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Our world is continuously changing as new technologies are developed, demographic patterns evolve and new social and political configurations emerge. However, most people would agree that, not unlike the time of the Industrial Revolution, the rate of change has accelerated over the last few decades. It is certainly the case of many economic activities, where the trend towards globalization, supported by a faster diffusion of knowledge and information, economies of scale, the opening of borders and improved transport networks, has induced a spatial redistribution of production and ownership. Until recently, the concomitant economic growth eased the way towards the necessary reorganization, but, at the same time, created additional environmental and transport problems. This phenomenon is pervasive all around the globe, but it particularly affects the European continent, which is still fragmented by administrative, cultural and linguistic differences, national boundaries and rivalries. In this context, the slow, but persistent drive towards market integration and deregulation makes Europe an extraordinary laboratory for politicians, economic researchers and especially for transport analysts.

For many decades scholars from various disciplines have been intrigued by the question whether there are unifying principles or models that have a validity in different disciplines. The building of such analytical frameworks bridging the gaps between scientific traditions is a very ambitious task and has not been very successful up till now. In the past - in a static context - several such principles have been defined and advocated at the edge of the natural sciences on the one hand and social sciences (in particular, economics and geography) on the other hand, mainly based on the paradigm of'social physics'. Some important contributions to the integration of the spatial systems sciences and physics can be found in gravity theory and entropy theory, which have formed the comer stones of interaction models in space. This book is about spatial interaction models. It describes the origin, the history and the correspondence of such models from a'social physics'perspective. It is emphasized that such models need a clear behavioural underpinning as a sine qua non for a valid use in spatial systems analysis. This view also explains the use of micro-based disaggregate choice models as a tool for analyzing spatial systems. This is mainly analyzed in Part A of this book.

Is our world more dynamic than it used to be in the past? Have phenomena in the social science field become unpredictable? Are chaotic events nowadays occurring more frequently than in the past? Such questions are often raised in popular debates on nonlinear evolution and self-organizing systems. At the same time, many scientists are also raising various intruiging methodological issues. Is it possible to separate deterministic chaos from random disturbances if their trajectories are (almost) similar? Is prediction still possible in a world of chaos (Poincare)? Is it possible to distinguish specification errors from measurement errors in a nonlinear dynamic model? Is evolution a random process? The list of such questions can easily be extended with dozens of others. But despite the myriad of questions on problems of nonlinear evolution, one common trait is evident: in both the natural and the social sciences we are still groping in the dark in areas which are par excellence promising hunting grounds for exploratory and exploratory research, viz. structural grounds in an uncertain nonlinear world. The present book aims at offering a collection of refreshing contributions to the above research issues by focusing attention, in particular on nonlinear dynamic evolution in space at the Netherlands Institute for Advanced Study (NIAS) in Wassenaar, the Netherlands. The Institute has to be thanked for its hospitality and support, reflected inter alia in a workshop at which several of the papers included in this book were discussed.

With the dawn of the twenty-first century comes the awareness that current rapid political-economic-social and technological transformations will affect our of living, by producing new forms of information, communications, common way market, work-style and leisure. In this context, human behaviour will certainly change its'fixed'parameters. It is likely that the relationships between internal structures and external influences, between individual components and collective behaviour, as well as between multi-scale networks and interrelated dynamics, will show spatio-temporal patterns which will be difficult to predict by means of our usual tools. As a consequence, academic research is increasingly being required to play an active role in addressing new ways of understanding and forecasting the sets of interacting structures, ranging from the technical to the organizational, and from the social to the economic and political levels, while at the same time incorporating concerns about the'new'economy, environment, society, information and technology. It is now evident that social science - especially spatial and economic scienc- needs innovative'paths', together with continuous cross-fertilization among the many disciplines involved. In order to investigate these intriguing perspectives, we seem to have embarked on an era of methodological reflections - rather than developing strong theoretical foundations. This volume aims to provide an overview of these new insights and frontiers for theoretical/methodological studies and research applications in the space-economy.

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.

One aspect of the new economy is a transition to a networked society, and the emergence of a highly interconnected, interdependent and complex system of networks to move people, goods and information. An example of this is the in creasing reliance of networked systems (e. g., air transportation networks, electric power grid, maritime transport, etc.) on telecommunications and information in frastructure. Many of the networks that evolved today have an added complexity in that they have both a spatial structure – i. e., they are located in physical space but also an a spatial dimension brought on largely by their dependence on infor mation technology. They are also often just one component of a larger system of geographically integrated and overlapping networks operating at different spatial levels. An understanding of these complexities is imperative for the design of plans and policies that can be used to optimize the efficiency, performance and safety of transportation, telecommunications and other networked systems. In one sense, technological advances along with economic forces that encourage the clustering of activities in space to reduce transaction costs have led to more efficient network structures. At the same time the very properties that make these networks more ef ficient have also put them at a greater risk for becoming disconnected or signifi cantly disruptedwh en super connected nodes are removed either intentionally or through a targeted attack.