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This monograph presents urban simulation methods that help in better understanding urban dynamics. Over historical times, cities have progressively absorbed a larger part of human population and will concentrate three quarters of humankind before the end of the century. This "urban transition" that has totally transformed the way we inhabit the planet is globally understood in its socio-economic rationales but is less frequently questioned as a spatio-temporal process. However, the cities, because they are intrinsically linked in a game of competition for resources and development, self organize in "systems of cities" where their future becomes more and more interdependent. The high frequency and intensity of interactions between cities explain that urban systems all over the world exhibit large similarities in their hierarchical and functional structure and rather regular dynamics. They are complex systems whose emergence, structure and further evolution are widely governed by the multiple kinds of interaction that link the various actors and institutions investing in cities their efforts, capital, knowledge and intelligence. Simulation models that reconstruct this dynamics may help in better understanding it and exploring future plausible evolutions of urban systems. This would provide better insight about how societies can manage the ecological transition at local, regional and global scales. The author has developed a series of instruments that greatly improve the techniques of validation for such models of social sciences that can be submitted to many applications in a variety of geographical situations. Examples are given for several BRICS countries, Europe and United States. The target audience primarily comprises research experts in the field of urban dynamics, but the book may also be beneficial for graduate students.

The use of simulation modelling techniques in studies of technological innovation dates back to Nelson and Winter's 1982 book, An Evolutionary Theory of Economic Change. Four main issues are identified in reviewing the key contributions in this burgeoning literature. First, a key driver in the construction of computer simulations has been the desire to develop theoretical models capable of dealing with the complex phenomena characteristic of technological innovation. Second, no single model captures all of the dimensions and stylized facts of innovative learning. The article develops a taxonomy that distinguishes between these dimensions and clarifies the different perspectives underpinning the contributions made by mainstream economists and nonmainstream, neo-Schumpeterian economists. Third, the simulation models are heavily influenced by the research questions of these different schools of thought. Finally, attention is drawn to the difference between learning and adaptation within a static environment and within a dynamic environment in which the introduction of new artifacts and patterns of behavior changes the selective pressure faced by agents.

For the broad understanding of sustainable development, items such as seasonal change, timing of restrained harvesting and the rate of regeneration of natural resources, as well as the theories of population growth are crucial. Similarly, in the world of computing as a whole and computing simulation in particular, three important components of models are theory, data and program. A model for global sustainability should include sub‐models for different resources and consumers in the ecological system. In this paper, a sample of sub‐models relating to areas such as human population growth, water, soil and land, greenhouse gases and CO2, conservation, forests and harvesting are listed, combined with other models such as pollution, waste treatment and ozone levels. The complexity of such simulations and their relevance to the current debate on sustainability are discussed.