A Methodology for Environmental Systems Management: Dynamic Application of the Nested Lagrangian Multiplier Method

In this paper an alternative method for solving multiobjective optimization problems is presented. We are especially concerned with bridging a gap between procedures for obtaining the Pareto-optimal solutions and the "best compromised" preferred solution for the decisionmaker. First, the main concepts of the utility approach are briefly reviewed from the point of view of multiobjective systems analysis, and some shortages of this approach are examined. Second, a new method which we call the nested Lagrangian multiplier method (or NLM method) is introduced and compared with precedent devices for the utility approach. The theoretical background is also scrutinized. Third, the use of the NLM method for environmental systems management in the greater Osaka area is demonstrated, providing an example of dynamic application of this method. Finally, it is recalled that utilization of a mathematical optimization method for integrated plannings would simultaneously provide optimal solutions for allocation as well as evaluation problems, based on duality of mathematical programming. A stress is placed on the utilization of dual optimal solutions as a base of evaluation factors

System and Decision Sciences at IIASA 1973-1980

This report contains a brief history of the past achievements of the System and Decision Sciences Area at IIASA, and a summary of its current and future research directions. There is a comprehensive list of the scientific staff of the Area since 1973, together with a list of their publications; abstracts of the most recent reports and biographies of the scholars working in the Area in 1980 are also included

Conflicting Objectives in Decisions

This book deals with quantitative approaches in making decisions when conflicting objectives are present. This problem is central to many applications of decision analysis, policy analysis, operational research, etc. in a wide range of fields, for example, business, economics, engineering, psychology, and planning. The book surveys different approaches to the same problem area and each approach is discussed in considerable detail so that the coverage of the book is both broad and deep. The problem of conflicting objectives is of paramount importance, both in planned and market economies, and this book represents a cross-cultural mixture of approaches from many countries to the same class of problem

Maintenance and safety of deteriorating systems: a life-cycle perspective

This paper reviews the key aspects associated with maintenance and safety of deteriorating infrastructure systems from a life-cycle perspective. The main conceptual aspects related to probabilistic optimization of maintenance and rehabilitation of structural systems are discussed. These aspects include life-cycle risk and sustainability assessment, risk-informed and utility-based decision making, and multi-objective optimization of interventions. In general, sustainability assessment is performed by quantifying economic, social, and environmental impacts associated with infrastructure management activities. This keynote paper also reviews various methods for determining optimum life-cycle maintenance, repair, and rehabilitation types and times, as well as the impact of such activities on the total life-cycle cost. The role of probabilistic performance indicators including reliability and risk, the sustainability assessment of deteriorating infrastructure systems, and risk- and utility-informed decision making are highlighted herein

Analysis of Layered Social Networks

Prevention of near-term terrorist attacks requires an understanding of current terrorist organizations to include their composition, the actors involved, and how they operate to achieve their objectives. To aid this understanding, operations research, sociological, and behavioral theory relevant to the study of social networks are applied, thereby providing theoretical foundations for new methodologies to analyze non-cooperative organizations, defined as those trying to hide their structure or are unwilling to provide information regarding their operations. Techniques applying information regarding multiple dimensions of interpersonal relationships, inferring from them the strengths of interpersonal ties, are explored. A layered network construct is offered that provides new analytic opportunities and insights generally unaccounted for in traditional social network analyses. These provide decision makers improved courses of action designed to impute influence upon an adversarial network, thereby achieving a desired influence, perception, or outcome to one or more actors within the target network. This knowledge may also be used to identify key individuals, relationships, and organizational practices. Subsequently, such analysis may lead to the identification of exploitable weaknesses to either eliminate the network as a whole, cause it to become operationally ineffective, or influence it to directly or indirectly support National Security Strategy

Robustness of Multiple Objective Decision Analysis Preference Functions

This research investigated value and utility functions in multiobjective decision analysis to examine the relationship between them in a military decision making context. The impact of these differences was examined to improve implementation efficiency. The robustness of the decision model was examined with respect to the preference functions to reduce the time burden imposed on the decision maker. Data for decision making in a military context supports the distinction between value and utility functions. Relationships between value and utility functions and risk attitudes were found to be complex. Elicitation error was significantly smaller than the difference between value and utility functions. Risk attitudes were generally neither constant across the domain of the evaluation measure nor consistent between evaluation measures. An improved measure of differences between preference functions, the weighted root means square, is introduced and a goodness of fit criterion established. An improved measure of risk attitudes employing utility functions is developed. Response Surface Methodology was applied to improve the efficiency of decision analysis utility model applications through establishing the robustness of decision models to the preference functions. An algorithm was developed and employs this information to provide a hybrid value-utility model that offers increased elicitation efficiency

Machine learning and statistical techniques : an application to the prediction of insolvency in Spanish non-life insurance companies

