The Economics of Complex Spatial Systems

Preface. Uncertainty and Complexity. Towards a science of uncertainty. Dynamic systems in perspective. Pathways to nonlinear dynamic modelling. Dissipative structures and synergetics. The science of complexity in the space-economy. Structure of Book. Nonlinear Dynamics in Spatial Systems: The Relevance of Chaos and Asymmetric Behaviour. Nonlinear modelling: exploring the scene. Pathways to nonlinearities and chaos. Modelling chaos phenomena. Chaos theory and asymmetries in economics. Analysis of chaos in regional economics. Introduction. Urban systems. Transport systems. Migration systems. Industrial/production systems. General comments. Concluding remarks. Evolutionary Approaches to Spatial-Economic Systems: The Relevance of Ecologically-Based Models. Introduction. Connections between ecology and economics. Ecologically-based models. The May model. The Lotka-Volterra model. Niche theory as unifying framework. Niche concepts. Formalization of the niche concept. Systems evolution by means of niche chains. Introduction. Evolution of self-organizing systems. Analysis of a two-dimensional niche system. Introduction. A niche model in continuous form. A niche model in discrete form. A methodological view. Simulation experiments: illustration of a two-dimensional niche system. A simple interurban transport model. Numerical experiments: the case of a 'dominance' competition system. Numerical experiments: the case of 'dominance' symbiosis system. An economic interpretation of niches. Introduction. Simulation experiments. Simulation experiments: a three-dimensional niche system. Introduction. Simulation experiments. A niche model for the evolution of cities. Concluding remarks. Complex Synergetics: Spatial Growth and Diffusion Processes. Space-time aspects of growth and innovation diffusion. Modelling innovation diffusion in a space-time context. Modelling growth and diffusion processes: a nested synergetic approach. Introduction to dynamic diffusion and synergy. Presentation of a growth diffusion model. Simulation experiments for a synergetic dynamic model for growth and innovation diffusion. Introduction. Case 1: two areas (core/periphery). Simulation experiments for three-dimensional spatial dynamics. Introduction. Case 2: three areas. Conclusions and future research directions. Complexity and Connnectivity in Multi-Layer Spatial-Economic Systems. Introduction. Multi-layer niche structures: an alternative view. A hierarchical evolutionary approach to interacting spatial systems. A multi-layer economic-environmental model for spatial competition. Introduction. A two-layer model with three competing modes. Simulation experiments for the modal choice model. Concluding remarks. A multi-layer complex modal choice model with dynamic growth rates. Introduction. Stable behaviour. Cyclical/irregular behaviour. Modelling spatial complexity and (in)stability. Connectivity, networks and complexity in spatial-economic growth. Connectivity in space-time systems. A simple spatial-economic growth model. Stability and complexity in a simple spatial network model. Conclusion and future research directions. The Economics of Network Synergy and Spatial Evolution. Network synergy: an introduction. Network synergy and performance. An economic performance analysis of network links. A single mode analysis for network behaviour. A multi-modal analysis for network behaviour. Towards dynamic network synergy models in an evolutionary perspective. Introduction. Prey-predator relationships between the input factors. Symbiotic relationships between the input factors. Ecological relationships between the input factors and output. Competitive relationships between input factors. Conclusions. Annex 6A typologies of network performance attractors. Neural Networks for the Analysis of Complex Spatial Systems. Introduction to neurocomputing. A brief history of neural network analysis. The structure of neural networks. Micro-structure. Meso-structure. Macro-structure. The learning phase in neural networks. Supervisor learning. Unsupervisor learning. Neural networks and the social sciences. Applications of neural networks in spatial economics and transportation science. Introduction. Neural networks in spatial economic systems. Neural networks in transport systems. Concluding remarks. Complex Spatial Interaction Systems: Empirical Applications by Means of Neural Networks and Logit Models. Introduction. The models adopted. Introduction. The logit approach. The feedforward neural net model. Statistical indicators. Neural networks and logit models: applications to passenger transport flows in Italy. Introduction. Spatial forecasting results. Impacts of transport time. Concluding remarks. Neural networks and logit models: applications to European freight transport flows. Introduction. Spatial forecasting results. Sensitivity analysis by means of social costs scenarios. Conclusions. The Complex Dynamics of Spatial-Economic Systems Revisited. Nonlinear pathways: reprise. Nonlinear pathways to the future. References. Acknowledgements. Index.