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Communicating Social Simulation Models to Sceptical Minds

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Abstract

When talking to fellow modellers about the feedback we get on our simulation models the conversation quickly shifts to anecdotes of rejective scepticism. Many of us experience that they get only few remarks, and especially only little helpful constructive feedback on their simulation models. In this forum paper, we give an overview and reflections on the most common criticisms experienced by ABM modellers. Our goal is to start a discussion on how to respond to criticism, and particularly rejective scepticism, in a way that makes it help to improve our models and consequently also increase acceptance and impact of our work. We proceed by identifying common criticism on agent-based modelling and social simulation methods and show where it shifts to rejection. In the second part, we reflect on the reasons for rejecting the agent-based approach, which we mainly locate in a lack of understanding on the one hand, and academic territorialism on the other hand. Finally, we also give our personal advice to socsim modellers of how to deal with both forms of rejective criticism.

Suggested Citation

  • Annie Waldherr & Nanda Wijermans, 2013. "Communicating Social Simulation Models to Sceptical Minds," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(4), pages 1-13.
    • Handle: RePEc:jas:jasssj:2013-3-2
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    Cited by:

    1. Ozge Dilaver, 2015. "From Participants to Agents: Grounded Simulation as a Mixed-Method Research Design," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(1), pages 1-15.
    2. Thompson, Jason & McClure, Roderick & Scott, Nick & Hellard, Margaret & Abeysuriya, Romesh & Vidinaarachichi, Rajith & Thwaites, John & Lazarus, Jeffrey & Michie, Susan & Bullen, Chris, 2021. "A framework for communicating the utility of models when facing tough decisions in public health," SocArXiv 2duk5, Center for Open Science.
    3. Nicholas R. Magliocca, 2020. "Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus," Land, MDPI, vol. 9(12), pages 1-25, December.
    4. 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.

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