The Arrow of the Law in Modern Administrative States: Using Complexity Theory to Reveal the Diminishing Returns and Increasing Risks the Burgeoning of Law Poses to Society

This article is the third in my series of articles exploring the application of complex adaptive systems (CAS) theory to legal systems. Building on the model outlined in the first two installments (in the Duke and Vanderbilt law reviews), this work examines the arrow or direction of the legal system in the context of the administrative state. Drawing from diverse work such as Burke\u27s study of history\u27s nonlinearity and Tainter\u27s classic study of the collapse of complex civilizations, we argue that the administrative state is becoming too resource intensive and burdened by a proliferation of rules

Performance modelling for system-level design

xii+208hlm.;24c

Mapping environmental injustices: pitfalls and potential of geographic information systems in assessing environmental health and equity.

Geographic Information Systems (GIS) have been used increasingly to map instances of environmental injustice, the disproportionate exposure of certain populations to environmental hazards. Some of the technical and analytic difficulties of mapping environmental injustice are outlined in this article, along with suggestions for using GIS to better assess and predict environmental health and equity. I examine 13 GIS-based environmental equity studies conducted within the past decade and use a study of noxious land use locations in the Bronx, New York, to illustrate and evaluate the differences in two common methods of determining exposure extent and the characteristics of proximate populations. Unresolved issues in mapping environmental equity and health include lack of comprehensive hazards databases; the inadequacy of current exposure indices; the need to develop realistic methodologies for determining the geographic extent of exposure and the characteristics of the affected populations; and the paucity and insufficiency of health assessment data. GIS have great potential to help us understand the spatial relationship between pollution and health. Refinements in exposure indices; the use of dispersion modeling and advanced proximity analysis; the application of neighborhood-scale analysis; and the consideration of other factors such as zoning and planning policies will enable more conclusive findings. The environmental equity studies reviewed in this article found a disproportionate environmental burden based on race and/or income. It is critical now to demonstrate correspondence between environmental burdens and adverse health impacts--to show the disproportionate effects of pollution rather than just the disproportionate distribution of pollution sources

Efficient simulation tools for real-time monitoring and control using model order reduction and data-driven techniques

Numerical simulation, the use of computers to run a program which implements a mathematical model for a physical system, is an important part of today technological world. It is required in many scientific and engineering fields to study the behaviour of systems whose mathematical models are too complex to provide analytical solutions and it makes virtual evaluation of systems responses possible (virtual twins). This drastically reduces the number of experimental tests for accurate designs of the real system that the numerical model represents. However these virtual twins, based on classical methods which make use of a rich representations of the system (ex. finite element method), rarely allows real-time feedback, even when considering high performance computing, operating on powerful platforms. In these circumstances, the real-time performance required in some applications are compromised. Indeed the virtual twins are static, that is, they are used in the design of complex systems and their components, but they are not expected to accommodate or assimilate data so as to define dynamic data-driven application systems. Moreover significant deviations between the observed response and the one predicted by the model are usually noticed due to inaccuracy in the employed models, in the determination of the model parameters or in their time evolution. In this thesis we propose different methods to solve these handicaps in order to perform real-time monitoring and control. In the first part Model Order Reduction (MOR) techniques are used to accommodate real-time constraints; they compute a good approximation of the solution by simplifying the solution procedure instead of the model. The accuracy of the predicted solution is not compromised and efficient simulations can be performed (digital twins). In the second part Data-Driven modelling are employed to fill the gap between the parametric solution computed by using non-intrusive MOR techniques and the measured fields, in order to make dynamic data-driven application systems, DDDAS, possible (Hybrid Twins).La simulación numérica, el uso de ordenadores para ejecutar un programa que implementa un modelo matemático de un sistema físico, es una parte importante del mundo tecnológico actual. En muchos campos de la ciencia y la ingeniería es necesario estudiar el comportamiento de sistemas cuyos modelos matemáticos son demasiado complejos para proporcionar soluciones analíticas, haciendo posible la evaluación virtual de las respuestas de los sistemas (gemelos virtuales). Esto reduce drásticamente el número de pruebas experimentales para los diseños precisos del sistema real que el modelo numérico representa. Sin embargo, estos gemelos virtuales, basados en métodos clásicos que hacen uso de una rica representación del sistema (por ejemplo, el método de elementos finitos), rara vez permiten la retroalimentación en tiempo real, incluso cuando se considera la computación en plataformas de alto rendimiento. En estas circunstancias, el rendimiento en tiempo real requerido en algunas aplicaciones se ve comprometido. En efecto, los gemelos virtuales son estáticos, es decir, se utilizan en el diseño de sistemas complejos y sus componentes, pero no se espera que acomoden o asimilen los datos para definir sistemas de aplicación dinámicos basados en datos. Además, se suelen apreciar desviaciones significativas entre la respuesta observada y la predicha por el modelo, debido a inexactitudes en los modelos empleados, en la determinación de los parámetros del modelo o en su evolución temporal. En esta tesis se proponen diferentes métodos para resolver estas limitaciones con el fin de realizar un seguimiento y un control en tiempo real. En la primera parte se utilizan técnicas de Reducción de Modelos para satisfacer las restricciones en tiempo real; estas técnicas calculan una buena aproximación de la solución simplificando el procedimiento de resolución en lugar del modelo. La precisión de la solución no se ve comprometida y se pueden realizar simulaciones efficientes (gemelos digitales). En la segunda parte se emplea la modelización basada en datos para llenar el vacío entre la solución paramétrica, calculada utilizando técnicas de reducción de modelos no intrusivas, y los campos medidos, con el fin de hacer posibles los sistemas de aplicación dinámicos basados en datos (gemelos híbridos).La simulation numérique, c'est-à-dire l'utilisation des ordinateurs pour exécuter un programme qui met en oeuvre un modèle mathématique d'un système physique, est une partie importante du monde technologique actuel. Elle est nécessaire dans de nombreux domaines scientifiques et techniques pour étudier le comportement de systèmes dont les modèles mathématiques sont trop complexes pour fournir des solutions analytiques et elle rend possible l'évaluation virtuelle des réponses des systèmes (jumeaux virtuels). Cela réduit considérablement le nombre de tests expérimentaux nécessaires à la conception précise du système réel que le modèle numérique représente. Cependant, ces jumeaux virtuels, basés sur des méthodes classiques qui utilisent une représentation fine du système (ex. méthode des éléments finis), permettent rarement une rétroaction en temps réel, même dans un contexte de calcul haute performance, fonctionnant sur des plates-formes puissantes. Dans ces circonstances, les performances en temps réel requises dans certaines applications sont compromises. En effet, les jumeaux virtuels sont statiques, c'est-à-dire qu'ils sont utilisés dans la conception de systèmes complexes et de leurs composants, mais on ne s'attend pas à ce qu'ils prennent en compte ou assimilent des données afin de définir des systèmes d'application dynamiques pilotés par les données. De plus, des écarts significatifs entre la réponse observée et celle prévue par le modèle sont généralement constatés en raison de l'imprécision des modèles employés, de la détermination des paramètres du modèle ou de leur évolution dans le temps. Dans cette thèse, nous proposons di érentes méthodes pour résoudre ces handicaps afin d'effectuer une surveillance et un contrôle en temps réel. Dans la première partie, les techniques de Réduction de Modèles sont utilisées pour tenir compte des contraintes en temps réel ; elles calculent une bonne approximation de la solution en simplifiant la procédure de résolution plutôt que le modèle. La précision de la solution n'est pas compromise et des simulations e caces peuvent être réalisées (jumeaux numériquex). Dans la deuxième partie, la modélisation pilotée par les données est utilisée pour combler l'écart entre la solution paramétrique calculée, en utilisant des techniques de réduction de modèles non intrusives, et les champs mesurés, afin de rendre possibles des systèmes d'application dynamiques basés sur les données (jumeaux hybrides)

