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Simulation Tools for Social Scientists: Building Agent Based Models with SWARM

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  • Pietro Terna

Abstract

Social scientists are not computer scientists, but their skills in the field have to become better and better to cope with the growing field of social simulation and agent based modelling techniques. A way to reduce the weight of software development is to employ generalised agent development tools, accepting both the boundaries necessarily existing in the various packages and the subtle and dangerous differences existing in the concept of agent in computer science, artificial intelligence and social sciences. The choice of tools based on the object oriented paradigm that offer libraries of functions and graphic widgets is a good compromise. A product with this kind of capability is Swarm, developed at the Santa Fe Institute and freely available, under the terms of the GNU license. A small example of a model developed in Swarm is introduced, in order to show directly the possibilities arising from the use of these techniques, both as software libraries and methodological guidelines. With simple agents - interacting in a Swarm context to solve both memory and time simulation problems - we observe the emergence of chaotic sequences of transaction prices.

Suggested Citation

  • Pietro Terna, 1998. "Simulation Tools for Social Scientists: Building Agent Based Models with SWARM," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(2), pages 1-4.
    • Handle: RePEc:jas:jasssj:1998-4-1
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    References listed on IDEAS

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    1. Nelson Minar & Rogert Burkhart & Chris Langton & Manor Askenazi, 1996. "The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations," Working Papers 96-06-042, Santa Fe Institute.
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    3. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
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    2. Chen Gao & Xiaochong Lan & Nian Li & Yuan Yuan & Jingtao Ding & Zhilun Zhou & Fengli Xu & Yong Li, 2024. "Large language models empowered agent-based modeling and simulation: a survey and perspectives," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-24, December.
    3. Giovanni Rabino & Alberto Girotti, 2004. "Ontology of multi-agents processes of spatial decision," ERSA conference papers ersa04p142, European Regional Science Association.
    4. Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 1(1), pages 57-72, March.
    5. Lamieri, Marco & Bertacchini, Enrico, 2006. "What if Hayek goes shopping in the bazaar?," MPRA Paper 367, University Library of Munich, Germany, revised 21 Jun 2006.
    6. Junying Chu & Can Wang & Jining Chen & Hao Wang, 2009. "Agent-Based Residential Water Use Behavior Simulation and Policy Implications: A Case-Study in Beijing City," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(15), pages 3267-3295, December.
    7. Fu-ren Lin & Shyh-ming Lin, 2006. "Enhancing the Supply Chain Performance by Integrating Simulated and Physical Agents into Organizational Information Systems," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(4), pages 1-1.
    8. 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".

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