IDEAS home Printed from https://ideas.repec.org/p/wiw/wiwrsa/ersa15p1044.html
   My bibliography  Save this paper

Showcasing a Domain Specific Language for Spatial Simulation Scenarios with case studies

Author

Listed:
  • Luís de Sousa
  • Alberto Rodrigues da Silva

Abstract

Techniques such as cellular automata and agent-based modelling have long been used to capture and simulate the temporal dynamics of spatial information. Tools commonly employed to implement spatial simulation models include code libraries and pre-compiled models; the former require advanced programming skills while the latter impose relevant constraints on application scope. Previous attempts to produce domain specific languages in the field have invariably resulted in new textual programming languages (e.g. SELES, NetLogo, Ocelet) that are platform specific and in some cases with weak GIS support and interoperability. The Domain Specific Language for Spatial Simulation Sce narios (DSL3S) is a Domain Specific Language (DSL) for spatial simulation in the GIS context. It tries to ease the development of simulation models through a Model-Driven Development (MDD) approach, whereby models are developed through the arrangement of graphical elements and their relationships, dispensing formal programming knowledge. These graphical models can then be translated into ready to run simulations through the application of a code generation infrastructure. This language has been implemented using the Model Driven Development (MDD) tools distributed with the Eclipse IDE. Model-Driven Development for Spatial Simulation Scenarios (MDD3S) is the resulting code generation infrastructure, that produces ready to run simulations from DSL3S models. The MDD3S framework currently relies on MASON, a modern Java library for spatial simulation. This option also guarantees interoperability with geographic data, namely through the GeoMASON extension. This framework was developed using the Eclipse MDD ad-ons Papyrus, for UML modelling, and Acceleo, for code generation. Both the DSL3S UML profile, the MDD3S framework and the example models presently in the public domain. This article reviews previous DSLs attempted in the field, noting the differences to DSL3S. It briefly describes the abstract syntax of the language and MDD3S, its implementation prototype, detailing the tools on which it relies. The usage of DSL3S is exemplified through a set of use cases that portrait its application to common spatial simulation scenarios. The MDD approach has proved to raise the level of abstraction at which development takes place, thus simplifying the communication between programmers and analysts, and other stakeholders lacking programming skills (Mohagheghi et al., 2013). MDD should also be applicable to other fields of simulation, allowing the development of complex models from a relatively simple graphical constructs. It may also be the foundation for a standard language in simulation, as the successful examples of SysML and ModelicaML in the field of Systems Engineering attest to.

Suggested Citation

  • Luís de Sousa & Alberto Rodrigues da Silva, 2015. "Showcasing a Domain Specific Language for Spatial Simulation Scenarios with case studies," ERSA conference papers ersa15p1044, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa15p1044
    as

    Download full text from publisher

    File URL: https://www-sre.wu.ac.at/ersa/ersaconfs/ersa15/e150825aFinal01044.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nelson Minar & Rogert Burkhart & Chris Langton & Manor Askenazi, 1996. "The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations," Working Papers 96-06-042, Santa Fe Institute.
    2. Degenne, P. & Lo Seen, D. & Parigot, D. & Forax, R. & Tran, A. & Ait Lahcen, A. & Curé, O. & Jeansoulin, R., 2009. "Design of a Domain Specific Language for modelling processes in landscapes," Ecological Modelling, Elsevier, vol. 220(24), pages 3527-3535.
    3. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David O'Sullivan & Mordechai Haklay, 2000. "Agent-Based Models and Individualism: Is the World Agent-Based?," Environment and Planning A, , vol. 32(8), pages 1409-1425, August.
    2. Tomas Klos, "undated". "Decentralized Interaction and Co-adaptation in the Repeated Prisoner's Dilemma," Computing in Economics and Finance 1997 88, Society for Computational Economics.
    3. Pietro Terna, 1998. "Simulation Tools for Social Scientists: Building Agent Based Models with SWARM," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(2), pages 1-4.
    4. repec:dgr:rugsom:97b33 is not listed on IDEAS
    5. Christoph Schlueter-Langdon, 2000. "Information Technology And The Vertical Organization Of Industry," Computing in Economics and Finance 2000 174, Society for Computational Economics.
    6. Eugenio Caverzasi & Antoine Godin, 2013. "Stock-flow Consistent Modeling through the Ages," Economics Working Paper Archive wp_745, Levy Economics Institute.
    7. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2015. "An agent based decentralized matching macroeconomic model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 305-332, October.
    8. Michael J. Radzicki, 2003. "Mr. Hamilton, Mr. Forrester, and a Foundation for Evolutionary Economics," Journal of Economic Issues, Taylor & Francis Journals, vol. 37(1), pages 133-173, March.
    9. Kazuya Yamamoto, 2015. "Mobilization, Flexibility of Identity, and Ethnic Cleavage," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-8.
    10. Dirk Helbing & Thomas U. Grund, 2013. "Editorial: Agent-Based Modeling And Techno-Social Systems," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 16(04n05), pages 1-3.
    11. Ross Richardson & Matteo G. Richiardi & Michael Wolfson, 2015. "We ran one billion agents. Scaling in simulation models," LABORatorio R. Revelli Working Papers Series 142, LABORatorio R. Revelli, Centre for Employment Studies.
    12. Gennaro Zezza & Michalis Nikiforos, 2017. "Stock-flow Consistent Macroeconomic Models: A Survey," EcoMod2017 10762, EcoMod.
    13. Cincotti, Silvano & Raberto, Marco & Teglio, Andrea, 2010. "Credit money and macroeconomic instability in the agent-based model and simulator Eurace," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-32.
    14. Khalil, Elias L., 2010. "The Bayesian fallacy: Distinguishing internal motivations and religious beliefs from other beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 75(2), pages 268-280, August.
    15. Jakub Bijak & Jason D. Hilton & Eric Silverman & Viet Dung Cao, 2013. "Reforging the Wedding Ring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(27), pages 729-766.
    16. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    17. Joshua M. Epstein, 2007. "Agent-Based Computational Models and Generative Social Science," Introductory Chapters, in: Generative Social Science Studies in Agent-Based Computational Modeling, Princeton University Press.
    18. Rich, Karl M. & Ross, R. Brent & Baker, A. Derek & Negassa, Asfaw, 2011. "Quantifying value chain analysis in the context of livestock systems in developing countries," Food Policy, Elsevier, vol. 36(2), pages 214-222, April.
    19. Ugo Merlone & Daren Sandbank & Ferenc Szidarovszky, 2013. "Equilibria analysis in social dilemma games with Skinnerian agents," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 12(2), pages 219-233, November.
    20. Laobing Zhang & Gabriele Landucci & Genserik Reniers & Nima Khakzad & Jianfeng Zhou, 2018. "DAMS: A Model to Assess Domino Effects by Using Agent‐Based Modeling and Simulation," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1585-1600, August.
    21. Günter Küppers & Johannes Lenhard, 2005. "Validation of Simulation: Patterns in the Social and Natural Sciences," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-3.

    More about this item

    Keywords

    Domain Specific Language; Spatial Simulation; UML Profile; Model-Driven Developm;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa15p1044. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gunther Maier (email available below). General contact details of provider: http://www.ersa.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.