IDEAS home Printed from https://ideas.repec.org/a/ijm/journl/v10y2017i3p184-201.html
   My bibliography  Save this article

The Code is the Model

Author

Listed:
  • Luzius Meisser

    (University of Zurich, Zurich, Switzerland)

Abstract

Conventionally, agent-based models are specified in a combination of natural language and mathematical terms, and their implementation seen as an afterthought. I challenge this view and argue that it is the source code that represents the model best, with natural language and mathematical descriptions serving as documentation. This modeling paradigm is inspired by agile software development and adopting it leads to various - mostly beneficial - consequences. First, discrepancies between the specification documents and what the model actually does are eliminated by definition as the code becomes the specification. Second, replicability is greatly improved. Third, object-oriented programming is recognized as an integral part of a modeler?s skill set. Forth, tools and methods from software engineering can support the modeling process, making it more agile. Fifth, increased modularity allows to better manage complexity and enables the collaborative construction of large models. Sixth, the way models are published needs to be reconsidered, with source code ideally being part of the peer review. Seventh, the quality of source code in science is improved as it enjoys more importance, attention and scrutiny.

Suggested Citation

  • Luzius Meisser, 2017. "The Code is the Model," International Journal of Microsimulation, International Microsimulation Association, vol. 10(3), pages 184-201.
    • Handle: RePEc:ijm:journl:v10:y:2017:i:3:p:184-201
    as

    Download full text from publisher

    File URL: http://www.microsimulation.org/IJM/V10_3/IJM_2017_10_3_6.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    2. John H. Miller & Scott E. Page, 2007. "Social Science in Between, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    3. 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.
    4. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    5. Caiani, Alessandro & Godin, Antoine & Caverzasi, Eugenio & Gallegati, Mauro & Kinsella, Stephen & Stiglitz, Joseph E., 2016. "Agent based-stock flow consistent macroeconomics: Towards a benchmark model," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 375-408.
    6. 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.
    7. Marco A. Janssen, 2017. "The Practice of Archiving Model Code of Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-2.
    8. Gualdi, Stanislao & Tarzia, Marco & Zamponi, Francesco & Bouchaud, Jean-Philippe, 2015. "Tipping points in macroeconomic agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 29-61.
    9. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    10. 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.
    11. John H. Miller & Scott E. Page, 2007. "Complexity in Social Worlds, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters, in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
    12. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    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. Matteo Richiardi, 2017. "The Code and the Model. A response to "The Code is the Model", by Luzius Meisser," International Journal of Microsimulation, International Microsimulation Association, vol. 10(3), pages 204-208.

    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. Adão, Luiz F.S. & Silveira, Douglas & Ely, Regis A. & Cajueiro, Daniel O., 2022. "The impacts of interest rates on banks’ loan portfolio risk-taking," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    2. David C. Earnest & Ian F. Wilkinson, 2018. "An agent based model of the evolution of supplier networks," Computational and Mathematical Organization Theory, Springer, vol. 24(1), pages 112-144, March.
    3. Michael S. Harr'e, 2018. "Multi-agent Economics and the Emergence of Critical Markets," Papers 1809.01332, arXiv.org.
    4. Fenintsoa Andriamasinoro & Raphael Danino-Perraud, 2021. "Use of artificial intelligence to assess mineral substance criticality in the French market: the example of cobalt," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 34(1), pages 19-37, April.
    5. Gräbner, Claudius, 2016. "From realism to instrumentalism - and back? Methodological implications of changes in the epistemology of economics," MPRA Paper 71933, University Library of Munich, Germany.
    6. Ciola, Emanuele & Turco, Enrico & Gurgone, Andrea & Bazzana, Davide & Vergalli, Sergio & Menoncin, Francesco, 2023. "Enter the MATRIX model:a Multi-Agent model for Transition Risks with application to energy shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    7. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2017. "How to benefit from a common European electricity market design," Energy Policy, Elsevier, vol. 101(C), pages 629-643.
    8. Aur'elien Hazan, 2016. "Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model," Papers 1601.00822, arXiv.org, revised Jan 2017.
    9. Juana Castro & Stefan Drews & Filippos Exadaktylos & Joël Foramitti & Franziska Klein & Théo Konc & Ivan Savin & Jeroen van den Bergh, 2020. "A review of agent‐based modeling of climate‐energy policy," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(4), July.
    10. Matus Halas, 2018. "Balancing Against Threats In Interactions Determined By Distance And Overall Gains," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 1-22, August.
    11. Ringler, Philipp & Keles, Dogan & Fichtner, Wolf, 2016. "Agent-based modelling and simulation of smart electricity grids and markets – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 205-215.
    12. Ciola, Emanuele & Turco, Enrico & Gurgone, Andrea & Bazzana, Davide & Vergalli, Sergio & Menoncin, Francesco, 2022. "Charging the macroeconomy with an energy sector: an agent-based model," FEEM Working Papers 319877, Fondazione Eni Enrico Mattei (FEEM).
    13. Florian Chávez-Juárez, 2017. "On the Role of Agent-based Modeling in the Theory of Development Economics," Review of Development Economics, Wiley Blackwell, vol. 21(3), pages 713-730, August.
    14. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    15. Zhang, Hui & Cao, Libin & Zhang, Bing, 2017. "Emissions trading and technology adoption: An adaptive agent-based analysis of thermal power plants in China," Resources, Conservation & Recycling, Elsevier, vol. 121(C), pages 23-32.
    16. Pascal Seppecher & Isabelle Salle & Dany Lang, 2019. "Is the market really a good teacher?," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 299-335, March.
    17. Tommaso Ciarli & André Lorentz & Marco Valente & Maria Savona, 2019. "Structural changes and growth regimes," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 119-176, March.
    18. Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018. "Agent-based model calibration using machine learning surrogates," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
    19. 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.
    20. 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.

    More about this item

    Keywords

    AGENT-BASED MODELING; AGILE SOFTWARE ENGINEERING; MODELING METHODOLOGY;
    All these keywords.

    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

    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:ijm:journl:v10:y:2017:i:3:p:184-201. 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: Jinjing Li (email available below). General contact details of provider: http://www.microsimulation.pub .

    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.