This research presents a method to identify candidate features of a resilient versus vulnerable social-ecological system, and employs complex systems science, using computer simulation to explore this topic using the ancient Maya as an example.
Few topics gain as much cross-disciplinary interest as the rise and fall of ancient civilisations. The story of development and demise in complex societies contains narratives of the human endeavour threatened by devastating droughts, greedy rulers, foreign imperialism, and overuse of natural resources, among others. Societies are, however, a set of interacting elements which as a whole express characteristic features, interpreted as emergent properties of underlying processes at multiple scales. Designing a holistic approach to understanding social-ecological systems requires methods which simultaneously observe patterns in many dimensions, a kind of observation for which van der Leeuw (2012) argues that traditional Western science is not very well equipped. An analogy is the example of solving a Rubik’s Cube, in that one cannot get the cube ‘in order’ by dealing first with one side, then the next, and so forth. The only way to arrive at order is by looking at the patterns on all sides simultaneously, and not favouring any particular one at any time (van der Leeuw 2012). This research presents a method to identify candidate features of a resilient versus vulnerable social-ecological system, and employs complex systems science, using computer simulation to explore this topic using the ancient Maya as an example.
A number of research questions are presented for exploration:
- What dynamics lead to the development of the densely populated and interconnected human geography of the ancient Maya?
- Is it possible to use computational social science to ‘grow’ the three Maya temporal periods of the Preclassic (1000 BC – AD 250), Classic (AD 250–900), and Postclassic (AD 900–1500)?
- How does the simulated social-ecological system develop and respond to changing conditions, and what modelled indicators warn of vulnerability?
In order to explore these research questions, a simulation model was designed and calibrated for the landscape of Central America. Model runs produce temporal and spatial patterns which can be understood through examining the underlying assumptions of the different integrated components of the model. MayaSim is a combined agent-based, cellular automata, and network model that represents the ancient Maya social-ecological system. Agents, cells, and networks are programmed to represent elements of the historical Maya civilisation, including demographics, trade, agriculture, soil degradation, provision of ecosystem services, climate variability, hydrology, primary productivity, and forest succession. Simulating these in combination allows patterns to emerge at the landscape level, effectively growing the social-ecological system from the bottom up. This approach constructs an artificial social-ecological laboratory where different theories can be tested and hypotheses proposed for how the system will perform under different configurations.
The model is able to reproduce spatial patterns and timelines somewhat analogous to that of the ancient Maya’s history. This proof of concept model requires refinement and further archaeological data for calibration to improve results, although it is noted that there is little empirical evidence by which to validate such models, and such evidence is generally site-specific and discontinuous through time.
The purpose of the model is to better understand the complex dynamics of social-ecological systems and to test quantitative indicators of resilience as predictors of system sustainability. An integrated agent-based, cellular automata, and network model was constructed using the software Netlogo. The full model, code and documentation is available via the www.openabm.org website.
The MayaSim model represents settlements as agents located in a gridded landscape. The software interface, shown in the figure below presents the spatial view of the model with graphs tracking model data and a user interface for interacting with the model. The view can be changed to observe different spatial data layers within the model. Upon model initialisation, base GIS layers are imported using the Netlogo GIS extension. Imported spatial data include elevation and slope, soil productivity, and temperature and precipitation.
Watch the MayaSim video here
Heckbert, S., Costanza, R., Parrott, L. (in press). Achieving sustainable societies: Lessons from modelling the ancient Maya. Solutions Journal.
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.. (in press). 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.
Heckbert, S., & Bishop, I. (2011). Empirical calibration of spatially explicit agent-based models. Chapter in: D. Marceau & I. Benenson (Eds.), Advanced Geosimulation. Bentham. 92-110.
Heckbert, S., Baynes, T., & Reeson, A. (2010). Agent-based modelling in ecological economics. NYAS Ecological Economics Reviews, 1185, 39-53.
Scott Heckbert, Christian Isendahl, Joel Gunn, Andrew Reeson, Simon Brewer, Tim Baynes, Vernon Scarborough, Arlen Chase, Diane Chase, Robert Costanza, John Murphy, Derek Robinson, Nicholas Dunning, Carsten Lemmen, Lael Parrot, Timothy Beach, Sheryl Luzzadder-Beach, David Lentz, Paul Sinclair, Carole Crumley and Sander van der Leeuw. This project was supported by Alberta Innovates Technology Futures, Portland State University, Arizona State University, Uppsala University, and University of Cincinnati.
Alberta Innovates Technology Futures, Portland State University, Arizona State University, Uppsala University, and University of Cincinnati.