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Keynote Speakers
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NAACSOS Conference 2006
Morphology and Modularity: ABM Approaches to Biomedical Modeling
Dr. Gary An, M.D.
Director of the Burn Intensive Care Unit
Cook County Hospital
Chicago, IL
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Dr. Gary An is a trauma surgeon in Chicago currently working as the Director of the Burn Intensive Care Unit at Cook County Hospital. He has been interested in the application of complex systems analysis to sepsis and inflammation since 1999, and worked primarily with using agent based modeling to create mechanistic models of various aspects of the acute inflammatory response. He is a founding member of the Society of Complexity in Acute Illness, and is also a faculty member of the Center for Inflammation and Regenerative Modeling at the McGowan Institute of Regenerative Medicine at the University of Pittsburgh. He has been a member of the Swarm community since 1999.
Keynote Abstract :
Prior to the advent of molecular biology a primary focus of traditional biology was classification based on observed morphological and structural differences. The melding of biochemistry, cell biology and genetics in the mid-20th century led to a switch in the emphasis of biology towards more formal analysis along the lines of the Newtonian physics-based reductionist paradigm. While this approach has been, and continues to be, extremely successful in the acquisition of mounds of detailed information, it is now recognized that there are significant limitations to this method. One is the sheer volume of information that needs to be analyzed and integrated, another is the recognition of the importance of systems-level approaches needed to re-integrate the connectivity lost in the reductionist process. In the medical field this has led to difficulty in translating the results of basic science research into effective clinical regimens. I believe that Agent Based Modeling is particularly well suited to this translational role. The traditional emphasis on classification/morphology is directly applicable to ABM construction, and the inherent modularity of ABMs makes them good platforms for the collaborative efforts necessary in a widely dispersed and compartmentalized research community. I present here an ABM framework with a modular, multi-scale approach that will hopefully become a prototype platform for a community-wide, open-source, dynamic-functional data base of biomedical information.
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Robust Inference in Computational Social Science
Steve Bankes
Chief Technology Officer
Evolving Logic/RAND
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Steve Bankes is Chief Technology Officer for Evolving Logic Inc., a software firm developing decision support systems for complex and deeply uncertain problems. He is also Professor of Information Science at the RAND Graduate School, where he teaches courses in Agent Based Modeling and Artificial Societies, and Policy Analysis for Complex Systems.
Dr. Bankes is the originator of the computational research methodologies known as “exploratory modeling”, which provide a basis for studying complex, adaptive, and incompletely understood systems through computational experiments. And he is the main designer of the Computer Assisted Reasoning system (CARs), a technology that facilitates robust decision support for many important problems in government and industry.
Dr. Bankes has over 50 publications in a variety of areas including computer science, operations research, global climate policy, sustainable development, computational social science, and neurophysiology. He received his B.S in Engineering from Caltech, and a Ph.D. in Computer Science from the University of Colorado. He holds two patents for software technology with two others pending. He is a r ecipient of the Barchi Prize, awarded by the Military Operations Research Society. He serves on the board of directors for the Center for Computational Social Science and the Center for Governance, and is a member of the Center for the Study of the Origins and Evolution of Life, all at UCLA.
His current research interests includes computational science, modeling and simulation theory and practice, complex adaptive systems, machine learning and self-organizing systems, and agent based simulation of social systems.
Keynote Abstract :
The world faces profound social, economic, environmental, and technological transitions. How we choose to meet our challenges – stemming global terror, halting the spread of AIDS and other infectious diseases, achieving sustainable development, managing new genetic technologies, etc. -- will resonate throughout the 21st century. Models of social-political behavior can be informative in understanding these problems, and can be used to anticipate possible future developments and the possible implications of contemplated actions. Their value can be greatly enhanced by the use of robust inference in the design and analysis of computational experiments using them. Techniques have proven their utility including the use of co-evolutionary mechanisms to seek in parallel robust conclusions and cases that maximally stress them, and the use of machine learning techniques to infer human interpretable generalizations from the results of thousands or millions of experiments. These methods harness computation not to solve the intractable problem of predicting the long-term future, but instead to enable a fundamentally different, more sensible question: Given what we know today, how should we act to best shape the future to our liking? Our greatest potential influence for shaping the future may often be precisely over those time scales where our gaze is most dim.
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Signs, Language, Culture, Meaning
Les Gasser
Professor
University of Illinois at Urbana-Champaign
Urbana-Champaign, IL |
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Les Gasser is a Professor of Library and Information Science, with joint appointments in Computer Science and Computational Science/Engineering, at the University of Illinois at Urbana-Champaign. He received his B.A. in English Literature, Magna cum Laude, from the University of Massachusetts in 1976, and his M.S. and Ph.D. degrees in Computer Science from the University of California, Irvine, in 1978 and 1984. His research and teaching covers topics in Social Informatics and Multi-Agent Systems, specifically open source software, electronic games, language evolution, and adaptive information systems.
Dr. Gasser has published over seventy technical papers and five books in Social Informatics and Multi-Agent Systems. Prior to joining the University of Illinois, he was at the University of Southern California, and has held visiting faculty posts at the University of Paris and the Ecole des Mines de Paris. He is currently Past-President of the International Foundation for Multi-Agent Systems (IFMAS), and was one of the founders of that field. From 1996 to 1998 he directed the Program on Computation and Social Systems in the Computer Science Directorate of the National Science Foundation. He has significant project management, leadership, and entrepreneurial experience including co-directing a $10M, industry-university effort to develop theories, technology and methods for computer-supported design of high-performance organizations, later commercialized. He has also been a principal or advisor with a number of technology startup firms. He and his wife Terry have two children, and he is an avid hiker, bicyclist, cook, and musician.
Keynote Abstract:
Computational modeling has now been productively applied to many organizational and social phenomena. A foundation of social organization, under an information processing perspective, is the creation of meaningful, structured, and collective representation and language systems. This talk surveys recent perspectives and results from our group at Illinois and elsewhere on mathematical and computational approaches to issues including: conditions for creation of collective representations and language in general; emergence of specific languages; how languages can change, stabilize, and incorporate (or resist) innovation; and relationships between social network structure, cultural learning, and language features.
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