CNH: RCN: A Research Network for Computational Modeling in the Socioecological Sciences

Project: Research project

Project Details


Emerging technologies and innovationsalong with the near instantaneous transmission of informationhave created this tightly-linked world where new possibilities for improving or degrading our quality of life actually lead to a heightened sense of uncertainty about our ability to respond effectively to rapid changes at multiple scales. Critical decisions could improve the lives of humanity and integrate institutions across the globe, or they could leave our world unable to support current populations and societies. Study of the near and distant human past shows that measures to maintain resilience and plan for the future resulted in collapse more often than notsometimes catastrophically. We now know that recursively interacting social and environmental systems are complex, and policy and management outcomes often have unintended and commonly undesirable outcomes (Diamond 2005; Fisher and Feinman 2005; Redman 1999). At least for past societies, the most serious consequences of social collapse generally did not extend far beyond the borders of a particular polityeven in the case of large, regional states. Similarly, environmental degradation, even if profound and long lasting, was generally localized. Today however, for the first time in our collective history, our world is tightly interconnected in a social, economic, and ecological web, such that both slow and fast socioecological degradation coupled with environmental mismanagement has the potential to initiate a global cascade with profound and diverse consequences. Complex, constantly adapting systems, such as human societies, are in a state of perpetual flux ranging from dynamic stability to gradual reorganization of interactions and actors to the sudden and catastrophic. Small changes can cascade through multiple systems resulting in surprise. In part to address this challenge, National Science Foundation programs like CNH promote basic research to gain new insights into the recursive interrelationships between society and environmental change, and explicitly encourage quantitative modeling as a means to accomplish this. A review of CNH abstracts since 2000 reveals that 88% of awarded grants mention modeling and nearly 20% specifically mention agent-based modeling. While any simplified representation (qualitative or quantitative) of a real-world system can be considered a model, the often non-linear, non-intuitive, and usually surprising relationships and consequences that emerge from recurrent, complex interactions between human social practice and the biophysical environment make it imperative that scientific modelsincluding their underlying assumptions, algorithmic processes, logical consistency, and connections with empirical databe explicit, quantitative, and grounded in the real world (Epstein 2008; Subrahmanian 2007; Turchin 2008). This makes it much more likely that their abilities to characterize the dynamics of coupled natural and human systems can be evaluated, that they can be replicated, and that a succession of researchers can continue to elaborate and improve them, building both a legacy and a cohesive body of knowledge. The complex adaptive system nature of human social systems and at least the living components of biophysical systems means that computational modeling in general, and agent-based modeling in particular (ABM/CM, including individual-based modeling, cellular automata, dynamic network modeling, and other forms of computer simulation) are important tools for research in this domain. However, in spite of the fact that ABM has existed as a potential research tool for nearly two decades (and related computational modeling techniques even longer) and is mentioned in many CNH proposals, it remains little used or understood in the broader scientific and applied communities involved in the science and management of coupled natural and human systems. In this sense, ABM is an exemplar both of the potential
Effective start/end date10/1/099/30/16


  • National Science Foundation (NSF): $499,735.00


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