Simplicity Complexity And Modelling

Several points of disagreement exist between different modelling traditions as to whether complex models are always better than simpler models, as to how to combine results from different models and how to propagate model uncertainty into forecasts. This book represents the result of collaboration between scientists from many disciplines to show how these conflicts can be resolved. Key Features: Introduces important concepts in modelling, outlining different traditions in the use of simple and complex modelling in statistics. Provides numerous case studies on complex modelling, such as climate change, flood risk and new drug development. Concentrates on varying models, including flood risk analysis models, the petrol industry forecasts and summarizes the evolution of water distribution systems. Written by experienced statisticians and engineers in order to facilitate communication between modellers in different disciplines. Provides a glossary giving terms commonly used in different modelling traditions. This book provides a much-needed reference guide to approaching statistical modelling. Scientists involved with modelling complex systems in areas such as climate change, flood prediction and prevention, financial market modelling and systems engineering will benefit from this book. It will also be a useful source of modelling case histories.

Simulation models are an established method used to investigate processes and solve practical problems in a wide variety of disciplines. Central to the concept of this second edition is the idea that environmental systems are complex, open systems. The authors present the diversity of approaches to dealing with environmental complexity and then encourage readers to make comparisons between these approaches and between different disciplines. Environmental Modelling: Finding Simplicity in Complexity 2nd edition is divided into four main sections: An overview of methods and approaches to modelling. State of the art for modelling environmental processes Tools used and models for management Current and future developments. The second edition evolves from the first by providing additional emphasis and material for those students wishing to specialize in environmental modelling. This edition: Focuses on simplifying complex environmental systems. Reviews current software, tools and techniques for modelling. Gives practical examples from a wide variety of disciplines, e.g. climatology, ecology, hydrology, geomorphology and engineering. Has an associated website containing colour images, links to WWW resources and chapter support pages, including data sets relating to case studies, exercises and model animations. This book is suitable for final year undergraduates and postgraduates in environmental modelling, environmental science, civil engineering and biology who will already be familiar with the subject and are moving on to specialize in the field. It is also designed to appeal to professionals interested in the environmental sciences, including environmental consultants, government employees, civil engineers, geographers, ecologists, meteorologists, and geochemists.

Simulation models are increasingly used to investigate processes and solve practical problems in a wide variety of disciplines eg. climatology, ecology, hydrology, geomorphology, engineering. Environmental Modelling: A Practical Approach addresses the development, testing and application of such models, which apply across traditional boundaries, and demonstrate how interactions across these boundaries can be beneficial. Provides a general overview of methods and approaches as well as focusing on key subject areas written by leading practitioners in the field Assesses the advantages and disadvantages of different models used and provides case studies supported with data, output, tutorial exercises and links to the model and/or model applications via the book's website Covers major developments in the field, eg. the use of GIS and remote sensing techniques, and scaling issues As associated website contains colour images, as well as links to www resources

The idea that simplicity matters in science is as old as science itself, with the much cited example of Ockham's Razor, 'entia non sunt multiplicanda praeter necessitatem': entities are not to be multiplied beyond necessity. A problem with Ockham's razor is that nearly everybody seems to accept it, but few are able to define its exact meaning and to make it operational in a non-arbitrary way. Using a multidisciplinary perspective including philosophers, mathematicians, econometricians and economists, this 2002 monograph examines simplicity by asking six questions: what is meant by simplicity? How is simplicity measured? Is there an optimum trade-off between simplicity and goodness-of-fit? What is the relation between simplicity and empirical modelling? What is the relation between simplicity and prediction? What is the connection between simplicity and convenience? The book concludes with reflections on simplicity by Nobel Laureates in Economics.

This paper describes, in its introduction, its main objective and some of its investigative premises,emphasizing the need to address micro- and macroeconomic models using the major principles of statistical thinking. A central section is devoted to the concepts of complexity and simplicity, rediscovering the paradox of approaching them simultaneously and the paradigm of simplexity, first formulated by Jeffrey Kluger.

This book investigates the characteristics of simple versus complex systems, and what the properties of a cyber-physical system design are that contribute to an effective implementation and make the system understandable, simple to use, and easy to maintain. The targeted audience is engineers, managers and advanced students who are involved in the design of cyber-physical systems and are willing to spend some time outside the silo of their daily work in order to widen their background and appreciation for the pervasive problems of system complexity. In the past, design of a process-control system (now called cyber-physical systems) was more of an art than an engineering endeavor. The software technology of that time was concerned primarily with functional correctness and did not pay much attention to the temporal dimension of program execution, which is as important as functional correctness when a physical process must be controlled. In the ensuing years, many problems in the design of cyber-physical systems were simplified. But with an increase in the functional requirements and system size, the complexity problems have appeared again in a different disguise. A sound understanding of the complexity problem requires some insight in cognition, human problem solving, psychology, and parts of philosophy. This book presents the essence of the author’s thinking about complexity, accumulated over the past forty years.

How do scientists model crowd behaviour, epidemics, earthquakes or the internet? What can we learn from the collective intelligence and adaptability of an ant colony? This book answers such questions by highlighting common themes in the study of complex systems. Topics covered include self-organisation, emergence, agent-based simulations, complex networks, phase plane plots, fractals, chaos, measures of complexity, model building, and the scientific method. Explanations are simple and concise, with common misconceptions clarified. Numerous exercises help enthusiasts consolidate their understanding through peer learning. Supplementary resources are at the companion websites www.simplicitysg.net/books and www.facebook.com/simcomty.

Complex systems analysis has become a fascinating topic in modern research on non-linear dynamics, not only in the physical sciences but also in the life sciences and the social sciences. After the era of bifurcation theory, chaos theory, syn- getics, resilience analysis, network dynamics and evolutionary thinking, currently we observe an increasing interest in critical transitions of dynamic real-world systems in many disciplines, such as demography, biology, psychology, economics, earth sciences, geology, seismology, medical sciences, and so on. The relevance of this approach is clearly re?ected in such phenomena as traf?c congestion, ?nancial crisis, ethnic con?icts, eco-system breakdown, health failures, etc. This has prompted a world-wide interest in complex systems. Geographical space is one of the playgrounds for complex dynamics, as is witnessed by population movements, transport ?ows, retail developments, urban expansion, lowland ?ooding and so forth. All such dynamic phenomena have one feature in common: the low predictability of uncertain interrelated events occurring at different interconnected spatio-temporal scale levels and often originating from different disciplinary backgrounds. The study of the associated non-linear (fast and slow) dynamic transition paths calls for a joint research effort of scientists from different disciplines in order to understand the nature, the roots and the con- quences of unexpected or unpredictable changes in complex spatial systems.