Denise Pumain

SIMPOP: A Multiagent System for the Study of Urbanism

SIMPOP is a knowledge-based simulation system for the description of the evolution of settlement patterns over long time periods. Rules and parameters are introduced into a multiagent systems formalism where each settlement is considered as a separate entity interacting with the others and transforming itself. The rules may allow for the simulation of the ‘urban transition’ from a set of homogeneous, agriculture-oriented, and scattered villages into a complex system of functionally diverse, competing, and hierarchised urban settlements. In this paper we show how several modifications of rules and parameters alter further the spatial and hierarchical structure of the simulated urban system.

Hierarchy in natural and social sciences

Hierarchy: A Short History of a Word in Western Thought.- Biological and Ecological Systems Hierarchical Organisation.- Size, Scale and the Boat Race Conceptions, Connections and Misconceptions.- Hierarchy, Complexity, Society.- Hierarchy in Lexical Organisation of Natural Languages.- Hierarchy in Cities and City Systems.- Alternative Explanations of Hierarchical Differentiation in Urban Systems.- Conclusion.

Complexity perspectives in innovation and social change

Introduction.- Section 1: From biology to society.- Ch 1: Lane, Maxfield, Read and van der Leeuw, From population to organization thinking.- Ch 2: Read, Lane and van der Leeuw, The innovation innovation.- Ch 3: van der Leeuw, Lane and Read, The long-term evolution of social organization.- Ch 4: Ginzburg, Biological metaphors in economics: Natural selection and competition.- Ch 5: White, Innovation in the context of networks, hierarchy and social cohesion.- Section 2: Innovation and urban systems.- Ch 6: Bretagnolle, Pumain, The organization of urban systems.- Ch 7: Bettancourt, Lobo and West, The self similarity of human social organization in cities.- Ch 8: Pumain, Paulus and Vacchiani-Marcuzzo, Innovation cycles and urban dynamics.- Section 3: Innovation and market systems.- Ch 9: Lane and Maxfield, Building a new market system.- Ch 10: Rossi, Bertossi, Gurisatti and Sovieni, Incorporating a new technology into agent-artifact space: The case of control system automation in Europe.- Ch 11: Russo and Rossi, Innovation policies: Levels and levers.- Section 4: Modeling innovation and social change.- Ch 12: Pumain, Sanders, Bretagnolle, Glisse, and Mathian, The future of urban systems: exploratory models.- Ch 13: Serra, Villani and Lane, Modeling innovation.- Ch 14: Ferrari, Read, van der Leeuw, An agent based model of information flows in social dynamics.- Ch 15: Villani, Bonacini, Ferrari and Serra, An agent based model of exaptive processes.- Ch 16: Helbing, Kuhnert, Lammer, Johannsen, Gelsen, Ammoser and West, Power laws in urban supply networks, social systems and dense pedestrian.- Ch 17: Knappett et al., Using statistical physics to understand relational space: A case study from Mediterranean.- Conclusion.- List of contributors

City size distributions and metropolisation

Many controversial questions about the shape and evolution of city size distributions can be solved if reliable, large and comparable set of data are used for several countries. We provide new empirical evidence by using the large data base ‘Geopolis’, which has strictly comparable figures for all towns and cities of the world over 10,000 inhabitants between 1950 and 1990. A Pareto model is used for identifying as metropolises one or a few large cities for each national urban system. From those data, two empirical power laws are established, linking the size of the metropolises to the size of their national urban system. The first is a transversal law: for a set of countries at a given date, the share of population concentrated in metropolises tends to decrease when larger countries are considered. The second law, which is longitudinal, shows that metropolises in the past have grown in a systematic way more rapidly than the rest of their urban system, invalidating Gibrats urban growth model. Such empirical regularities could help for predicting the future of nowadays observed metropolisation trends.

