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Evaluation of free Java-libraries for social-scientific agent based simulation

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This paper compares four freely available programming libraries for support of social scientific agent based computer simulation: RePast, Swarm, Quicksilver, and VSEit. Our aim is evaluation to determine the simulation framework that is the best suited for theory and data based modeling of social interventions, such as information campaigns. Our first step consisted in an Internet search for programming libraries and the selection of suitable candidates for detailed evaluation on the basis of 'knock out' criteria. Next, we developed a rating system and assessed the selected simulation environments on the basis of the rating criteria. The evaluation was based on official program documentation, statements by developers and users, and the experiences and impressions of the evaluators. The evaluation results showed the RePast environment to be the clear winner. In a further step, the evaluation results were weighted according to effort/time/energy saved by social scientists by using the particular ready-made programming library as compared to doing their own programming. Once again, the weighted results show RePast to win out over the other Java based programming libraries examined.

Suggested Citation

  • Robert Tobias & Carole Hofmann, 2004. "Evaluation of free Java-libraries for social-scientific agent based simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 7(1), pages 1-6.
    • Handle: RePEc:jas:jasssj:2003-45-2
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    Cited by:

    1. Cynthia Nikolai & Gregory Madey, 2009. "Tools of the Trade: A Survey of Various Agent Based Modeling Platforms," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(2), pages 1-2.
    2. Alan G. Isaac, 2011. "The ABM Template Models: A Reformulation with Reference Implementations," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(2), pages 1-5.
    3. Steven M Manson, 2007. "Challenges in Evaluating Models of Geographic Complexity," Environment and Planning B, , vol. 34(2), pages 245-260, April.
    4. Le-le Cao & Xiao-xue Li & Fen-ni Kang & Chang Liu & Fu-chun Sun & Ramamohanarao Kotagiri, 2015. "The Quantitative and Qualitative Evaluation of a Multi-Agent Microsimulation Model for Subway Carriage Design," International Journal of Microsimulation, International Microsimulation Association, vol. 8(3), pages 6-40.
    5. Guido Fioretti, 2005. "Agent-Based Models of Industrial Clusters and Districts," Urban/Regional 0504009, University Library of Munich, Germany.
    6. Torsten Trimborn & Philipp Otte & Simon Cramer & Maximilian Beikirch & Emma Pabich & Martin Frank, 2020. "SABCEMM: A Simulator for Agent-Based Computational Economic Market Models," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 707-744, February.
    7. T W Tewoldeberhan & A Verbraeck & V Hlupic, 2010. "Implementing a discrete-event simulation software selection methodology for supporting decision making at Accenture," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1446-1458, October.
    8. Richard Wallick, 2012. "Agent-based modeling, public choice, and the legacy of Gordon Tullock," Public Choice, Springer, vol. 152(1), pages 223-244, July.
    9. Camelia Delcea & Liviu-Adrian Cotfas & Ramona Paun, 2018. "Agent-Based Evaluation of the Airplane Boarding Strategies’ Efficiency and Sustainability," Sustainability, MDPI, vol. 10(6), pages 1-26, June.
    10. Junseok Hwang & Jihyoun Park & Jorn Altmann, 2010. "Two Risk-aware Resource Brokering Strategies in Grid Computing:Broker-driven vs. User-driven Methods," TEMEP Discussion Papers 201063, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Mar 2010.
    11. Stefano Balbi & Carlo Giupponi, 2009. "Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability," Working Papers 2009_15, Department of Economics, University of Venice "Ca' Foscari".
    12. Hilder André Bezerra Farias & Sérgio Luiz de Medeiros Rivero & Márcia Jucá Teixeira Diniz, 2017. "Negative incentives and sustainability in the amazonian logging industry [Negative incentives and sustainability in the amazonian logging industry]," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 27(3), pages 363-391, September.

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