MayaSim: An agent-based model of the ancient Maya social-ecological system (1.0.0)
The MayaSim model is an integrated agent-based, cellular automata, and network model representing the ancient Maya social-ecological system. The model represents the relationship between population growth, agricultural production, soil degradation, climate variability, primary productivity, hydrology, ecosystem services, forest succession, and the stability of trade networks. Agents representing settlements develop and expand within a spatial landscape that changes under climate variation and responds to anthropogenic impacts. The model is able to reproduce spatial patterns and timelines somewhat analogous to that of the ancient Maya, although this proof-of-concept model requires refinement and further archaeological data for calibration. This paper aims to identify candidate features of a resilient versus vulnerable social-ecological system, and employs computer simulation to explore this topic, using the ancient Maya as an example. Complex systems modelling identifies how interconnected variables behave, considering fast-moving variables such as land cover change and trade connections, meso-speed variables such as demographics and climate variability, as well as slow-moving variables such as soil degradation.
Release Notes
Associated Publications
Heckbert, S. (in press) MayaSim: An Agent-Based Model of the Ancient Maya Social-Ecological System. Journal of Artificial Societies and Social Simulation.
Heckbert, S., Isendahl, C., Gunn, J., Brewer, S., Scarborough, V., Chase, A.F., Chase, D.Z., Costanza, R., Dunning, N., Beach, T., Luzzadder-Beach, S., Lentz, D., Sinclair, P.. (2013). Growing the ancient Maya social-ecological system from the bottom up. In: Isendahl, C., and Stump, D. (eds.), Applied Archaeology, Historical Ecology and the Useable Past. Oxford University Press.
This release is out-of-date. The latest version is
1.3.0
MayaSim: An agent-based model of the ancient Maya social-ecological system 1.0.0
Submitted byScott HeckbertPublished Jul 11, 2012
Last modified Feb 23, 2018
The MayaSim model is an integrated agent-based, cellular automata, and network model representing the ancient Maya social-ecological system. The model represents the relationship between population growth, agricultural production, soil degradation, climate variability, primary productivity, hydrology, ecosystem services, forest succession, and the stability of trade networks. Agents representing settlements develop and expand within a spatial landscape that changes under climate variation and responds to anthropogenic impacts. The model is able to reproduce spatial patterns and timelines somewhat analogous to that of the ancient Maya, although this proof-of-concept model requires refinement and further archaeological data for calibration. This paper aims to identify candidate features of a resilient versus vulnerable social-ecological system, and employs computer simulation to explore this topic, using the ancient Maya as an example. Complex systems modelling identifies how interconnected variables behave, considering fast-moving variables such as land cover change and trade connections, meso-speed variables such as demographics and climate variability, as well as slow-moving variables such as soil degradation.
Cite this Model
Scott Heckbert (2013, April 27). “MayaSim: An agent-based model of the ancient Maya social-ecological system” (Version 1.0.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/3063/releases/1.0.0/
Associated Publication(s)
Heckbert, S. (in press) MayaSim: An Agent-Based Model of the Ancient Maya Social-Ecological System. Journal of Artificial Societies and Social Simulation.
Heckbert, S., Isendahl, C., Gunn, J., Brewer, S., Scarborough, V., Chase, A.F., Chase, D.Z., Costanza, R., Dunning, N., Beach, T., Luzzadder-Beach, S., Lentz, D., Sinclair, P.. (2013). Growing the ancient Maya social-ecological system from the bottom up. In: Isendahl, C., and Stump, D. (eds.), Applied Archaeology, Historical Ecology and the Useable Past. Oxford University Press.
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