Advertisement

Replication of Sugarscape Using MASON

  • Anthony Bigbee
  • Claudio Cioffi-Revilla
  • Sean Luke
Part of the Springer Series on Agent Based Social Systems book series (ABSS, volume 3)

Abstract

The purpose of this research was to replicate the Sugarscape model (Eptstein and Axtell 1996) and simulation outcomes as described in Growing Artificial Societies (GAS). Sugarscape is a classic agent-based model and contemporary simulation toolkits usually only have a very simple replication of a few core rules. There is scant evidence of significant replication of the rules and simulation outcomes; code supplied with Repast, Swarm, and NetLogo implement a minority of the rules in Sugarscape. In particular, the standard Repast distribution only implements Growback, Movement, and Replacement. Sugarscape implementations in these toolkits are clearly provided only as basic demonstrations of how wellknown social models might be implemented, rather than complete achievements of scientific replication.

Keywords

Cellular Automaton Cellular Automaton None None Pollution Diffusion Rule Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bigbee A (2005) Replication of Sugarscape Using MASON. Unpublished master’s thesis, George Mason University, Fairfax, VA.Google Scholar
  2. Densmore O (2005) Sugarscape. Retrieved April 1, 2005, from http://backspaces.net/Models/sugarscape.htmlGoogle Scholar
  3. Doran J (2000) Questions in the Methodology of Artificial Societies. In. Suleiman R, Troitzsch K, Gilbert N (eds), Tools and Techniques for Social Science Simulation.: Physica-Verlag, HeidelbergGoogle Scholar
  4. Epstein J, Axtell R (1996) Growing Artificial Societies: Social Science from the Bottom Up. Brookings Institution Press, Washington, D.C.Google Scholar
  5. Hegselmann, R, Flache A (1998, June) Understanding Complex Social Dynamics: A Plea for Cellular Automata Based Modeling. J Artificial Societies and Social Simulation 3, Retrieved August 1, 2004, from http://www.soc.surrey.ac.Uk/JASSS/l/3/l.htmlGoogle Scholar
  6. Huberman B, Hogg T (1988) The Behavior of Computational Ecologies. In B. Huberman (Ed.), The Ecology of Computation. Amsterdam: North-Holland.Google Scholar
  7. Kleiber C, Kotz S (2003) Statistical Size Distributions in Economics and Actuarial Sciences. Wiley, Hoboken, NJMATHGoogle Scholar
  8. Kliemt H (1996) Simulation and Rational Practice. In R. Hegselmann, U. Mueller (Eds.), Modelling and simulation in the social sciences from a philosophy of science point of view. Kluwer, DordrechtGoogle Scholar
  9. Luke S, Cioffi-Revilla C, Panait L, Sullivan K, Balan G (2005) MASON: A Multi-Agent Simulation Environment. Simulation 81: 517–527CrossRefGoogle Scholar
  10. Nowak A, Lewenstein M (1996) Modeling social change with cellular automata. In R. Hegselmann, U. Mueller (Eds.), Modelling and simulation in the social sciences from a philosophy of science point of view. Kluwer, DordrechtGoogle Scholar
  11. Wheeler D SLOCCount. Retrieved April 1, 2005, from http://www.dwheeler.com/sloccount/Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Anthony Bigbee
    • 1
  • Claudio Cioffi-Revilla
    • 1
  • Sean Luke
    • 1
  1. 1.Center for Social Complexity and Evolutionary Computation LaboratoryGeorge Mason UniversityFairfaxUSA

Personalised recommendations