Simulating Urban Networks through Multiscalar Space-Time Dynamics: Europe and the United States, 17th-20th Centuries

Simpop2 is a generic multi-agent model designed for simulating any system of cities. From an evolutionary theory built upon the observation of networks of cities in different parts of the world and over long time-periods, it has been possible to identify stylised facts that characterise their main features and properties. This paper presents data-oriented simulations of two kinds of system: in early settled countries (Europe, 1300—2000) and in countries more recently settled (the United States, 1650—2000). The model can simulate properly the general dynamics of urban systems, at different scales of observation (general configuration and trajectories of individual cities). The simulations help to identify some dynamic properties that are shared by both systems: a general growth trend and spatial expansion (produced through interurban competition which generates emulation towards innovation that explain the persistency of the hierarchical configuration); a dramatic increase of contrasts in city sizes since the first industrial revolution linked to the increase of communication speed; and, a differentiation of urban economic functions produced through interactions between cities and innovation cycles, as industrial revolution. The model also puts forward the necessary integration of a new urban function in the model, which represents the early emergence of global cities. Yet, beyond these similarities in the evolution of all urban systems, when they are fully integrated, the model also measures to what extent the observed peculiarities in their contemporary spatial and functional configuration depend on differences in the early space-filling process between the two kinds of system.

Built-Up Encroachment and the Urban Field: A Comparison of Forty European Cities

We define the urban field as the spatial organisation of urban densities according to decreasing gradients from centre to periphery. This urban field can be estimated from the encroachment of built-up areas. The CORINE Land Cover database enables the measurement of the gradient values for the spatial distribution of built-up areas in European cities. Instead of the exponential or power functions, which usually provide the best fit for the distributions of population densities, we find that two linear functions strongly differentiating a central and a peripheral gradient provide the best fit for built-up surfaces. The comparison between the 1990 and 2000 CORINE images demonstrates a convergence in the trend of urban spread between Northern and Southern European cities. However, it is still difficult from the data to decide which of the models of urban field is winning: will the steeper central gradient become diluted into the less dense periphery, or are the closer fringes of the central parts becoming integrated into the ordered pattern of the urban central agglomerations?

Alternative explanations of hierarchical differentiation in urban systems

The hierarchical differentiation of urban systems has been noticed for a long time and various explanations have been suggested. Among them: an intentional functional organisation for controlling a territory; the application of a spatial economic equilibrium principle; a “purely” random growth process; the statistical addition of Pareto-like elementary phenomena; self-organisation or co-evolution of competing subsystems, without constraint or under space-time optimisation principle. We review these explanations and related methods of analysis, trying to assess their relevance and exploring the possible similarities between urban dynamics and other types of hierarchical complex systems.

Knowledge-based simulation of settlement systems

A knowledge-based approach for estimating and predicting the population growth of a town according to its relative position in a settlement system is presented. This approach allows the explicit representation of hypotheses regarding the urban development process. It makes it possible to systematically test the effect of each of these hypotheses on the results. It also makes the comparison and combination of existing models easier. As a result, the tool we have developed is a combination of elementary models. This tool simulates the spatial effects of urban growth at different scales, from the local integration of peri-urban communes into urban areas towards the transnational competition between large metropolitan areas.

Innovation Cycles and Urban Dynamics

Urban systems are adaptive systems, in the sense that they continuously renew their structure while fulfilling very different functionalities. Many examples of adaptation in city size, spacing, and their social and functional components have been given in Chapter 6 of this book. There, we defined the structure of urban systems as a rather persistent configuration of relative and relational properties differentiating cities, which, over long periods, maintains the same cities in categories of size or socio-economic specialization. The content of these categories changes in terms of the quantitative thresholds or the qualitative attributes used for defining them at each date, but they retain the same meaning in terms of the relative situation of cities in the urban systems. Hierarchical differentiation and socio-economic specialization are the major structural features shared by all city systems. On the scale of national, continental, or world urban systems, the structures result mainly from self-organization processes, even if intentional decisions made by individuals or institutions (for instance, the choice of Brussels for the seat of many European Union institutions) may sometimes influence the general configuration.

Urban research and complexity

During the last two decades, a number of interesting innovations have appeared in the field of urban research. New paradigms such as the dynamics of open systems, self-organisation, synergetics, chaos and evolution theory, have been recognised as conveying fruitful analogies for urban theory. New types of modelling, such as sets of nonlinear differential equations for spatial systems, cellular automata, multi-agents models, fractal growth, neural networks and evolutionary models have been investigated. By reviewing the orientations of contemporary urban research, it is possible to recognise among these new urban models several types of strategy which are useful for dealing with the complexity of urban systems.