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We ran one billion agents. Scaling in simulation models

Author

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  • Ross Richardson

    (Institute for New Economic Thinking at the Oxford Martin School; Mathematical Institute, University of Oxford, UK)

  • Matteo Richiardi

    (Institute for New Economic Thinking and Nuffield College, Oxford, UK; Collegio Carlo Alberto, Moncalieri, Italy Mathematical Institute, University of Oxford)

  • Michael Wolfson

    (University of Ottawa, Canada)

Abstract

We provide a clarification of scaling issues in simulation models, distinguishing between sample size determination, discovery of emergent properties involving a qualitative change in the behaviour of the system at an aggregate level, and ‘true’ scaling, the dependence of the quantitative behaviour of the system at any given level of aggregation, to its size. Scaling issues arise because we want to understand what happens when we run one billion agents, without actually having to run one billion agents. We discuss how we can use the Buckingham Pi theorem, a key tool in dimensional analysis, to provide guidance on the nature and structure of scaling relationships in agent-based models.

Suggested Citation

  • Ross Richardson & Matteo Richiardi & Michael Wolfson, 2015. "We ran one billion agents. Scaling in simulation models," Economics Papers 2015-W05, Economics Group, Nuffield College, University of Oxford.
  • Handle: RePEc:nuf:econwp:1505
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    References listed on IDEAS

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    1. Jakob Grazzini & Matteo G. Richiardi, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," LABORatorio R. Revelli Working Papers Series 130, LABORatorio R. Revelli, Centre for Employment Studies.
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