Introduction:
Dr. Rogers recently recieved an NSF grant to develop computer models of how societies interact with their environments. Read on for a brief description of the project, and check out the recent news articles for more examples of what kinds of problems the team is looking to explore.
Suite of computational models to be developed for the new NSF project.
Project Description:
Mason-Smithsonian Joint Project on Climate and Societal Modeling
NSF/CDI Type II: Cyber-Enabled Understanding of Complexity in Socio-Ecological Systems via Computational Thinking
Claudio Cioffi-Revilla (Principal Investigator)
J.Daniel Rogers (Co-Principal Investigator)
Paul Schopf (Co-Principal
Investigator)
Sean Luke (Co-Principal
Investigator)
This project will advance understanding of complexity in climate-societal dynamics by applying cyber-enabled multiagent systems models integrated with evolutionary computation algorithms. We will develop, validate, and analyze new computational agent-based models as transformative tools for simulating human societies spatially situated in regions with diverse ecosystems and explicit climate dynamics. The new suite of models built with MASON and ECJ will focus on two geographic regions where climate change has significant consequences for humans and ecosystems: Sub-Saharan Africa (over a billion people at high risk of displacement, disease, starvation) and the Arctic Circumboreal region (where the fastest ecological changes are now occurring with shifting patterns comparable to earlier major climate events). The project responds directly to the goals of the CDI Program of the US National Science Foundation and research recommendations of the NRC (2009) by forging interdisciplinary collaboration among anthropologists, political scientists, earth scientists, and computer scientists to advance the science of complex adaptive systems.
The core intellectual merit of the project is its contribution to
basic understanding of multiscale complexity in climate-society dynamics, by
creating a cyber-enabled integrated computational framework for modeling,
simulating, and exploring scenarios. Spatial multiagent systems will be created
that include climate dynamics as well as other natural hazards and stressors
with direct and indirect effects on social dynamics composed of households and
governance institutions.
Broader impacts from this project will include (1) new cross-disciplinary
understanding of climate-society dynamics, (2) new computational tools; (3) new
policy-relevant insights derived via scenario analysis with such tools; (4)
innovations in new advanced forms of hybrid computational modeling, enabling
new collaborations across disciplines; (5) new support for innovative forms of
teaching, training, and learning through computational modeling of complex
socio-ecological systems; (6) and multi-institutional opportunities for
students from underrepresented groups via classroom visits and museum exhibits.
Perpetual archiving of all electronic materials (code and data) will be
implemented through the Smithsonian data archiving initiative.
News Articles:
Mason Center to Model Social Consequences of Climate Change
If you’ve ever played a simulation game like World of Warcraft or SimCity, then you may begin to understand what the Center for Social Complexity does.
CSC, directed by Claudio Cioffi-Revilla, uses complex models of civilizations to try to predict possible outcomes of certain situations over many years. The center examines many ‘what ifs’ on very serious topics — from terrorism and conflict to humanitarian assistance and disaster relief. Their latest project, funded by the National Science Foundation, will look at how climate change may affect humans and societies in the next 100 years. [Read More]
Mason Center for Social Complexity Receives Grant to Model Social Consequences of Climate Change
A new grant to awarded George Mason University’s Center for Social Complexity will allow researchers to examine how climate change may affect humans and societies over the next 100 years.
The center, directed by Claudio Cioffi-Revilla, will look at two key areas of the world. The first, Sub-Saharan Africa, is where the largest number of people are currently at risk of displacement, diseases, or death due to climate change. The team will also look at the Circum-boreal regions—which includes Europe, the United States, and Canada—where the largest economies and most wealth are at risk due to the greatest physical changes in the biosphere.
“The Center for Social Complexity has investigated a range of topics that include terrorism and conflict, humanitarian assistance and disaster relief, and financial and economic market dynamics. This project examining the societal impacts of climate change is a perfect extension to our ongoing and previous work on complex crises,” says Cioffi-Revilla.
The center was awarded $1.6 million from the National Science Foundation. The four-year project is in its first year and is a joint effort with the Smithsonian National Museum of Natural History. [Read More]
Modeling and Simulation at the Mason Center for Social Complexity
At Mason, scientists at the Center for Social Complexity investigate and are gaining new, fundamental understanding on a range of topics that include terrorism and conflict, humanitarian assistance and disaster relief (“HA/DR”), financial and economic market dynamics, and societal impacts of climate change. Most of these and other modeling and simulation projects lead by the Mason Center in collaboration with other research groups (at Yale, the Smithsonian, and other universities) utilize the “MASON toolkit,” a multi-agent simulation system invented and maintained by our Associate Director, Professor Sean Luke of the Computer Science Department. MASON (Multi-Agent Simulator of Networks and Neighborhoods) is well-known in the multi-agent simulation community for its lively and international user community, as well as for its easy interface with the evolutionary computation system ECJ—also created by Sean Luke. Salient MASON features include an architecture that separates computation from visualization, a computational architecture based on data fields, an efficient scheduler, and RNG; replicability/reproducibility of results; and a publication-grade GIU facility for simulation graphics. [Read More]