The Science And Art Of Simulation I

The new book series “The Science and Art of Simulation” (SAS) addresses computer simulations as a scientific activity and engineering artistry (in the sense of a technē). The first volume is devoted to three topics: 1. The Art of Exploring Computer Simulations Philosophy began devoting attention to computer simulations at a relatively early stage. Since then, the unquestioned point of view has been that computer simulation is a new scientific method; the philosophy of simulation is therefore part of the philosophy of science. The first section of this volume discusses this implicit, unchallenged assumption by addressing, from different perspectives, the question of how to explore (and how not to explore) research on computer simulations. Scientists discuss what is still lacking or considered problematic, while philosophers draft new directions for research, and both examine the art of exploring computer simulations. 2. The Art of Understanding Computer Simulations The results of computer simulations are integrated into both political and social decisions. It is implicitly assumed that the more detailed, and consequently more realistic, a computer simulation is, the more useful it will be in decision-making. However, this idea is by no means justified. Different types of computer simulations have to be differentiated, which in turn requires the specific skill of understanding computer simulation results. The articles in this section examine the capabilities and limits of simulation results in political and social contexts, exploring the art of understanding computer simulation results. 3. The Art of Knowing through Computer Simulations? The advent of computer simulation in today’s scientific practices challenges the order of science. What kind of knowledge is gained through computer simulations is the key question in this section. Computer simulations are often compared to experiments or to arguments, and the transformation of our traditional scientific notions might be more challenging than expected – these Ideas are put forward in the third section to conceptualize the art of knowing through computer simulations.

Fundamentals of modeling. Systems investigation.

Computer simulation was first pioneered as a scientific tool in meteorology and nuclear physics in the period following World War II, but it has grown rapidly to become indispensible in a wide variety of scientific disciplines, including astrophysics, high-energy physics, climate science, engineering, ecology, and economics. Digital computer simulation helps study phenomena of great complexity, but how much do we know about the limits and possibilities of this new scientific practice? How do simulations compare to traditional experiments? And are they reliable? Eric Winsberg seeks to answer these questions in Science in the Age of Computer Simulation. Scrutinizing these issue with a philosophical lens, Winsberg explores the impact of simulation on such issues as the nature of scientific evidence; the role of values in science; the nature and role of fictions in science; and the relationship between simulation and experiment, theories and data, and theories at different levels of description. Science in the Age of Computer Simulation will transform many of the core issues in philosophy of science, as well as our basic understanding of the role of the digital computer in the sciences.

Seminar paper from the year 2010 in the subject Philosophy - Theoretical (Realisation, Science, Logic, Language), grade: 1,0, University of Stuttgart (Institut für Philosophie/Wissenschaftstheorie/Technikphilosophie), course: HS Philosophy of Simulation, language: English, abstract: In the early 1980s, Robert Axelrod published several articles on The Evolution of Cooperation, discussing and interpreting the results of his well-known computer tournaments and of a series of subsequent simulations. Both the tournaments and simulations were conducted in order to find a suitable, evolutionary stable strategy for the iterated prisoner's dilemma, which is generally considered an appropriate model of a certain type of social dilemma that arises when "the pursuit of self-interest by each leads to a poor outcome for all." The results of the tournaments and simulations led to a generalized theory of the evolution of cooperation, which claims to provide an explanation for various historical, social and biological phenomena. Axelrod`s work contributed extensively to popularizing computer simulation as a scientific method in the social sciences. Besides the fact that his approach had an unquestionably high impact on succeeding research and ushered in the "simulation era" in the social sciences, the use Axelrod made of computer simulations raises questions about their methodological and epistemological status: If, as Axelrod states in his paper "Advancing the Art of Simulation in the Social Sciences", simulation can serve the purposes of prediction, proof and even scientific discovery, what need is there for conducting experiments any longer? Can't we simulate science? Admittedly, this suggestion sounds somewhat exaggerated, but why exactly do most of us share the intuition that there are fundamental differences persisting between simulations and experiments? What are the characteristic features distinguishing them? Do computer simulations in general - and Axelrod's tournaments in particular - resemble experiments insofar as their potential to provide us with surprising results that permit further theorizing is concerned? Or are they nothing else than mere "number-crunching techniques", using brute-force computational means in order to generate data from theoretical knowledge and assumptions already built into the underlying model? The question where to draw the conceptual line between simulation and experiment has turned out to be of great interest to philosophy of science, not least since the categorization might be relevant to the way the results are assessed and used. The objective of this paper is to elaborate on the distinctive characteristics of simulations in contrast to experiments.

Simulation is the art of using tools – physical or conceptual models, or computer hardware and software, to attempt to create the illusion of reality. The discipline has in recent years expanded to include the modelling of systems that rely on human factors and therefore possess a large proportion of uncertainty, such as social, economic or commercial systems. These new applications make the discipline of modelling and simulation a field of dynamic growth and new research. Stanislaw Raczynski outlines the considerable and promising research that is being conducted to counter the problems of uncertainty surrounding the methods used to approach these new applications. It aims to stimulate the reader into seeking out new tools for modelling and simulation. Examines the state-of-the-art in recent research into methods of approaching new applications in the field of modelling and simulation Provides an introduction to new modelling tools such as differential inclusions, metric structures in the space of models, semi-discrete events, and use of simulation in parallel optimization techniques Discusses recently developed practical applications: for example the PASION simulation system, stock market simulation, a new fluid dynamics tool, manufacturing simulation and the simulation of social structures Illustrated throughout with a series of case studies Modelling and Simulation: The Computer Science of Illusion will appeal to academics, postgraduate students, researchers and practitioners in the modelling and simulation of industrial computer systems. It will also be of interest to those using simulation as an auxiliary tool.

This book/software package brings the tools and excitement of modeling to pre-college teachers, to researchers involved in curriculum development, and to software developers interested in the pre-college market.

This unique volume introduces and discusses the methods of validating computer simulations in scientific research. The core concepts, strategies, and techniques of validation are explained by an international team of pre-eminent authorities, drawing on expertise from various fields ranging from engineering and the physical sciences to the social sciences and history. The work also offers new and original philosophical perspectives on the validation of simulations. Topics and features: introduces the fundamental concepts and principles related to the validation of computer simulations, and examines philosophical frameworks for thinking about validation; provides an overview of the various strategies and techniques available for validating simulations, as well as the preparatory steps that have to be taken prior to validation; describes commonly used reference points and mathematical frameworks applicable to simulation validation; reviews the legal prescriptions, and the administrative and procedural activities related to simulation validation; presents examples of best practice that demonstrate how methods of validation are applied in various disciplines and with different types of simulation models; covers important practical challenges faced by simulation scientists when applying validation methods and techniques; offers a selection of general philosophical reflections that explore the significance of validation from a broader perspective. This truly interdisciplinary handbook will appeal to a broad audience, from professional scientists spanning all natural and social sciences, to young scholars new to research with computer simulations. Philosophers of science, and methodologists seeking to increase their understanding of simulation validation, will also find much to benefit from in the text.