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SugarScape model is an artificialy intelligent agent-based social simulation following some/all rulles presented by J. Epstein & R. Axtell in their book Growing Artificial Socities.
Origins
Fundaments of SugarScape models could be tracked back to the University of Maryland where economist Thomas Schelling presented his paper titled " Models of Segregation" [1]. Written in 1969, Schilling's and the rest of the social environment modelling fraternity had their options limited by a lack of adequate computing power and a applicable programming mechanism to fully develop potential of their model.
John Conway's agent-based simulation " Game of Life" was enhanced and applied to Schelling original idea by Joshua M. Epstein & Robert Axtell in their book, Growing Artificial Socities. To demonstrate their findings on the field of agent-based simulation, model was created and distributed with their book on CD-ROM. Concept of this model has come to be known as "The Sugarscape model" [2].
Nowadays name "Sugarscape" is used for agent-based models using same or simillar rules defined by J. Epstein & R. Axtell in their book.
Principles
All Sugarscape models includes the agents (inhabitants), the environment (two-dimensional grid) and the rules governing the interaction of the agents with each other and the environment.
Original model presented by J. Epstein & R. Axtell (considered as first large scale agent model) is based on 51x51 cells grid, where every cell can contain different amount of sugar (or spice). In every step agents look around, find closest cell filled with sugar, move and metabolise. They can leave pollution, die, have sex (multiply), inherit sources, transfer information, trade or borrow sugar, generate imunity or transmit diseases - it all depends on specific scenario and variables defined at the setup of the model.
Sugar in simulation could be seen as a metaphor for resources in an artificial world through which examiner can study the effects of social dynamics such as evolution, marital status and inheritance on populations. [3]
Exact simulation of original rulles provided by J. Epstein & R. Axtell in their book can be tricky and itsn't always possible to recreate same results as those presented in Growing Artificial Societies.
Model implementations
Ascape
Original implementation was done in Ascape, Java software suitable for agent based social simulation. Nowadays sugarscape model is still part of the build-in models library [4].
Very complex and developed implementation of original sugarscape model done in Object Pascal and later in Java by Mark A. O'Neill. It can be easily used by other researchers as a testbed, where can they lay out their theory in terms of initial and subsequent states of the Sugarscape. The patterns resulting from the execution of the simulation can be used to confirm or revise their claims. For eg., a thesis about links between population concentrations and soil fertility could be explored by setting varying levels of fertility and noting the corresponding populations that the Sugarscape is able to support. [5] Everyone can access web version and test paramater variations for different scenarios or download whole project and modify core files for himself.
NetLogo
includes three Sugarscape scenarios in NetLogo Models Library. Immediate Growback, Constant Growback and Wealth Distribution. Besides these three scenarios lies Iain Weaver's Sugarscape NetLogo model, which is part of User Community Models Library. "It builds on Owen Densmore's NetLogo community model to encompass all rules discussed in GAS with the exception of the combat rule (although trivial to include, it adds little value to the model)." [6] Model is equipped with rich documentation of things to try and replication success of original sugarscape rulles.
SugarScape on steroids: simulating over a million agents at interactive rates
Due to the emergent nature of Agent-based models (ABMs), it is critical that the population sizes in the simulations match the population sizes of the dynamic systems being modeled. [7] However, the performance of current agent simulation frameworks is inadequate to handle such large population sizes and parallel computing frameworks designed to run on computing clusters is not answer due to the bandwidth limitations. Team of R. M. D’Souza, M. Lysenko and K Rahmani from Michigan Technological University used Sugarscape model to demonstrate power of GPU in ABM simulations with over 50 updates per second with agent populations exceeding 2 millions. [8]
Other implemantations could be found written in
Mathematica or in
GMU's Mason.
References
- ^ http://sugarscape.sourceforge.net/ [1], accessed November 7, 2010.
- ^ Joshua M. Epstein,Robert Axtell Growing artificial societies: social science from the bottom up. Brookings Institution Press, 1996. p. 6.
- ^ Agents at Work, (2003, June). CIO Insight, 1(27), 43. Retrieved November 11, 2010, from ABI/INFORM Global. (Document ID: 347271391).
- ^ http://ascape.sourceforge.net/manual/Section1.html, accessed November 9, 2010.
- ^ http://sugarscape.sourceforge.net/sugarscape.html, accessed November 9 2010
- ^ http://ccl.northwestern.edu/netlogo/models/community/Sugarscape, accessed November 9, 2010
- ^ Gilbert, N., Bankes, S., 2002, Platforms and Methods for Agent-Based Modeling, PNAS, 99(3) :7197–7198.
- ^ SugarScape on steroids: simulating over a million agents at interactive rates by: R. M. D'Souza, M. Lysenko, K. Rahmani, http://www.me.mtu.edu/~rmdsouza/Papers/2007/SugarScape_GPU.pdf, 2007
External links