Publications
How Simple is Simple Enough?: Military Modeling Case Studies
Proceedings of Agent 2005, , , 2005
Status: Published
Citations:
Cite: [bibtex]

Abstract: All models are abstractions of the real world. Determining the appropriate level of abstraction is a balancing of the complexity of the system being modeled, the available data resolution provided by data sources and subject matter experts, the needs of decision makers, and the limitations of the computational and developmental resources. Results from algorithmically linear, physical, closed-system simulations can often be improved by using higher-resolution inputs and by modeling lower-order phenomena. It is not as obvious; however, that ever-increasing resolution will necessarily improve the results from modeling complex systems. Two military course-of-action (COA) development case studies are examined to determine what level of model resolution is sufficient to provide significant insight into COA development. We examine the appropriate level of fidelity for modeling force structures and behaviors as well as the appropriate level of detail for modeling the terrain and physical environment. Methods for evaluating and comparing the results of varying model resolutions are presented.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Continuous Self-Modeling. Science 314, 1118 (2006). [Journal Page]

Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.

Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.

Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.

Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).