I am co-organizing with Patrick Bitterman two AAG 2017 sessions on “Directions for Computational Modeling of Complex Systems with Big Data and Big Models.” We invite researchers interested in the following topics to participate in the AAG sessions on April 5, 2017 in AAG Annual Meeting in Boston, MA. We are looking forward to seeing you in Boston.
Complex adaptive systems are characterized by non-linear processes and feedbacks, alternative stable states, learning, and self-organization. Couplings among systems can be diverse, connecting systems across spatiotemporal scales, distance, and nature-society boundaries. Such systems are path dependent, with potential states differentiated by fine-scale tipping points that, when crossed, may result in trajectories leading to very different social or environmental outcomes. While advancements in modeling have provided methods for linking models of relatively simple behavior to environmental patterns, less progress has been made in scaling models to more realistic simulations and understanding the root causes of alternative system trajectories. Regardless of paradigmatic framing (e.g., resilience, sustainability, or vulnerability), there is a need to better understand the role of local context, system structure, and disturbance characteristics in shaping possible futures.
In an age of growing (if not always “big”) data, advancements in distributed sensor networks, ubiquitous computing, and data analytics have potential to provide new insights into the structure and function of complex systems. These data may assist model design, aid in distinguishing among alternative states, and help characterize potential system trajectories through an evolving state space. Finally, high velocity simulation data provide a starting point for visualizing connections among systems and uncertain future states.
We are particularly interested in papers that employ novel approaches with advanced computational techniques and new types of data to model, analyze, or visualize complex adaptive systems, addressing uncertainty in system outcomes, and/or integrating context.
Possible topics include (but are not limited to):
• Multi-scale interactions in complex systems
• Spatiotemporal analysis methods
• Validation of model outputs
• Model and system uncertainty
• Modeling context in coupled systems
• Models of individual or institutional adaptability in complex systems
• Data-enabled simulation models (which Fotheringham and others might term “big models”)
• Scaling relations in complex adaptive human-environment systems
• Visual representations of model processes and outputs
Session I: Directions for Computational Modeling of Complex Systems with Big Data and Big Models I
Wednesday, 4/5/2017, from 8:00 AM – 9:40 AM in Hyannis, Marriott, 4th Floor
8:00 AM: Sean C. Ahearn (Hunter College – City University), Exploring Patterns of Interaction in Movement of Tigers (Panthera tigris)
8:20 AM: Guangzhao Chen (Sun Yat-sen University), Coupling Cellular Automata and Cyberspace Data for Modeling Large Scale Urban Land-Use Dynamics
8:40 AM: Alex Kara (University of Cincinnati), Canals, Collapse, and Causation: Applying Agent-Based Modeling towards Understanding the Ancient Maya’s Terminal Classic Collapse
9:00 AM: Renia Kagkou (Harvard University), Understanding Urbanization Through Food Systems: An exploration based on land use modeling and simulation
9:20 AM: Victor Pena-Guillen (University of Tsukuba), An Agent-Based Model on the Improvement of Informal Houses at City Block Scale in Lima
Session II: Directions for Computational Modeling of Complex Systems with Big Data and Big Models II
Wednesday, 4/5/2017, from 10:00 AM – 11:40 AM in Hyannis, Marriott, 4th Floor
10:00 AM: Herman Geyer (CRUISE), Determining the Micro-Effects of Dimensionality on Agent Mobility in Polycentric City Regions using Fractal Scaling
10:20 AM: Arnaud Josephus Temme (Kansas State University), Reconciling the Robustness of Mathematical Models with the Complexity of Real, Historical Landscapes
10:40 AM: April Morton (Oak Ridge National Laboratory), A Monte-Carlo Simulation Approach for Estimating and Quantifying the Uncertainty of United States Worker Population Distributions and Journey to Work Travel Flows by Demographic Category
11:00 AM: Ling Xue (Peking University), Non-equilibrium Dynamics of Spatial Evolution of Urban and Regional System in China
Sponsoring AAG Specialty Groups: Geographic Information Science and Systems Specialty Group; Graduate Student Affinity Group