The articles collected in this volume have two features in common: they wantto integrate economics, demography and geography, and they want to overcome the stationary approach in modelling in favour of a dynamic one. The book is subdivided into three parts, where Part I is focussing on economic evolution, Part II on geographical development and Part III is related to demographic change. The present volume aims at providing a new look at this triangle in view of the classical background of discussions by introducing new research ideas focussing in nonlinear dynamics and stochastic modelling. Thus the main purpose of this book is to make a contribution to the interdisciplinary work needed to integrate the effortsbetween these three research fields and to serve as a research source in demonstrating the current state of art in dynamic modelling. The book isaddressed to social scientists in general, and those in particular with a background in economics, geographics and demographics. It should also be of interest to mathematicians, physicists, and systems analysts interested in model building and applications of nonlinear dynamics.

Hersteller:
Springer Verlag GmbH
juergen.hartmann@springer.com
Tiergartenstr. 17
DE 69121 Heidelberg

InhaltsangabeI Formal Models in Economics.- 1 A chaotic process with slow feed back: The case of business cycles.- 1.1 A first model.- 1.1.1 Investments.- 1.1.2 Consumption.- 1.2 The cubic iterative map.- 1.2.1 Fixed points, cycles and chaos.- 1.2.2 Formal analysis of chaotic dynamics.- 1.2.3 Symbolic dynamics.- 1.3 "Brownian random walk".- 1.4 Digression on order and disorder.- 1.5 The general model.- 1.5.1 Relaxation cycles.- 1.5.2 Other cycles.- 1.5.3 The Slow Feed Back.- 1.6 Conclusion.- 2 Nonlinear Interactions in the Economy.- 2.1 Introduction.- 2.2 The Long Wave Model.- 2.3 Mode-Locking and Chaos.- 2.4 Conclusion.- 3 Fast and Slow Processes of Economic Evolution.- 3.1 Introduction and Background.- 3.2 The Problems of Economic Development Theory.- 3.3 Synergetic Development Economics - Some Basic Concepts.- 3.4 The Arena.- 3.5 Rules of the Game.- 3.6 Networks.- 3.7 Knowledge As Networks and Knowledge On Networks.- 3.8 Communication and Creativity - some Historical Evidence.- 3.9 Creativity and Communications - Econometric Results.- 3.10 The Inverted Explanation.- 3.11 The Destruction of the Industrial Society.- 3.12 The New Economic Structure.- 4 A stochastic model of technological evolution.- 4.1 Introduction.- 4.2 A Substitution Model.- 4.3 Application of a general evolutionary model to technological change.- 4.4 Discussion.- 5 Evolution of Production Processes.- 5.1 Introduction.- 5.2 Basic Assumptions.- 5.3 Formalization.- 5.4 Chernenko's Results.- 5.5 An alternative macro model.- 5.6 Simulation results.- 5.7 Modeling evolution on the individual level.- 5.7.1 Simulation run with total extinction.- 5.7.2 Simulation run without extinction.- 5.8 Conclusions.- 6 Innovation Diffusion through Schumpeterian Competition.- 6.1 Introduction: From "Homo Economicus" to "Homo Socialis": Innovation diffusion as a collective socio-ecological dynamic choice process.- 6.2 Analytical basis of Schumpeterian Competition: Collective choice and relative socio-spatial dynamics.- 6.3 Explicit analytical presentation of the innovation diffusion dynamics: Dynamic choice models.- 6.4 Intervention of an active environment: Generation of innovation adoption niches.- 6.5 Temporal innovation diffusion process.- 6.5.1 Qualitative analysis of the Schumpeter competition cycles for Clusters of competitive innovations.- 6.5.2 Variational principle of meso-level collective choice behaviour.- 6.6 Concluding Remark.- 7 Nonlinear Threshold Dynamics: Further Examples for Chaos in Social Sciences.- 7.1 Introduction.- 7.2 A Short Course into Chaos.- 7.3 How Addictive Behaviour and Threshold Adjustment May Imply Chaos.- 7.4 How Asymmetric Investment Behaviour of Two Competing Firms Generates Chaos.- 7.5 Concluding Remarks.- II Formal Models in Geography.- 8 Geography Physics and Synergetics.- 8.1 Introduction.- 8.2 Models of geographical interactions.- 8.2.1 Polarization and gravitation.- 8.2.2 Reformulations of the gravity model.- 8.2.3 The entropy maximizing approach.- 8.2.4 About men and particles.- 8.3 Models of geographical structures.- 8.3.1 The relativity of geographical space.- 8.3.2 Fractality of geographical space.- 8.3.3 Space-time convergence.- 8.3.4 The example of urban hierarchies.- 8.3.5 Processes and geographical forms.- 8.4 Conclusion.- 9 Chaotic Behaviour in Spatial Systems and Forecasting.- 9.1 Introduction.- 9.2 An Example for Chaotic Evolution: Migratory Systems.- 9.2.1 A Numerical Simulation.- 9.3 Estimation of Trend Parameters.- 9.4 The Estimation Procedure.- 9.5 Forecasting for Systems with Chaotic Evolution.- 9.5.1 Step I: Confidence Limits on Model Parameters by Monte Carlo Estimation.- 9.5.2 Step II: Monte Carlo Simulation of Systems Trajectories.- 10 Model Identification for Estimating Missing Values in Space-Time Data Series: Monthly Inflation in the US Urban System, 1977-1990.- 10.1 Introduction.- 10.2 Background.- 10.3 Update of individual urban area ARIMA models.- 10.4 Jackknife results for New York and Los Angeles.- 10.5 Transfer function i