IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v18y2012i1d10.1007_s10588-012-9110-5.html
   My bibliography  Save this article

Metamodels for role-driven agent-based modelling

Author

Listed:
  • Rubén Fuentes-Fernández

    (Universidad Complutense de Madrid)

  • Samer Hassan

    (Universidad Complutense de Madrid)

  • Juan Pavón

    (Universidad Complutense de Madrid)

  • José M. Galán

    (Universidad de Burgos)

  • Adolfo López-Paredes

    (Universidad de Valladolid)

Abstract

A major challenge in agent-based modelling is the management of the process to generate executable simulations from the initial conceptual models. This process is complex and usually involves several roles, which may raise communication problems due to the diverse backgrounds and perspectives of participants and the use of non-explicit knowledge. This situation demands a clear separation and precise definition of the multiple aspects of the process, in order to facilitate their understanding, grasp their relationships and develop them. This paper addresses this goal with a fine-step refinement process for information based on the use of domain-specific languages. It considers analysis contexts that include a particular theoretical framework, domain, type of problem and target platform. For a given context, the process formally defines modelling languages conceptually close to the different aspects relevant to it. It also defines mappings between concepts in those languages. Researchers develop simulations by specifying models with the languages, and share and refine information by using mappings between these models. This infrastructure provides guidance throughout the process and makes the information involved explicit. A case study of continuous double auctions illustrates the approach.

Suggested Citation

  • Rubén Fuentes-Fernández & Samer Hassan & Juan Pavón & José M. Galán & Adolfo López-Paredes, 2012. "Metamodels for role-driven agent-based modelling," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 91-112, March.
    • Handle: RePEc:spr:comaot:v:18:y:2012:i:1:d:10.1007_s10588-012-9110-5
    DOI: 10.1007/s10588-012-9110-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-012-9110-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10588-012-9110-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Matteo Richiardi & Roberto Leombruni & Nicole J. Saam & Michele Sonnessa, 2006. "A Common Protocol for Agent-Based Social Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(1), pages 1-15.
    2. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    3. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    4. José Manuel Galán & Luis R. Izquierdo & Segismundo S. Izquierdo & José Ignacio Santos & Ricardo del Olmo & Adolfo López-Paredes & Bruce Edmonds, 2009. "Errors and Artefacts in Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-1.
    5. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    6. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    7. Uri Wilensky & William Rand, 2007. "Making Models Match: Replicating an Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(4), pages 1-2.
    8. Smith, Vernon L, 1982. "Microeconomic Systems as an Experimental Science," American Economic Review, American Economic Association, vol. 72(5), pages 923-955, December.
    9. J. Gareth Polhill & Dawn C. Parker & Daniel Brown & Volker Grimm, 2008. "Using the ODD Protocol for Describing Three Agent-Based Social Simulation Models of Land-Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(2), pages 1-3.
    10. Marta Posada & Adolfo López-Paredes, 2007. "How to Choose the Bidding Strategy in Continuous Double Auctions: Imitation Versus Take-The-Best Heuristics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(1), pages 1-6.
    11. Brenner, Thomas, 2006. "Agent Learning Representation: Advice on Modelling Economic Learning," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 18, pages 895-947, Elsevier.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matthias Meyer & Klaus G. Troitzsch, 2012. "Epistemological perspectives on simulation: overview and introduction," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 1-4, March.
    2. David Passenier & Colin Mols & Jan Bím & Alexei Sharpanskykh, 2016. "Modeling safety culture as a socially emergent phenomenon: a case study in aircraft maintenance," Computational and Mathematical Organization Theory, Springer, vol. 22(4), pages 487-520, December.
    3. Bing Bai & Byungjoon Yoo & Xiuquan Deng & Iljoo Kim & Dehua Gao, 2016. "Linking routines to the evolution of IT capability on agent-based modeling and simulation: a dynamic perspective," Computational and Mathematical Organization Theory, Springer, vol. 22(2), pages 184-211, June.

    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. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    2. Edoardo Gaffeo & Mauro Gallegati & Umberto Gostoli, 2015. "An agent-based “proof of principle” for Walrasian macroeconomic theory," Computational and Mathematical Organization Theory, Springer, vol. 21(2), pages 150-183, June.
    3. Paola Tubaro, 2011. "Computational Economics," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 10, Edward Elgar Publishing.
    4. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    6. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    7. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    8. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    9. Salle, Isabelle & Yıldızoğlu, Murat & Sénégas, Marc-Alexandre, 2013. "Inflation targeting in a learning economy: An ABM perspective," Economic Modelling, Elsevier, vol. 34(C), pages 114-128.
    10. Graubner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)," IAMO Discussion Papers 109915, Institute of Agricultural Development in Transition Economies (IAMO).
    11. Vincze, János & Varga, Gergely, 2016. "Megtakarítási típusok - egy adaptív-evolúciós megközelítés [Types of saving - an adaptive-evolutionary approach]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 162-187.
    12. Graupner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM) [Das räumliche agenten-basierte Wettbewerbsmodell SpAbCoM]," IAMO Discussion Papers 135, Leibniz Institute of Agricultural Development in Transition Economies (IAMO).
    13. Albert Banal-Estañol & Augusto Rupérez-Micola, 2010. "Are agent-based simulations robust? The wholesale electricity trading case," Economics Working Papers 1214, Department of Economics and Business, Universitat Pompeu Fabra.
    14. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.
    15. Steinbacher, Matjaz, 2008. "Stochastic Processes in Finance and Behavioral Finance," MPRA Paper 13603, University Library of Munich, Germany.
    16. Marco LiCalzi & Lucia Milone & Paolo Pellizzari, 2011. "Allocative Efficiency and Traders’ Protection Under Zero Intelligence Behavior," Dynamic Modeling and Econometrics in Economics and Finance, in: Herbert Dawid & Willi Semmler (ed.), Computational Methods in Economic Dynamics, pages 5-28, Springer.
    17. Waltman, L. & van Eck, N.J.P., 2009. "A Mathematical Analysis of the Long-run Behavior of Genetic Algorithms for Social Modeling," ERIM Report Series Research in Management ERS-2009-011-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    18. Friederike Wall, 2016. "Agent-based modeling in managerial science: an illustrative survey and study," Review of Managerial Science, Springer, vol. 10(1), pages 135-193, January.
    19. Gergely Varga & Janos Vincze, 2017. "Saver types: An evolutionary-adaptive approach," CERS-IE WORKING PAPERS 1702, Institute of Economics, Centre for Economic and Regional Studies.
    20. Luzius Meisser, 2017. "The Code is the Model," International Journal of Microsimulation, International Microsimulation Association, vol. 10(3), pages 184-201.

    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:spr:comaot:v:18:y:2012:i:1:d:10.1007_s10588-012-9110-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.