Special Libraries, August 1980

Volume 71, Issue 8https://scholarworks.sjsu.edu/sla_sl_1980/1006/thumbnail.jp

Methods for Employing Real Options Models to Mitigate Risk in R&D Funding Decisions

Government acquisitions requiring research and development (R&D) efforts are fraught with uncertainty. The risks are often mitigated by employing a multi-stage competition, with multiple projects funded initially until a single successful project is selected. While decision-makers recognize they are using a real options approach, analytical tools are often unavailable to evaluate optimal decisions. The use of these techniques for R&D project selection to reduce the uncertainties has been shown to increase overall project value. This dissertation first presents an efficient stochastic dynamic programming (SDP) approach that managers can use to determine optimal project selection strategies and apply the proposed approach on illustrative numerical examples. While the SDP approach produces optimal solutions for many applications, this approach does not easily accommodate the inclusion of a budget-optimal allocation or side constraints, since its formulation is scenario specific. Thus, we then formulate an integer program (IP), whose solution set is equivalent to the SDP model, but facilitates the incorporation of these features and can be solved using available commercial IP solvers. The one-level IP formulation can solve what is otherwise a nested two-level problem when solved as an SDP. We then compare the performance of both models on differently sized problems. For larger problems, where the IP approach appears to be untenable, we provide heuristics for the two-level SDP formulation to solve problems efficiently. Finally, we apply these methods to carbon capture and storage (CCS) projects in the European Union currently under development that may be subject to public funding. Taking the perspective of a funding agency, we employ the real options models presented in this dissertation for determining optimal funding strategies for CCS project selection. The models demonstrate the improved risk reduction by employing a multi-stage competition and explicitly consider the benefits of knowledge spillover generated by competing projects. We then extend the model to consider two sensitivities: 1) the flexibility to spend the budget among the time periods and 2) optimizing the budget, but specifying each time period's allocation a priori. State size, scenario reduction heuristics and run-times of the models are provided

Pitfalls of Analysis

Systems Analysis-or Policy Analysis, as it is sometimes called when public problems are being addressed-aims to deepen our understanding of sociotechnical problems, and to bring about improved solutions to them. However, the craft of systems analysis is a relatively new one, and its practitioners are still exploring ways to conduct such analyses properly, a task complicated by the necessary use of tools and knowledge from many disciplines and contexts. Any craft learns both by what works and by what fails to work, and systems analysis is no exception. This book contributes to the craft of systems analysis by describing many pitfalls of analysis that will lead to failure, thus helping analysts and users to recognize and avoid them