Complex patterns are common throughout nature, from the distribution of the galaxies in the Universe to the organization of neurons in the human brain. It is generally assumed that such complex structure must have a complex cause, but it may be that the patterns spontaneously arise through the repeated application of simple rules. This workshop will provide examples of self-organization in nature and will describe six simple computer models that can replicate the features of these patterns. The models typically produce fractal spatial structure and chaotic temporal dynamics characterized by power laws and unpredictability, even when the models are simple and purely deterministic. Workshop participants will be challenged to propose simple models of complex systems that potentially exhibit self-organization in fields as diverse as physics, ecology, political science, economics, sociology, and art.

**Julien Clinton Sprott, Ph.D.** received his B.S. in physics from the Massachusetts Institute of Technology in 1964 and his Ph.D. in physics from the University of Wisconsin in 1969. He worked at the Oak Ridge National Laboratory for several years before returning to the University of Wisconsin to join the physics faculty in 1973. In 2008, he became an Emeritus Professor of Physics. His research has been primarily in the area of experimental plasma physics and controlled nuclear fusion. In 1989 his interests turned to nonlinear dynamics, chaos, fractals, and complexity. He has authored or coauthored over 300 scientific papers in these and related fields. He has authored or coauthored six books, 25 hours of physics educational videos, and four commercial software packages. He received the John Glover Award from Dickinson College, the Van Hise Outreach Award for Excellence in Teaching from the University of Wisconsin-Madison, and a Lifetime Achievement Award from the Wisconsin Association of Physics Teachers for his work in public science education.

This workshop introduces participants to the network-oriented, agent-based model developed by Carnegie Mellon University to study organization and group behavior. The model is embodied in a software program called Construct. Construct simulates and models groups and organizations as complex social-technical systems and captures the variability in human and organizational factors; these characteristics are modeled as multi-level social networks. The nonlinearity of the model generates complex temporal behavior due to dynamic relationships among agents. These dynamic relationships are grounded in structuration theory which is the notion of construction and reconstruction of the social system through human interaction based on rules and resources. The changes in the social system are defined and analyzed through the lens of social and dynamics network analysis. These techniques have been used for analysis and consulting in industry (health care, aerospace, consulting, professional associations, financial), non-profit and emergency response (charity foundation, American Red Cross), higher education (universities), military (DARPA, ONR) and government (NSF, NASA). Participants will be introduced to the theory behind the model in a lecture-format and will be shown operational aspects of the simulation software in a demonstration-format.

**Dr. Terrill L. Frantz, Ed.D.** works in the Center for Computational Analysis of Social and Organizational Systems at Carnegie Mellon University, which brings together computer science, dynamic network analysis and the empirical study of complex socio-technical systems to develop a better understanding of the fundamental principles of organizing, coordinating, managing and destabilizing systems engaged in real tasks at the team, organizational or societal level. Dr. Frantz's primary research focuses on organizational merger integration and statistical analysis of social networks. He hold degrees from Pepperdine University (Ed.D. Organizational Change), New York University (MBA), and Drexel University (BS, Business Administration). He is currently studying for a Ph.D. in Information Science at Carnegie Mellon.

Markov chains can be used to model dynamical processes, analyze general time series, provide simulations of time series with the same properties as a given series, and for 1-d dynamical systems with a parameter, uncover the bifurcation structure. In this workshop, all of these topics will be discussed with examples. All participants will receive a CD with data and algorithms in MATLAB (m-files) to facilitate their own use of these methods.

**Stephen J. Merrill, Ph.D.** is Professor of Mathematics at Marquette University in Milwaukee. His research involves using mathematical models to answer biological, clinical, and theoretical problems. He has held visiting positions at the Lefschetz Center for Dynamical Systems (Brown University), the Santa Fe Institute, and Los Alamos National Laboratory. Dr. Merrill edits the Computational Modeling section of the Journal of Immunological Methods and serves on the Editorial Board of Nonlinear Dynamics, Psychology, and Life Sciences.

With an emphasis on dynamics of emergence, this workshop introduces a general theory and its methods, the nonlinear Model of Hierarchical Complexity, which accounts for and measures emergence. As a math-based formal theory, the Model applies to all information-organizing tasks, from simple organism behaviors to the most complex human behaviors. Its nonlinearly-increasing orders and discrete state transition steps result in an inherent power law, a fractal dimension of 1.37. Its theory and a breadth of applications are published in a 2008 special issue of World Futures: The Journal of General Evolution, 64(5-7), co-edited by Ross with Michael Commons, originator of the Model. The workshop is relevant to anyone who wants to analyze and measure behaviors, whether in consulting or research, whether quantitatively or qualitatively. Participants will apply the new skill to their own examples to reinforce learning and understand the broad applicability of the measure. Methods include presentation, individual and group exercises, reflection, and discussion.

**Sara Nora Ross, Ph.D.,** has been studying micro, meso, and macro transition dynamics of increasing complexity since the 1980s in individuals, groups, communities, and societies. With a keen interest in the applied and scientific dynamics of decision making, she discovered and continues to develop the measure of nested fractals of hierarchical complexity in ubiquitous phase transitions that account for emergence, integrating the micro- and macro-development of entities. She is founder and president of ARINA, publisher of the journal Integral Review: A Transdisciplinary and Transcultural Journal for New Thought, Research, and Praxis and The Integral Process for Working on Complex Issues. She teaches at Antioch University McGregor in Ohio.