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

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

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

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 entity rather than a simple receptacle

Aim of the EUROSIM model  Use simulation in order to explore the past and future evolution 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)

The EUROSIM model: 2 types of agents

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

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

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

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

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)

Network building from 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

Different specializations with associated ranges and selectivity criteria

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

Evolving ranges change the local context: an example

Potential networks of exchange : the Manufacturing2 function

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

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

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

Size of the networks are evolving through time Examples of Manchester and Glasgow

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

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  3. Testing scenarios on the evolution of the European urban system by 2050

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

Scenarios: The intervals of possible futures Full barrierNo barrier effects Declining Europe with no immigration LF0LF1 Open and dynamic Europe HF0HF1 Barrier (key parameter) Demographic features Conjonctural variables

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

Outputs according to the four scenarios Total urban population at the level of the three geographical blocks

Diversity of responses to the different scenarios at city level

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

barriersno barriers

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)