Prediction of insurance companies insolvency has arisen as an important problem in the field of financial research. Most methods applied in the past to tackle this issue are traditional statistical techniques which use financial ratios as explicative variables. However, these variables often do not satisfy statistical assumptions, which complicates the application of the mentioned methods. In this paper, a comparative study of the performance of two non-parametric machine learning techniques (See5 and Rough Set) is carried out. We have applied the two methods to the problem of the prediction of insolvency of Spanish non-life insurance companies, upon the basis of a set of financial ratios. We also compare these methods with three classical and well-known techniques: one of them belonging to the field of Machine Learning (Multilayer Perceptron) and two statistical ones (Linear Discriminant Analysis and Logistic Regression). Results indicate a higher performance of the machine learning techniques. Furthermore, See5 and Rough Set provide easily understandable and interpretable decision models, which shows that these methods can be a useful tool to evaluate insolvency of insurance firms.El pronóstico sobre la insolvencia de las compañías de seguro ha surgido como un problema importante en el ámbito de investigación financiera. La mayoría de los métodos aplicados en el pasado para abordar este problema, son técnicas estadísticas tradicionales que usan los ratios financieros como variables explicativas. Aunque, estas variables a menudo no satisfacen las suposiciones estadísticas, lo que complica la aplicación de dichos métodos. En este artículo, se lleva a cabo un estudio comparativo sobre la actuación de dos técnicas de aprendizaje automático no paramétrico (See5 y Rough Set). Hemos aplicado ambos métodos al problema del pronóstico sobre la insolvencia de compañías españolas de seguros no de vida, sobre la base de un conjunto de ratios financieros. Además, hemos comparado estos métodos con tres técnicas clásicas y muy conocidas: una de ellas perteneciente al área del Aprendizaje Automático (Perceptrón Multicapa), y dos estadísticos (Análisis Discriminante Lineal y Regresión Logística). Los resultados indican un desempeño más elevado en las técnicas de aprendizaje automático. Es más, See5 y Rough Set aportan unos modelos de decisión fácilmente entendibles, e interpretables, lo que demuestra que estos métodos pueden ser útiles para evaluar la insolvencia de empresas de seguros

A methodology for probabilistic aircraft technology assessment and selection under uncertainty

The high degree of complexity and uncertainty associated with aerospace engineering applications has driven designers and engineers towards the use of probabilistic and statistical analysis tools in order to understand and design for that uncertainty. As a result, probabilistic methods have permeated the aerospace field to the extent that single point deterministic designs are no longer credible, particularly in systems analysis, performance assessment, technology impact quantification, etc. However, as statistics theory is not the primary focus of most aerospace practitioners, incorrect assumptions and flawed methods are often unknowingly used in design. A common assumption of probabilistic assessments in the field of aerospace is the independence of random variables. These random variables represent design variables, noise variables, technology impacts, etc., which can be difficult to correlate but do have underlying relationships. The justification for the assumed independence is usually not discussed in the literature even though this can have a substantial effect on probabilistic assessment and uncertainty quantification results. In other cases the dependence between random variables is acknowledged but intentionally ignored on the basis of difficulty in characterizing underlying random variable relationships, a strong bias towards methodological simplicity and low computational expense, and the expectation of modest strength in random variable dependence. Probabilistic assessments also yield large amounts of data which is not effectively used due to the sheer volume of data and poor traceability to the drivers of uncertainty. The literature shows optimization techniques are resorted to in order to select from competing alternatives in multiobjective spaces, however, these techniques generally do not handle uncertainty well. The motivating question is, how can improvements be made to the probabilistic assessment process for aircraft technology assessments that capture technology impact tradeoffs and dependencies, and ultimately enable decision makers to make an axiomatic and rational selection under uncertainty? This question leads to the research objective of this work which is to develop a methodology ``to quantify and characterize aviation's environmental impact, uncertainties, and the trade-offs and interdependencies among various impacts'' \cite{Council2010}, in order to assess and select future aircraft technologies. Copula theory is suggested to address the problem of assumed independence on the input side of probabilistic assessments in aerospace applications. Copulas are functions that can be used to define probabilistic relationships between random variables. They are well documented in the literature and have been used in many fields such as the statistics, finance, and insurance industries. They can be used to quantify complex relationships, even if that is only qualitatively or notionally understood. In this way a designer's knowledge regarding uncertainty can be better represented and propagated to system level metrics through the probabilistic assessment. Utility theory is proposed as a solution to the challenge of effectively using output data from probabilistic assessments. Utility theory is a powerful tool used in economics, marketing, psychiatry, etc., to express preferences among competing alternatives. Utility theory can provide combined valuation to each alternative in a multiobjective design space while incorporating the uncertainty associated with each alternative. This can enable designers to rationally and axiomatically make selections consistent with their preferences, between complex solutions with varying degrees of uncertainty. This work provides an introduction to copula and utility theories for the aerospace audience. It also demonstrates how these theories can be applied in canonical problems to bridge gaps currently found in the literature with regards to probabilistic assessments of aircraft technologies. The key contributions of this research are (1) an Archimedean copula selection tree enabling practitioners to rapidly translate their qualitative understanding of dependence into copula families that can represent it quantitatively (2) estimation of the quantified effect of using copulas to capture probabilistic dependence in three representative aerospace applications (3) an expected utility formulation for axiomatically ranking and selecting aircraft technology packages under uncertainty and (4) a strategic elicitation procedure for multiattribute utility functions that does not need assumptions of independence conditions on preferences between the attributes. The proposed FAAST methodology is shown as an encompassing framework for the aircraft technology assessment and selection problem that fills capability gaps from the literature and supports the decision maker in a rational and axiomatic manner.Ph.D

Value Creation through Co-Opetition in Service Networks

Well-defined interfaces and standardization allow for the composition of single Web services into value-added complex services. Such complex Web Services are increasingly traded via agile marketplaces, facilitating flexible recombination of service modules to meet heterogeneous customer demands. In order to coordinate participants, this work introduces a mechanism design approach - the co-opetition mechanism - that is tailored to requirements imposed by a networked and co-opetitive environment