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An Agent Operationalization Approach for Context Specific Agent-Based Modeling

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Abstract

The potential of agent-based modeling (ABM) has been demonstrated in various research fields. However, three major concerns limit the full exploitation of ABM; (i) agents are too simple and behave unrealistically without any empirical basis, (ii) 'proof of concept' applications are too theoretical and (iii) too much value placed on operational validity instead of conceptual validity. This paper presents an operationalization approach to determine the key system agents, their interaction, decision-making and behavior for context specific ABM, thus addressing the above-mentioned shortcomings. The approach is embedded in the framework of Giddens' structuration theory and the structural agent analysis (SAA). The agents' individual decision-making (i.e. reflected decisions) is operationalized by adapting the analytical hierarchy process (AHP). The approach is supported by empirical system knowledge, allowing us to test empirically the presumed decision-making and behavioral assumptions. The output is an array of sample agents with realistic (i.e. empirically quantified) decision-making and behavior. Results from a Swiss mineral construction material case study illustrate the information which can be derived by applying the proposed approach and demonstrate its practicability for context specific agent-based model development.

Suggested Citation

  • Christof Knoeri & Claudia R. Binder & Hans-Joerg Althaus, 2011. "An Agent Operationalization Approach for Context Specific Agent-Based Modeling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(2), pages 1-4.
    • Handle: RePEc:jas:jasssj:2010-55-2
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    1. 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.
    2. Sebastian Schutte, 2010. "Optimization and Falsification in Empirical Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 1-2.
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    1. Kostadinov, Fabian & Holm, Stefan & Steubing, Bernhard & Thees, Oliver & Lemm, Renato, 2014. "Simulation of a Swiss wood fuel and roundwood market: An explorative study in agent-based modeling," Forest Policy and Economics, Elsevier, vol. 38(C), pages 105-118.
    2. Viet-Cuong Trieu & Fu-Ren Lin, 2022. "The Development of a Service System for Facilitating Food Resource Allocation and Service Exchange," Sustainability, MDPI, vol. 14(19), pages 1-29, September.
    3. Valeria Superti & Cynthia Houmani & Ralph Hansmann & Ivo Baur & Claudia R. Binder, 2021. "Strategies for a Circular Economy in the Construction and Demolition Sector: Identifying the Factors Affecting the Recommendation of Recycled Concrete," Sustainability, MDPI, vol. 13(8), pages 1-32, April.
    4. Xiaochao Wei & Yanfei Zhang & Qi Liao & Guihua Nie, 2022. "Multi-Agent Simulation of Product Diffusion in Online Social Networks from the Perspective of Overconfidence and Network Effects," Sustainability, MDPI, vol. 14(11), pages 1-18, May.
    5. Knoeri, Christof & Binder, Claudia R. & Althaus, Hans-Joerg, 2011. "Decisions on recycling: Construction stakeholders’ decisions regarding recycled mineral construction materials," Resources, Conservation & Recycling, Elsevier, vol. 55(11), pages 1039-1050.
    6. Hecher, Maria & Hatzl, Stefanie & Knoeri, Christof & Posch, Alfred, 2017. "The trigger matters: The decision-making process for heating systems in the residential building sector," Energy Policy, Elsevier, vol. 102(C), pages 288-306.
    7. Bhatt, Brijesh & Singh, Anoop, 2020. "Stakeholders’ role in distribution loss reduction technology adoption in the Indian electricity sector: An actor-oriented approach," Energy Policy, Elsevier, vol. 137(C).
    8. Busch, Jonathan & Roelich, Katy & Bale, Catherine S.E. & Knoeri, Christof, 2017. "Scaling up local energy infrastructure; An agent-based model of the emergence of district heating networks," Energy Policy, Elsevier, vol. 100(C), pages 170-180.

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