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An applied spatial agent-based model of administrative boundaries using SEAL


  • Bernardo Alves Furtado
  • Isaque Daniel Eberhardt Rocha


This paper extends and adapts an existing abstract model into an empirical metropolitan region in Brazil. The model - named SEAL: a Spatial Economic Agent-based Lab - comprehends a framework to enable public policy ex-ante analysis. The aim of the model is to use official data and municipalities spatial boundaries to allow for policy experimentation. The current version considers three markets: housing, labor and goods. Families' members age, consume, join the labor market and trade houses. A single consumption tax is collected by municipalities that invest back into quality of life improvements. We test whether a single metropolitan government - which is an aggregation of municipalities - would be in the best interest of its citizens. Preliminary results for 20 simulation runs indicate that it may be the case. Future developments include improving performance to enable running of higher percentage of the population and a number of runs that make the model more robust.

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  • Bernardo Alves Furtado & Isaque Daniel Eberhardt Rocha, 2017. "An applied spatial agent-based model of administrative boundaries using SEAL," Papers 1702.03226,, revised Mar 2017.
  • Handle: RePEc:arx:papers:1702.03226

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    References listed on IDEAS

    1. Edoardo Gaffeo & Domenico Delli Gatti & Saul Desiderio & Mauro Gallegati, 2008. "Adaptive Microfoundations for Emergent Macroeconomics," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 34(4), pages 441-463.
    2. Lengnick, Matthias, 2013. "Agent-based macroeconomics: A baseline model," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 102-120.
    3. repec:spo:wpecon:info:hdl:2441/eu4vqp9ompqllr09j0h130d0n is not listed on IDEAS
    4. Bernardo Alves Furtado & Isaque Daniel Rocha Eberhardt, 2016. "A Simple Agent-Based Spatial Model of the Economy: Tools for Policy," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-12.
    5. Dosi, Giovanni & Fagiolo, Giorgio & Napoletano, Mauro & Roventini, Andrea, 2013. "Income distribution, credit and fiscal policies in an agent-based Keynesian model," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1598-1625.
    6. David Colander & Roland Kupers, 2014. "Complexity and the Art of Public Policy: Solving Society’s Problems from the Bottom Up," Economics Books, Princeton University Press, edition 1, number 10207.
    7. Tatiana Filatova & Dawn C. Parker & Anne van der Veen, 2009. "Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-3.
    8. repec:sae:urbstu:v:54:y:2017:i:13:p:3085-3100 is not listed on IDEAS
    9. Bernardo Alves Furtado & Isaque Daniel Rocha Eberhardt & Alexandre Messa, 2016. "SEAL's operating manual: a Spatially-bounded Economic Agent-based Lab," Papers 1609.03996,
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