Exploring Complexity in Science and Technology from a Santa Fe Institute Perspective - Faculty 2011
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Melanie Mitchell, Professor, Computer Science, Portland State University; External Professor and Science Board member, Santa Fe Institute. Melanie Mitchell received a Ph.D. in Computer Science from the University of Michigan in 1990. Since then she has held faculty or professional positions at the University of Michigan, the Santa Fe Institute, Los Alamos National Laboratory, the OGI School of Science and Engineering, and Portland State University.
Melanie has served as Director of the Santa Fe Institute’s Complex Systems Summer School; at Portland State University she teaches, among other courses, Exploring Complexity in Science and Technology.
Her major work is in the areas of analogical reasoning, complex systems, genetic algorithms and cellular automata, and her publications in those fields are frequently cited. She is the author of An Introduction to Genetic Algorithms, a widely known introductory book published by MIT Press in 1996. Her most recent book is Complexity: A Guided Tour named by Amazon.com as one of the 10 best science books of 2009.
Melanie Mitchell (Course Director), Professor, Computer Science, Portland State University; External Professor, Santa Fe Institute. Author, Complexity: A Guided Tour (Oxford).
W. Brian Arthur, Researcher, Intelligent Systems Lab, PARC; External Professor, Santa Fe Institute. Author, The Nature of Technology: What it is and How it Evolves (Simon & Schuster).
Aaron Clauset, Assistant Professor, Computer Science, University of Colorado, Boulder; former Santa Fe Institute Omidyar Fellow.
J. Doyne Farmer is a professor at the Santa Fe Institute. He has broad interests in complex systems, and has done research in dynamical systems theory, time series analysis and theoretical biology. At present his main interest is in developing quantitative theories for social evolution, in particular for financial markets (which provide an accurate record of decision making in a complex environment) and the evolution of new technologies (whose performance through time provides a quantitative record of human achievement). He was a founder of Prediction Company, a quantitative trading firm that was recently sold to the United Bank of Switzerland, and was their chief scientist from 1991 - 1999. During the eighties he worked at Los Alamos National Laboratory, where he was an Oppenheimer Fellow, founding the Complex Systems Group in the theoretical division. He began his career as part of the U.C. Santa Cruz Dynamical Systems Collective, a group of physics graduate students who did early research in what later came to be called "chaos theory". In his spare time during graduate school he led a group that designed and built the first wearable digital computers (which were used to beat the game of roulette). For popular press see The Newtonian Casino by Thomas Bass, Chaos by Jim Gleick, Complexity by Mitch Waldrup, and The Predictors by Thomas Bass.
David Krakauer, Professor and Chair of the Faculty, Santa Fe Institute. My research is concerned with the evolutionary history of information processing mechanisms in biology and culture, with an emphasis on robust information transmission, signaling dynamics and their role in constructing novel, higher level features. The research spans several levels of organization finding analogous processes in genetics, cell biology, microbiology and in organismal behavior and society. At the cellular level I have been interested in molecular processes, which rely on volatile, error-prone, asynchronous, mechanisms, which can be used as a basis for decision making and patterning. I also investigate how signaling interactions at higher levels, including microbial and organismal, are used to coordinate complex life cycles and social systems, and under what conditions we observe the emergence of proto-grammars. Much of this work is motivated by the search for 'noisy-design' principles in biology and culture emerging through evolution that span hierarchical structures. In addition to general principles there is a need to provide an explicit theory of evolutionary history, a theory accounting for those incompressible regularities revealed once the regular components have been subtracted.
Research projects includes work on the molecular logic of signaling pathways, the evolution of genome organization (redundancy, multiple encoding, quantization and compression), robust communication over networks, the evolution of distributed forms of biological information processing, dynamical memory systems, the logic of transmissible regulatory networks (such as virus life cycles) and the many ways in which organisms construct their environments (niche construction). Thinking about niche constructing niches provides us with a new perspective on the major evolutionary transitions.
Many of these areas are characterized by the need to encode heritable information (genetic, epigenetic, auto-catalytic or linguistic) at distinct levels of biological organization, where selection pressures are often independent or in conflict. Furthermore, components are noisy and degrade and interactions are typically diffusively coupled. At each level I ask how information is acquired, stored, transmitted, replicated, transformed and robustly encoded. With collaborators I am engaged in projects applying insights from biological information processing to electronic, engineered systems.
The big question that many of us are asking is what will evolutionary theory look like once it has become integrated with the sciences of information, and of course, what will these sciences then look like?
Uri Wilensky, Professor, Learning Sciences and Computer Science, Northwestern University; developer of the NetLogo agent-based modeling platform.