Traditional Engineering seeks to predict in order to design systems according to a model. However, there are several problems that change constantly, making it difficult to make predictions. As a complement, one can done systems with adaptation, so that they can adapt to unpredictable changes of their environment. The concept of self-organization is used to build adaptive systems. Instead of a straightforward design of the system, the aim is to design the components of the system, so that they explore and find by themselves through their interactions solutions to ever changing problems. This requires to model more than one scale and their relationships.
For more information, visit the Self-Organizing Systems Lab.
To delve more into the topic of Complex Systems, you are invited to visit The Latin-American Node of FuturICT.
In addition, we offer the course on Adaptive Computing.
The knowledge generated here can be applied to the development of artificial systems that exhibit the properties of living systems, such as adaptation, learning, evolution, robustness, and self-organization.
Specifically, computational models of biological phenomena are developed, to improve their understanding. On the one hand, models of genetic networks (random Boolean networks) and their properties are studied. On the other hand, questions concerning the origin of life and the evolution of complexity are addressed.
Elements of a system can produce new information as they interact, i.e. information not present, derivable, or predictable from the description of the elements. For this reason, it becomes necessary to use the computer as a tool to make simulations and study exhaustively or statistically the behavior of such systems.