University of Cincinnati

COMPLEX ADAPTIVE SYSTEMS LABORATORY

Last updated on January 7, 2009

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INTRODUCTION AND MISSION

Complex adaptive systems (CAS) are all around us --- in the natural world as well as in engineered environments. Examples include traffic and communication networks, human organizations, markets, economies, cities, insect colonies, ecosystems, the nervous and immune systems, living organisms, and life itself.

The research conducted in the Complex Adaptive Systems Lab has two purposes:

To elucidate, and possibly control, the functioning of real complex adaptive systems by building mathematical and computational models.

To use insights obtained from existing complex systems in designing better, smarter, more efficient engineered systems.

The notion that complex adaptive systems can be studied as a class is based on the assumption that the same fundamental principles underlie all complex systems, ranging from neurosciene to economics, molecular biology to traffic engineering, ecology to the internet. The discovery and application of these principles is the goal of complex systems research.

Some of the fundamental attributes that characterize complex adaptive systems and distinguish them from systems that are merely "complicated" --- such as jet engines or computers --- are the following:

Large numbers of locally interacting, relatively simple but nonlinear components.

Spontaneous emergence of structures (e.g., groups, networks, etc.) and processes (e.g., competition, cooperation, communication, etc.) at multiple scales based on local interactions between components (self-organization).

Distribution of control, information acquisition, information processing, and representation over the entire system.

Adaptation of component behavior, resulting in implicit adaptation of the whole system.

The distributed, self-organized and multi-scale nature of complex systems makes them extremely scalable, robust, and flexible. These attributes allow them to survive, grow and evolve in changing environments without need for explicit repair or redesign. Since structure and process at large scales emerge spontaneously from small-scale interactions, there is no centralized control and no need to explicitly determine the state of the entire system. This avoids the combinatorial explosion seen in centrally controlled large-scale systems, and makes complex adaptive systems extremely scalable.

Internal Lab Network (authorization required)

Research Activities in the Complex Adaptive Systems Lab

Members of the Complex Adaptive Systems Lab

Publications

Interesting Links on Complex Systems

Interesting Links on Swarms, Sensor Networks and Ad-Hoc Wireless Networks

Last modified: August, 2006