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How Do Agents Make Decisions? A Survey

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Abstract

When designing an agent-based simulation, an important question to answer is how to model the decision making processes of the agents in the system. A large number of agent decision making models can be found in the literature, each inspired by different aims and research questions. In this paper we provide a review of 14 agent decision making architectures that have attracted interest. They range from production-rule systems to psychologically- and neurologically-inspired approaches. For each of the architectures we give an overview of its design, highlight research questions that have been answered with its help and outline the reasons for the choice of the decision making model provided by the originators. Our goal is to provide guidelines about what kind of agent decision making model, with which level of simplicity or complexity, to use for which kind of research question.

Suggested Citation

  • Tina Balke & Nigel Gilbert, 2014. "How Do Agents Make Decisions? A Survey," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(4), pages 1-13.
    • Handle: RePEc:jas:jasssj:2013-71-3
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    1. Amir Ali Safaei Pirooz & Mohammad J. Sanjari & Young-Jin Kim & Stuart Moore & Richard Turner & Wayne W. Weaver & Dipti Srinivasan & Josep M. Guerrero & Mohammad Shahidehpour, 2023. "Adaptation of High Spatio-Temporal Resolution Weather/Load Forecast in Real-World Distributed Energy-System Operation," Energies, MDPI, vol. 16(8), pages 1-16, April.
    2. Petra Ahrweiler, 2017. "Agent-based simulation for science, technology, and innovation policy," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 391-415, January.
    3. Cristiano CODAGNONE & Giovanni LIVA & Egidijus BARCEVICIUS & Gianluca MISURACA & Luka KLIMAVICIUTE & Michele BENEDETTI & Irene VANINI & Giancarlo VECCHI & Emily RYEN GLOINSON & Katherine STEWART & Sti, 2020. "Assessing the impacts of digital government transformation in the EU: Conceptual framework and empirical case studies," JRC Research Reports JRC120865, Joint Research Centre.
    4. Lorraine Dodd, 2019. "Choice-making and choose-ables: making decision agents more human and choosy," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 7(1), pages 101-115, May.
    5. Huber, Robert & Bakker, Martha & Balmann, Alfons & Berger, Thomas & Bithell, Mike & Brown, Calum & Grêt-Regamey, Adrienne & Xiong, Hang & Le, Quang Bao & Mack, Gabriele & Meyfroidt, Patrick & Millingt, 2018. "Representation of decision-making in European agricultural agent-based models," Agricultural Systems, Elsevier, vol. 167(C), pages 143-160.
    6. David Anzola & Peter Barbrook-Johnson & Juan I. Cano, 2017. "Self-organization and social science," Computational and Mathematical Organization Theory, Springer, vol. 23(2), pages 221-257, June.
    7. repec:eur:ejfejr:7 is not listed on IDEAS
    8. Robert Huber & Hang Xiong & Kevin Keller & Robert Finger, 2022. "Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 35-63, February.
    9. Ida Nadia S. Djenontin & Leo C. Zulu & Arika Ligmann-Zielinska, 2020. "Improving Representation of Decision Rules in LUCC-ABM: An Example with an Elicitation of Farmers’ Decision Making for Landscape Restoration in Central Malawi," Sustainability, MDPI, vol. 12(13), pages 1-35, July.
    10. 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).
    11. Pablo Pico-Valencia & Juan A Holgado-Terriza, 2018. "Agentification of the Internet of Things: A systematic literature review," International Journal of Distributed Sensor Networks, , vol. 14(10), pages 15501477188, October.
    12. Anna Borawska & Malgorzata Latuszynska, 2020. "Incorporating Neuroscience Data into Agent-Based Simulation Models of Buyer Behavior," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1197-1212.
    13. Gianluca Misuraca & Luigi Geppert & Cristiano Codagnone, 2017. "i-FRAME – Assessing impacts of social policy innovation in the EU: Proposed methodological framework to evaluate socio-economic returns on investment of social policy innovations," JRC Research Reports JRC108078, Joint Research Centre.
    14. María Elena Orduña Alegría & Niels Schütze & Samuel C. Zipper, 2020. "A Serious Board Game to Analyze Socio-Ecological Dynamics towards Collaboration in Agriculture," Sustainability, MDPI, vol. 12(13), pages 1-19, June.
    15. 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.
    16. Hazel Squires & Michael P. Kelly & Nigel Gilbert & Falko Sniehotta & Robin C. Purshouse, 2023. "The long‐term effectiveness and cost‐effectiveness of public health interventions; how can we model behavior? A review," Health Economics, John Wiley & Sons, Ltd., vol. 32(12), pages 2836-2854, December.
    17. Schlüter, Maja & Baeza, Andres & Dressler, Gunnar & Frank, Karin & Groeneveld, Jürgen & Jager, Wander & Janssen, Marco A. & McAllister, Ryan R.J. & Müller, Birgit & Orach, Kirill & Schwarz, Nina & Wij, 2017. "A framework for mapping and comparing behavioural theories in models of social-ecological systems," Ecological Economics, Elsevier, vol. 131(C), pages 21-35.

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    Keywords

    Decision Making; Agents; Survey;
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