Klaus G. Troitzsch – författare

Model building in the social sciences can increasingly rely on well elaborated formal theories. At the same time inexpensive large computational capacities are now available. Both make computer-based model building and simulation possible in social science, whose central aim is in particular an understanding of social dynamics. Such social dynamics refer to public opinion formation, partner choice, strategy decisions in social dilemma situations and much more. In the context of such modelling approaches, novel problems in philosophy of science arise which must be analysed - the main aim of this book. Interest in social simulation has recently been growing rapidly world- wide, mainly as a result of the increasing availability of powerful personal computers. The field has also been greatly influenced by developments in cellular automata theory (from mathematics) and in distributed artificial intelligence which provided tools readily applicable to social simulation. This book presents a number of modelling and simulation approaches and their relations to problems in philosophy of science. It addresses sociologists and other social scientists interested in formal modelling, mathematical sociology, and computer simulation as well as computer scientists interested in social science applications, and philosophers of social science.

Das vorliegende Buch ist aus Notizen und Skripten zu einer Vorlesung "Modellbildung und Simulation III den Sozialwissenschaften" hervorgegangen, die ich in der hier dokumentierten Form 1m Sommersemester 1988 zum vierten Male vor Informatik-Studenten mit dem Nebenfach Sozialwissenschaft am Diplom-Studiengang Informatik an der EWH Koblenz gehalten habe. Verschiedene Vorausversionen des vorliegenden Buches sind von den Studenten neben der Vorlesung benutzt worden, die letzte zusätzlich von den Teilnehmern des ZUMA-Workshops "Theorie dynamischer Systeme", den ich im März 1988 beim Zentrum für Umfragen, Methoden und Analysen e.V. in Mannheim durchgeführt habe und an dessen Zustandekommen vor allem Herr Privatdozent Dr.Dr. Ulrich Mueller maßgeblich mitgewirkt hat, wofür ich ihm herzlich danke. Den Teilnehmern dieses Workshops, aber vor allem mehreren Studentenjahrgängen und meinen Freunden und Mitarbeitern Dr. Andreas Engel und Dipl.-Inform. Michael Möhring verdanke ich eine Vielzahl von kritischen Hinweisen auf sprachliche, inhaltliche und mathematische Unstimmigkeiten. Ich hoffe, daß ich diese Unstimmigkeiten beseitigt habe und daß Text und mathematische Ableitungen nun für Sozialwissenschaftler, die über gute Abiturkenntnisse in Mathematik vedügen, ohne große Mühe nachzuvollziehen sind. Der Text ist mit dem von meinem Kollegen Prof. Dr.-Ing. Heinrich Giesen entwickelten Dokumentenverarbeitungssystem SCRIPT aufbereitet worden. Das Programm PIC, mit dessen Hilfe alle Graphiken in den Text integriert worden sind, stammt ebenfalls von ihm. Ich danke ihm für die Bereitschaft, meinen Sonderwünschen an SCRIPT und PIC manchmal über Nacht nachzukommen.

The articles collected in this volume have two features incommon: they wantto integrate economics, demography andgeography, and they want to overcome the stationary approachin modelling in favour of a dynamic one. The book issubdivided into three parts, where Part I is focussing oneconomic evolution, Part II on geographical development andPart III is related to demographic change. The presentvolume aims at providing a new look at this triangle in viewof the classical background of discussions by introducingnew research ideas focussing in nonlinear dynamics andstochastic modelling. Thus the main purpose of this book isto make a contribution to the interdisciplinary work neededto integrate the effortsbetween these three research fieldsand to serve as a research source in demonstrating thecurrent state of art in dynamic modelling.The book isaddressed to social scientists in general, andthose in particular with a background in economics,geographics and demographics. It should also be of interestto mathematicians, physicists, and systems analystsinterested in model building and applications of nonlineardynamics.

This book gives an overview of the state of the art in five different approaches to social science simulation on the individual level. The volume contains microanalytical simulation models designed for policy implementation and evaluation, multilevel simulation methods designed for detecting emergent phenomena, dynamical game theory applications, the use of cellular automata to explain the emergence of structure in social systems, and multi-agent models using the experience from distributed artificial intelligence applied to special phenomena. The book collects the results of an international conference which brought together social scientists and computer scientists both engaged in a wide range of simulation approaches for the first time.

This book gives an overview of the state of the art in five different approaches to social science simulation on the individual level. The volume contains microanalytical simulation models designed for policy implementation and evaluation, multilevel simulation methods designed for detecting emergent phenomena, dynamical game theory applications, the use of cellular automata to explain the emergence of structure in social systems, and multi-agent models using the experience from distributed artificial intelligence applied to special phenomena. The book collects the results of an international conference which brought together social scientists and computer scientists both engaged in a wide range of simulation approaches for the first time.

