Explaining through causal mechanisms : resilience and governance of social–ecological systems

This paper synthesizes and builds on recent critiques of the resilience literature; namely that the field has largely been unsuccessful in capturing the complexity of governance processes, in particular cause–effects relationships. We demonstrate that absence of a causal model is reflected in the black-boxing of governance processes which is problematic for resilience studies with explanatory ambitions. We introduce mechanism-based thinking as alternative research perspective that offers more analytical rigour and elaborate the key principles of this approach. Mechanism-based approaches are aligned to the ways of thinking in systems theory and complexity sciences and can be used to advance scientific inquiry and policy practice to govern complex sustainability issues

The Tools of Policy Formulation:An Introduction

What techniques or means do public policymakers use in their attempts to achieve policy goals? The roles of what may be termed policy instruments, tools and methods (Howlett 2011, p. 22) have attracted a great deal of attention. It is generally accepted that policy tools and instruments exist at all stages of the policy process (Howlett 2011, p. 22), ranging from policy formulation through to ex post evaluation (Dunn 2004). But in the public policy literature, much of the debate has focused on instruments for implementing agreed policy objectives, such as regulations, subsidies, taxes and voluntary agreements (Hood 1983; Hood and Margetts 2007; Salamon 2002). Recently, a second category of implementing instruments has been identified: procedural tools (Howlett 2000). These include education, training, provision of information and public hearings. These are procedural in the sense that they seek to affect outcomes indirectly through manipulating policy processes. The manner in which both types of instruments are selected and deployed aims to change the substance, effects and outcomes of policy, by sending signals about what is to be achieved and how government is likely to respond to target groups

The "Lumpiness" Thesis Revisited: The Venures of Policy Work and the Distribution of Analytical ...

10.2139/ssrn.22600281-3

A Rewriting-Logic-Based Technique for Modeling Thermal Systems

This paper presents a rewriting-logic-based modeling and analysis technique for physical systems, with focus on thermal systems. The contributions of this paper can be summarized as follows: (i) providing a framework for modeling and executing physical systems, where both the physical components and their physical interactions are treated as first-class citizens; (ii) showing how heat transfer problems in thermal systems can be modeled in Real-Time Maude; (iii) giving the implementation in Real-Time Maude of a basic numerical technique for executing continuous behaviors in object-oriented hybrid systems; and (iv) illustrating these techniques with a set of incremental case studies using realistic physical parameters, with examples of simulation and model checking analyses.Comment: In Proceedings RTRTS 2010, arXiv:1009.398

Robust polynomial controller design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The work presented in this thesis was motivated by the desire to establish an alternative approach to the design of robust polynomial controllers. The procedure of pole-placement forms the basis of the design and for polynomial systems this generally involves the solution of a diophantine equation. This equation has many possible solutions which leads directly to the idea of determining the most appropriate solution for improved performance robustness. A thorough review of many of the aspects of the diophantine equation is presented, which helps to gain an understanding of this extremely important equation. A basic investigation into selecting a more robust solution is carried out but it is shown that, in the polynomial framework, it is difficult to relate decisions in the design procedure to the effect on performance robustness. This leads to the approach of using a state space based design and transforming the resulting output feedback controller to polynomial form. The state space design is centred around parametric output feedback which explicitly represents a set of possible feedback controllers in terms of arbitrary free parameters. The aim is then to select these free parameters such that the closed-loop system has improved performance robustness. Two parametric methods are considered and compared, one being well established and the other a recently proposed scheme. Although the well established method performs slightly better for general systems it is shown to fail when applied to this type of problem. For performance robustness, the shape of the transient response in the presence of model uncertainty is of interest. It is well known that the eigenvalues and eigenvectors play an important role in determining the transient behaviour and as such the sensitivities of these factors to model uncertainty forms the basis on which the free parameters are selected. Numerical optimisation is used to select the free parameters such that the sensitivities are at a minimum. It is shown both in a simple example and in a more realistic application that a significant improvement in the transient behaviour in the presence of model uncertainty can be achieved using the proposed design procedure.UK Science and Engineering Research Counci

The Role of Control and System Theory in Systems Biology

The use of new technology and mathematics to study the systems of nature is one of the most significant scientific trends of the century. Driven by the need for more precise scientific understand, advances in automated measurement are providing rich new sources of biological and physiological data. This data provides information with which to create mathematical models of increasing sophistication and realism - models that can emulate the performance of biological and physiological systems with sufficient accuracy to advance our understanding of living systems and disease mechanisms. New measurement and modelling methods set the stage for control and systems theory to play their role in seeking out the mechanisms and principles that regulate life. It is of inestimable importance for the future of control as a discipline that this role is performed in the correct manner. If we handle the area wisely then living systems will present a seemly boundless range of important new problems - just as physical and engineering systems have done in previous centuries. But there is a crucial difficulty. Faced with a bewildering array of choices in an unfamiliar area, how does a researcher select a worthwhile and fruitful problem? This lecture is an attempt to help by offering a control oriented guide to the labyrinthine world of biology/physiology and the control research opportunity that it holds

Mainstreaming and beyond: Policy capacity and climate change decision-making

Mainstreaming involves integrating climate adaptation measures into existing policies and programs. This article reviews the policy process and policy capacity of government organizations and suggests that both need to be incorporated into climate change adaptation assessments. A critical part of mainstreaming is evidence-based decision-making, which emphasizes that decision makers should have the best available information in order to make knowledgeable decisions. This requires policy work that involves a wide variety of statistical methods, applied research, and advanced modeling techniques to gauge broad public opinion and attitudes as well as more routine research techniques. A review of previous past quantitative studies conducted mainly in Canada identifies factors driving policy capacity within government departments responsible for formulating, choosing, implementing, and evaluating climate change adaptation policies and programs. Policy capacity has traditionally been objectively measured and includes indicators such as the number of policy staff, their education levels, resources available, roles and tasks, and ongoing training. More attention needs to be paid to the subjective perceptions of individuals who undertake policy work, in particular the attitudes towards the policy-making process. This paper concludes by proposing a policy capacity framework that includes individual, organizational, and sectoral policy capacity considerations

Derivative-based optimization in colour image filtering: an application for derivative learning

[EN] Related to the notion of derivative of a function, its application to function optimization is an interesting and illustrative problem for Engineering students. In the present work, we develop an application of the derivative concept to optimize the filtering of a colour image. This implies to optimize the value of the filter parameter to maximize performance. We propose to maximize the quality of the filtered image represented by the Peak Signal to Noise Ratio (PSNR), which is a function of the filter parameter. The optimal value for the parameter is obtained by means of an algorithm based on the approximation of the derivative of the PSNR function so that finally the optimum filtered image is obtained.[ES] La noción de derivada de una función y su aplicación a la optimización de funciones es un problema interesante e ilustrativo para los estudiantes de ingeniería. En este trabajo, desarrollamos un aplicación del concepto de derivada para la optimización del filtrado de imágenes en color. Ello implica ajustar el parámetro del filtro para obtener un rendimiento óptimo de filtrado. Proponemos maximizar la calidad de la imagen filtrada representada por la relación señal-ruido (Peak Signal-to-Noise Ratio, PSNR), que es una función del parámetro del filtro. El valor óptimo del parámetro se obtiene mediante un algoritmo basado en la aproximación de la derivada de la función del PSNR de manera que se obtenga la imagen filtrada óptima.Sapena, A.; Morillas, S.; Camacho, J. (2009). Derivative-based optimization in colour image filtering: an application for derivative learning. Modelling in Science Education and Learning. 2:45-55. doi:10.4995/msel.2009.3117SWORD4555