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Lena Sanders, Hélène Mathian UMR Géographie-cités

Multi-agent systems for modelling the dynamics of interacting cities: the case of Europe 1950-2050. Lena Sanders, Hélène Mathian UMR Géographie-cités CNRS – University Paris 1 – University Paris 7. Dresden, ECCS’07, 1-6 oct 2007. Scientific background. European Projects:

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Lena Sanders, Hélène Mathian UMR Géographie-cités

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  1. Multi-agent systems for modelling the dynamics of interacting cities: the case of Europe 1950-2050 Lena Sanders, Hélène Mathian UMR Géographie-cités CNRS – University Paris 1 – University Paris 7 Dresden, ECCS’07, 1-6 oct 2007

  2. Scientific background • European Projects: • ISCOM (dir: D. Lane) • TiGrESS (dir: N. Winder) SimPop group: A. Bretagnolle J-M Favaro B. Glisse (LIP6) T. Louail H. Mathian D. Pumain L. Sanders C. Vacchiani UMR Géographie-cités www.parisgeo.cnrs.fr/simpop/

  3. From human-agent to city-agent Source: Ferber In most applications in social sciences: the agent = an individual (farmer, consumer..) or a household In SimPop, EUROSIM Agent= a town, a city

  4. The city-agent • Underlying hypotheses: - the driving force of a city’s dynamics: the interactions which link the city to the other ones rather than the specific features and events occurring within the city itself, even if the two are not independent - the role of the context: relative location, size, wealth, specialization of a city is more determining for understanding the way exchanges are functioning than the more specific mechanism underlying an economic actor’s behaviour. • the city-agent does not : - represent an individual actor (as a mayor), - neither an average (there is such a diversity of agents that an average has no meaning), - neither a representative actor in the classical sense. It represents rather the resultant of a diversity of behaviours. The city is seen as an indivisible collective entityrather than a simple receptacle

  5. Aim of the EUROSIM model • Use simulation in orderto explore the past and futureevolution of European cities during a period of 100 years: 1950 to 2050 • Test scenarios according to different policies: - demography: opened or closed Europe in matter of immigration - economy: presence or absence of barriers between European blocks (East and West)

  6. The EUROSIM model: 2 types of agents

  7. Involved urban theories • Central place theory: a hierarchy of urban functions associated to different ranges • Economic base theory: the highly specialized exporting activities are the driving one • Agglomeration economies • Innovation cycles • path dependence

  8. One iteration (one year) in the model Initialization of the variables (observed 1950) Updating of the economical and demographical situation Network building t t+1: Updating of the variables The market Information exchanges between cities Evaluation of change Wealth, Population, Labour composition * Transactions * Stop criterion: no more demand for remaining supply, no more supply for remaining demand

  9. Main variables - State variables: Population, Wealth, Labor force by sector - Conjonctural variables (exogenous): . Demographical: global growth rate • Observed for 1950-2000 • Forecasts of IIASA for 2000-2050 . Economical: Productivity, Demand, Added value, by sector of activity - Intermediate variables: size of the networks, unsold goods, unsatisfied demand…

  10. Key parameters: 3 families • the network building k network size criterium c stability criterium: % of valuable customer • the response of the cities to the return of the market e speed of adjustment of the labor force us, wg sensitivity of growth to unsold goods, resp. gain or lost of wealth • the barriers on the international exchanges f barrier effects of boundaries

  11. Computing of change Hyp: economical success drives demographic dynamics Change is computed at the level of each city according to the results of the market exchanges • wealth: change as a direct consequence of transfer of wealth corresponding to the effective transactions • Population change: 2 mechanisms - direct, as a response to the balance of exchange: . increase of the labor force of a sector if there is an unsatisfied demand . decrease of the labor force of a sector if there are unsold goods - indirect : growth rate of the city dependent on the balance of exchange (increased if there is a gain of wealth, decreased if there is a lost of wealth)

  12. Network buildingfrom potential to effective transactions • 1. the potential networks of exchanges of each city possible exchanges - spatial and/or territorial proximity: - selectivity • 2. The networks of information exchanges • 3. The networks of effective transactions

  13. Different specializations with associated ranges and selectivity criteria

  14. Potential networks of exchange : the Central 2 function : The potential of exchange of a city depends on the density of the local urban network

  15. Evolving ranges change the local context: an example

  16. Potential networks of exchange : the Manufacturing2 function

  17. Building the networks of information exchanges for a city “i” for a product “S”: a 2 steps random selection potential network Valuable customers from preceding periods (parameter c) Random selection according to selectivity criteria associated to « S » Random selection until: demand of selected cities < k . supply of city « i »

  18. The two roles of the city-agent • For a given time period, one network: • per product • per city • Consequence: Overlapping of sets of networks • competition

  19. 3rd step: the networks of transactions The example of Warsawa: 3 specializations

  20. Size of the networks are evolving through timeExamples of Manchester and Glasgow

  21. Zoom on the simulated transactions of Manchester and Glasgow with 4 cities of their networks of exchange (manufacturing2) A diversity of situations

  22. Running of the model: three stages • 1. Testing the sensitivity of the model : • To events • To variations in the conjonctural variables • To initial conditions • To variations in the values of the key parameters • 2. Calibrating the model using the period 1950-1990 • 3. Testing scenarios on the evolution of the European urban system by 2050

  23. Sensitivity testing Effect of a higher speed of adjustment of the labor force on the evolution of the sector of finance

  24. Scenarios: The intervals of possible futures Barrier (key parameter) Demographic features Conjonctural variables

  25. Outputs according to 2 extreme scenarios: Stability at the macro-level (rank-size distribution)

  26. Outputs according to the four scenariosTotal urban population at the level of the three geographical blocks

  27. Diversity of responses to the different scenarios at city level

  28. Barriers affect differently the cities: an example Exchanges maintain in the scenario with barriers, and fall in the scenario without

  29. barriers no barriers

  30. Concluding remarks on the Interest of the MAS formalization compared to a classical aggregate model of cities’ dynamics • Size and composition of the exchange networks are not pre-determined • Combination of networking principles with spatial proximity principles • Flexibility for a multi-scalar approach • Presented scenarios: - exogenous demographic variables (global change) - parameter f • Future scenarios: change at the local level of the city (testing role of governance)

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