News & Events
Dr. Joel Gunn’s new article on the Project IHOPE-Maya: Using Past to Inform the Future
Joel Gunn, Lecturer, Anthropology, . (UNCG photo by David Wilson – 10/09/2013)
Abstract: IHOPE-MAYA BEGAN IN 2009 at the Center Advanced Research in Santa Fe and formed a loose research network calling irregularly for workshops and meetings. After the Santa Fe meeting, we proceeded to select a uniform set of data for evaluation by Principal Component Analysis (PCA) (Gunn and IHOPE-Maya-Members 2010, Gunn et al 2016) and a simulation was compiled of basic resources and social processes (Heckbert 2013, Heckbert et al. 2016). In 2014 Miller and Morissette (2014) published an article in Ecology and Society that suggests that development of actionable science intelligence for the benefit of decision makers versus global challenges, requires data analysis, simulation and construction of scenarios that Involve interested parties. This has been the long-term goal of the IHOPE organization globally, so we decided to take some time to evaluate our efforts in relationship with their suggestions and use the results for future plans. This work evaluates some of our collective efforts so far in light of the plan of analysis independently developed by Miller and Morisette, theirs’s animal species and ours about socio-ecological systems. Other findings to date, in particular, compare our simulations and analysis of data regarding the coherence of the information and the energy gradients of societies in the Central lowlands of the Maya. In the data analysis we find evidence of a complex adaptive system with emergent properties and possible attractors. We would like to understand these phenomena in terms of long term and multi-stage cultural adaptation (Iannone 2014). Next, we move on to what these results imply for decision making addressing global challenges in view of the methodology of Miller and Morisette. This document should be considered in the light of reading Miller and Morisette, since their regime raises many issues such as uncertainties, verification, alternative methods of simulation and analysis schemes of data that we need to address.
