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Mission Statement

The C3S2 mission is to encourage and facilitate research and educational opportunities in the area of complex, nonlinear, dynamical and adaptive systems. The C3S2 will foster collaboration from an interdisciplinary group of researchers to address important problems from a wide range of scientific, technological and engineering disciplines for the advancement of technology and humanity.


In brief, complex systems science involves the study of how many elements develop behaviors that are beyond those behaviors possible by considering the individual elements alone. While the behavior of each individual component of a system in isolation may support intricate dynamics, together the individual components interact to support group behaviors and system dynamics well beyond those possible from individual components alone.

Complex systems science is a rapidly growing and emerging field that is inherently interdisciplinary. It can be applied to a wide variety of fields including biology, medicine and cognitive science, mechanical, chemical, electrical, and civil engineering, physics and astronomy, economics and social sciences. The future of research in these fields lies in understanding not just the isolated components of a given system, but the manner in which the individual components interact to produce “emergent” group behavior.

In contrast to “data mining” or “big data”, where a primary focus is to understand hidden patterns or structure in large data sets, complex systems science attempts to identify “causality” and uncover “universality” that exists in large scale systems. Causality and universality are due to peer and hierarchical interactions, patterns, and scaling of individual system components. Universality has been observed across a wide range of fields such as brain science, insect swarming, social science, and fluid dynamics.

Key to the advancement of complex systems science is the development and use of mathematical tools designed to understand the resultant outcome of group behaviors that are not evident when studying the behavior individual elements alone. Mathematical tools for complex systems science are drawn from the following fields:

  • Information dynamics - the study of interaction of elements and the information flow between elements. Of particular interest is the minimum information needed to produce an outcome of important behaviors.

  • Algorithmic complexity - In contrast to information dynamics and entropy of evolving systems is the concept of algorithmic complexity, Komolgorov complexity, and the concept of minimality of description, as a contrast that intricate behavior is often opposite to simplicity of design.

  • Structure and dynamics on networks, as a large number of interacting parts can give rise to behaviors that emerge from the group interactions and not implicit in any one element. Consider the collective behaviors and capabilities of an ant swarm, which is clearly not understood in terms of the behaviors of the parts. Considering networks brings in the mathematics of graph theory, but well beyond this when understanding dynamics on networks, comes complexity theory.

  • Criticality and scaling, modeling of random networks, the implications of critical phenomena to complexity, and the recent approaches to evolutionary dynamics are all part of this field. As such, understanding interactions from food webs to economies all have a universality that can be understood in terms of the science that includes hierarchical interactions. It is the characterization of such universalities that lead to complex systems as a unifying field across such disciplines.

  • Technical details and the tool-sets include areas of dynamical systems and chaos theory, network theory and graph theory, information theory, thermodynamics and statistical mechanics, cellular automata, information theory, activated processes including glasses, fractals, scaling and renormalization.

Specific applications of complex systems science include:

  • Complex and networked and multi-scaled processes

  • Nonlinear and chaotic systems

  • Information dynamics and causal inference

  • Systems science

  • Medicine and cognitive science,

  • Biology, systems biology, and mathematical epidemiology

  • Neuronal networks and the brain, extreme events and earthquakes as well as cardiac arrhythmia and, more fundamentally, nonlinear chemical reaction kinetics

  • Mechanical, chemical, electrical, and civil engineering,

  • Fluid dynamics, and turbulent systems, oceanography and meteorology, including questions of transport, modeling and sensing

  • Physics and astronomy,

  • Economics and social sciences

  • Dynamical systems and control systems

  • Sensor networks, ad hoc networks, and control coordination systems

  • Decision science, social systems, and science of human groups

  • Adaptive and designed systems engineering

  • Particular examples are protein interaction networks, niche development in microbial communities, disease and rumor spreading, and dynamics of innovations

  • Critical and rare events, mechanisms and detection. Robustness and fragility.

Center Motivation and Benefits to Clarkson

There exists and opportunity to “expand this collaboration inward” to include colleagues at Clarkson in various academic departments. Doing so would provide an opportunity for Clarkson to make a greater impact in the field thus enhancing our research reputation. A strength at Clarkson is that our culture, our size, and our application oriented philosophy all celebrate the idea that working together across fields is what we do, and what we do well. This center is specifically advanced in this spirit. We will offer a venue, and a means to bring us together in groupings and combinations to foster collaborations in cutting edge research that may not otherwise take place, or even occur to specialists in individual disciplines. Together, our work can emerge strengthened as a group. Theory, feeds application and application feeds theory, and together we benefit.

Center Activities

Foremost, the job of the center is match making, meaning bringing together interested people, faculty and students to discuss problems in complexity science that bring together complementary skills and interests.

Therefore, we propose the following Center activities:

  • Topical workshops and conferences - An important activity of our center will be outreach to the general scientific community.

  • An internal and external lecture series to take place each semester.

  • A student gathering club, from the involved students across disciplines. A club-like environment affiliated with the center enhances the activity. Encouragement under the banner of C3S2, encouragement of good food, (or good enough food!), interesting problems, and space, should fuel a talking forum between students, and here we are advocating this to occur in the absence of faculty “intervention” since sometimes a faculty in the room dramatically changes the nature of student brain storms.

Who we are and what we are

This center will be formed with a model of a few core faculty members who will participate in the formal activities and tasks, and then affiliate faculty members with related interests who wish to benefit from affiliation but otherwise may be less specifically committed.

Core Faculty will be asked to be actively involve in organizing Center activities including identification of opportunities, development of reporting materials, recruiting other faculty members and student participants, and suggesting educational dimensions. Affiliate faculty are those who have interest in the general areas described by this center but may not have time or means to become specifically regularly involved.