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Social Simulation for Non-hackers

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Multi-Agent-Based Simulation XXII (MABS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13128))

Abstract

Computer simulation is a powerful tool for social scientists, but popular platforms require representing the semantics of the model being simulated in computer code, leading to models that are either expensive to construct, inefficient, or inaccurate. We introduce SCAMP (Social Causality using Agents with Multiple Perspectives), a social simulator that uses stigmergy to execute models that are written as concept maps and spreadsheets, without requiring any programming expertise on the part of the modeler. This Repast-based framework has been extensively exercised in the DARPA Ground Truth program to generate realistic social data for analysis by social scientists.

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Notes

  1. 1.

    In addition to the author, the SCAMP team included J.A. Morell of 4.699 LLC; L. Sappelsa of ANSER LLC; J. Greanya and S. Nadella of Wright State Research Institute (now Parallax Advanced Research). Kathleen Carley of CMU consulted on social network issues.

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Acknowledgements

The development of SCAMP was funded by the Defense Advanced Research Projects Agency (DARPA), under Cooperative Agreement HR00111820003. The content of this paper does not necessarily reflect the position or the policy of the US Government, and no official endorsement should be inferred.

Author information

Authors and Affiliations

  1. Parallax Advanced Research, Beavercreek, OH, 45431, USA

    H. Van Dyke Parunak

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  1. H. Van Dyke Parunak
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Correspondence to H. Van Dyke Parunak .

Editor information

Editors and Affiliations

  1. Imperial College London, London, UK

    Koen H. Van Dam

  2. Toulouse 1 Capitole University, Toulouse, France

    Nicolas Verstaevel

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Parunak, H.V.D. (2022). Social Simulation for Non-hackers. In: Van Dam, K.H., Verstaevel, N. (eds) Multi-Agent-Based Simulation XXII. MABS 2021. Lecture Notes in Computer Science(), vol 13128. Springer, Cham. https://doi.org/10.1007/978-3-030-94548-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-94548-0_1

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