts; (iii) environmental risk assessment; (iv) human health risk assessment; (v) social implications; and (vi) technical performance. All the criteria are reliable and can be used in real case studies to increase the knowledge about the sustainability of NPs. The second original contribution presented in this thesis is a robust model (DRSA-based model) based on green chemistry principles implementation for the classification of synthesis processes of nanomaterials in preference-ordered classes. This tool was developed through knowledge elicitation techniques based on coconstructive MCDA with the collaboration of two experts (the decision makers) in synthesis of nanomaterials. The robustness of the ensuing model was assessed (and confirmed) by means of another model developed ad hoc (ELECTRE-based model), structured on an MCDA method implementing a stochastic multiple criteria classification strategy. The results confirm that MCDA is an effective decision support approach to foster sustainable development of nanotechnology, providing that the analysts who apply it take these considerations into account. They must ensure that (1) multidisciplinary teams are created to perform comprehensive and credible sustainability evaluations; (2) problem structuring and model construction are as important as (if not more important) than the results (i.e. decision recommendations) themselves; (3) identification of the appropriate MCDA method depends on the problem at hand and not vice-versa; and (4) the credibility of the decision recommendations is subject to the preferences of the decision-makers. If these considerations are accounted for, the possibility of advancing nanotechnology on a sustainable path is very concrete and realistic

Stochastic multiattribute acceptability analysis:an application to the ranking of Italian regions

<p>We consider the issue of ranking regions with respect to a range of economic and social variables. Departing from the current practice of aggregating different dimensions via a composite index, usually based on an arithmetic mean, we instead use stochastic multi-attribute acceptability analysis (SMAA). SMAA considers the ‘whole space’ of weights for the considered dimensions. The methodology is applied to the ranking of Italian regions, showing that although the north–south divide is definitely wider than the one measured simply in terms of gross domestic product. There are southern regions that perform generally better than those belonging to their broad region: a kind of ‘northern regions within the southern broad region’. This result poses interesting questions about the uneven development of Italian regions.</p

Towards a Comprehensive Evidence-Based Approach For Information Security Value Assessment

This thesis is motivated by the goals of understanding in depth which information security value aspects are relevant in real-world business environments and contributing a value-prioritised information security investment decision model suitable for practitioners in the field. Pursuing this goal, we apply a mixed method research approach that combines the analysis of the relevant literature, expert interviews, practitioner survey data and structural equation modelling and multicriteria decision analysis. In the first step, we address the identified terminology gap to clarify the meaning of ‘cyber security’ by analysing authoritative definition sources in the literature and presenting an improved definition distinct from that of ‘information security’. We then investigate the influence of repeated information security breaches on an organisation’s stock market value to benchmark the wider economic impact of such events. We find abnormal returns following a breach event as well as weak statistical significance on abnormal returns for later breach events, confirming that data breaches have a negative impact on organisations. To understand how security practitioners view this topic, we conduct and analyse semi-structured interviews following a grounded theory approach. Our research identifies 15 principles aligned with a conceptual information security investment framework. The key components of this framework such as the business environment, drivers (threat landscape, legal and regulatory) and challenges (cost of security, uncertainty) are found to be a crucial part of value-prioritised information security investment decisions. We verify these findings through a structural model consisting of five latent variables representing key areas in value-focused information security investment decisions. The model shows that security capabilities have the largest direct effect on the value organisations gain from information security investment. In addition, the value outcome is strongly influenced by organisation-specific constructs such as the threat landscape and regulatory requirements, which must therefore be considered when creating security capabilities. By addressing one of the key uncertainty issues, we use a probabilistic topic modelling approach to identify latent security threat prediction topics from a large pool of security predictions publicised in the media. We further verify the prediction outcomes through a survey instrument. The results confirm the feasibility of forecasting notable threat developments in this context, implying that practitioners can use this approach to reduce uncertainty and improve security investment decisions. In the last part of the thesis, we present a multicriteria decision model that combines our results on value-prioritised information security investments in an organisational context. Based on predefined criteria and preferences and by utilising stochastic multicriteria acceptability analysis as the adopted methodology, our model can deal with substantial uncertainty while offering ease of use for practitioners

Stakeholder-driven multi-attribute analysis for energy project selection under uncertainty

In practice, selecting an energy project for development requires balancing criteria and competing stakeholder priorities to identify the best alternative. Energy source selection can be modeled as multi-criteria decision-maker problems to provide quantitative support to reconcile technical, economic, environmental, social, and political factors with respect to the stakeholders' interests. Decision making among these complex interactions should also account for the uncertainty present in the input data. In response, this work develops a stochastic decision analysis framework to evaluate alternatives by involving stakeholders to identify both quantitative and qualitative selection criteria and performance metrics which carry uncertainties. The developed framework is illustrated using a case study from Fairbanks, Alaska, where decision makers and residents must decide on a new source of energy for heating and electricity. We approach this problem in a five step methodology: (1) engaging experts (role players) to develop criteria of project performance; (2) collecting a range of quantitative and qualitative input information to determine the performance of each proposed solution according to the selected criteria; (3) performing a Monte-Carlo analysis to capture uncertainties given in the inputs; (4) applying multi-criteria decision-making, social choice (voting), and fallback bargaining methods to account for three different levels of cooperation among the stakeholders; and (5) computing an aggregate performance index (API) score for each alternative based on its performance across criteria and cooperation levels. API scores communicate relative performance between alternatives. In this way, our methodology maps uncertainty from the input data to reflect risk in the decision and incorporates varying degrees of cooperation into the analysis to identify an optimal and practical alternative