Social Networks and Innovation Conference Presenter Michael J. North
Michael J. North
Michael J. North, MBA, Ph.D. is the Deputy Director of the Center for Complex Adaptive Agent Systems Simulation within the Decision and Information Sciences Division of Argonne National Laboratory. He is also a Senior Fellow in the joint Computation Institute of the University of Chicago and Argonne
Dr. North holds ten college degrees, including a Ph.D. in Computer Science from the Illinois Institute of Technology. In addition, Dr. North is a Project Management Institute certified Project Management Professional, an Institute of Electrical and Electronics Engineers Computer Society Certified Software Development Professional, and is also certified by the American Society for Quality in software development processes. Dr. North has over 20 years of experience performing advanced modeling for private industry including Pfizer, Procter & Gamble, and GE Healthcare; government including the U.S. Department of Defense and the U.S. Department of Energy; international agencies including the World Bank and the International Atomic Energy Agency; and academic research agencies including the U.S. National Science Foundation and the U.S. National Institutes of Health. Dr. North’s research specialties are agent-based modeling, software project management, software development, and high performance computing. Dr. North is the lead developer and is a principal investigator for the widely used free and open source Repast agent-based modeling suite (repast.sourceforge.net). Dr. North has authored or co-authored over 100 books, edited volumes, book chapters, journal articles, and conference papers.
“Complex Adaptive Systems Modeling of Dynamic Social Networks”. Dynamic social networks are complex adaptive systems composed of interacting agents. Agent-based modeling is a powerful computational method for studying such systems. This talk will introduce complex adaptive systems methods, describe agent-based modeling tools, and briefly discuss how social network researchers can apply these approaches to their research.