IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2018-35-2.html
   My bibliography  Save this article

One Theory - Many Formalizations: Testing Different Code Implementations of the Theory of Planned Behaviour in Energy Agent-Based Models

Author

Abstract

As agent-based modelling gains popularity, the demand for transparency in underlying modelling assumptions grows. Behavioural rules guiding agents' decisions, learning, interactions and possible changes in these should rely on solid theoretical and empirical grounds. This field has matured enough to reach the point at which we need to go beyond just reporting what social theory we base these rules upon. Many social science theories operate with various abstract constructs such as attitudes, perceptions, norms or intentions. These concepts are rather subjective and remain open to interpretation when operationalizing them in a formal model code. There is a growing concern that how modellers interpret qualitative social science theories in quantitative ABMs may differ from case to case. Yet, formal tests of these differences are scarce, and a systematic approach to analyse any possible disagreements is lacking. Our paper addresses this gap by exploring the consequences of variations in formalizations of one social science theory on the simulation outcomes of agent-based models of the same class. We ran simulations to test the impact of four types of differences: in model architecture concerning specific equations and their sequence within one theory, in factors affecting agents' decisions, in the representation of these potentially different factors, and finally in the underlying distribution of data used in a model. We illustrate emergent outcomes of these differences using the example of an agent-based model, which is developed to study regional impacts of households' solar panel investment decisions. The Theory of Planned Behaviour was applied as one of the most common social science theories used to define behavioural rules of individual agents. Our findings demonstrate qualitative and quantitative differences in the simulation outcomes, even when agents' decision rules are based on the same theory and data. The paper outlines a number of critical methodological implications for future developments in agent-based modelling.

Suggested Citation

  • Hannah Muelder & Tatiana Filatova, 2018. "One Theory - Many Formalizations: Testing Different Code Implementations of the Theory of Planned Behaviour in Energy Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(4), pages 1-5.
  • Handle: RePEc:jas:jasssj:2018-35-2
    as

    Download full text from publisher

    File URL: https://www.jasss.org/21/4/5/5.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Bourceret, Amélie & Amblard, Laurence & Mathias, Jean-Denis, 2022. "Adapting the governance of social–ecological systems to behavioural dynamics: An agent-based model for water quality management using the theory of planned behaviour," Ecological Economics, Elsevier, vol. 194(C).
    2. Apetrei, Cristina I. & Strelkovskii, Nikita & Khabarov, Nikolay & Javalera Rincón, Valeria, 2024. "Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models: Learning-by-doing and social learning," Ecological Modelling, Elsevier, vol. 489(C).
    3. William Orjuela-Garzon & Santiago Quintero & Diana P. Giraldo & Laura Lotero & César Nieto-Londoño, 2021. "A Theoretical Framework for Analysing Technology Transfer Processes Using Agent-Based Modelling: A Case Study on Massive Technology Adoption (AMTEC) Program on Rice Production," Sustainability, MDPI, vol. 13(20), pages 1-23, October.
    4. Juste Raimbault, 2020. "Relating Complexities for the Reflexive Study of Complex Systems," Post-Print halshs-02430521, HAL.
    5. Bourceret, Amélie & Amblard, Laurence & Mathias, Jean-Denis, 2023. "How do farmers’ environmental preferences influence the efficiency of information instruments for water quality management? Evidence from a social-ecological agent-based model," Ecological Modelling, Elsevier, vol. 478(C).
    6. Noeldeke, Beatrice & Winter, Etti & Ntawuhiganayo, Elisée Bahati, 2022. "Representing human decision-making in agent-based simulation models: Agroforestry adoption in rural Rwanda," Ecological Economics, Elsevier, vol. 200(C).
    7. Julien Walzberg & Jean‐Marc Frayret & Annika L. Eberle & Alberta Carpenter & Garvin Heath, 2023. "Agent‐based modeling and simulation for the circular economy: Lessons learned and path forward," Journal of Industrial Ecology, Yale University, vol. 27(5), pages 1227-1238, October.

    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:jas:jasssj:2018-35-2. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Francesco Renzini (email available below). General contact details of provider: .

    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.