The articles collected in this volume have two features incommon: they wantto integrate economics, demography andgeography, and they want to overcome the stationary approachin modelling in favour of a dynamic one. The book issubdivided into three parts, where Part I is focussing oneconomic evolution, Part II on geographical development andPart III is related to demographic change. The presentvolume aims at providing a new look at this triangle in viewof the classical background of discussions by introducingnew research ideas focussing in nonlinear dynamics andstochastic modelling. Thus the main purpose of this book isto make a contribution to the interdisciplinary work neededto integrate the effortsbetween these three research fieldsand to serve as a research source in demonstrating thecurrent state of art in dynamic modelling.The book isaddressed to social scientists in general, andthose in particular with a background in economics,geographics and demographics. It should also be of interestto mathematicians, physicists, and systems analystsinterested in model building and applications of nonlineardynamics.

The use of computer simulations to study social phenomena has grown rapidly during the last few years. Many social scientists from the fields of economics, sociology, psychology and other disciplines now use computer simulations to study a wide range of social phenomena. The availability of powerful personal computers, the development of multidisciplinary approaches and the use of artificial intelligence models have all contributed to this development. The benefits of using computer simulations in the social sciences are obvious. This holds true for the use of simulations as tools for theory building and for its implementation as a tool for sensitivity analysis and parameter optimization in application-oriented models. In both, simulation provides powerful tools for the study of complex social systems, especially for dynamic and multi-agent social systems in which mathematical tractability is often impossible. The graphical display of simulation output renders it user friendly to many social scientists that lack sufficient familiarity with the language of mathematics. The present volume aims to contribute in four directions: (1) To examine theoretical and methodological issues related to the application of simulations in the social sciences. By this we wish to promote the objective of designing a unified, user-friendly, simulation toolkit which could be applied to diverse social problems. While no claim is made that this objective has been met, the theoretical issues treated in Part 1 of this volume are a contribution towards this objective.

This book gives an overview of the state of the art in five different approaches to social science simulation on the individual level. The volume contains microanalytical simulation models designed for policy implementation and evaluation, multilevel simulation methods designed for detecting emergent phenomena, dynamical game theory applications, the use of cellular automata to explain the emergence of structure in social systems, and multi-agent models using the experience from distributed artificial intelligence applied to special phenomena. The book collects the results of an international conference which brought together social scientists and computer scientists both engaged in a wide range of simulation approaches for the first time.

The use of computer simulations to study social phenomena has grown rapidly during the last few years. Many social scientists from the fields of economics, sociology, psychology and other disciplines now use computer simulations to study a wide range of social phenomena. The availability of powerful personal computers, the development of multidisciplinary approaches and the use of artificial intelligence models have all contributed to this development. The benefits of using computer simulations in the social sciences are obvious. This holds true for the use of simulations as tools for theory building and for its implementation as a tool for sensitivity analysis and parameter optimization in application-oriented models. In both, simulation provides powerful tools for the study of complex social systems, especially for dynamic and multi-agent social systems in which mathematical tractability is often impossible. The graphical display of simulation output renders it user friendly to many social scientists that lack sufficient familiarity with the language of mathematics. The present volume aims to contribute in four directions: (1) To examine theoretical and methodological issues related to the application of simulations in the social sciences. By this we wish to promote the objective of designing a unified, user-friendly, simulation toolkit which could be applied to diverse social problems. While no claim is made that this objective has been met, the theoretical issues treated in Part 1 of this volume are a contribution towards this objective.

Model building in the social sciences can increasingly rely on well elaborated formal theories. At the same time inexpensive large computational capacities are now available. Both make computer-based model building and simulation possible in social science, whose central aim is in particular an understanding of social dynamics. Such social dynamics refer to public opinion formation, partner choice, strategy decisions in social dilemma situations and much more. In the context of such modelling approaches, novel problems in philosophy of science arise which must be analysed - the main aim of this book. Interest in social simulation has recently been growing rapidly world- wide, mainly as a result of the increasing availability of powerful personal computers. The field has also been greatly influenced by developments in cellular automata theory (from mathematics) and in distributed artificial intelligence which provided tools readily applicable to social simulation. This book presents a number of modelling and simulation approaches and their relations to problems in philosophy of science. It addresses sociologists and other social scientists interested in formal modelling, mathematical sociology, and computer simulation as well as computer scientists interested in social science applications, and philosophers of social science.