“Complexity science and chaos theory, as the study and analysis of nonlinear systems is known, is a very fascinating area of scientific research. Nonlinear systems, although deterministic, demonstrate a strange behavior, due to widely diverging outcomes, provoked by small variations in initial conditions or system parameters that lead to inobservance of long-term system-behavior prediction. This behavior is known as deterministic chaos (simply chaos). The interdisciplinary nature of complexity and chaos is a feature that provides scientists with a global theoretical tool. Complex systems give rise to emergent behaviors that lead to interesting phenomena in science, engineering, as well as social sciences.”

BETWEEN TWO ENGINEERING AGES: OF INFORMATION AND COMPLEX SYSTEMS, PROFESIONAL RESEARCH IS BASED ON A TEAM WORK!

 

            In its most general sense, engineering is turning an idea into a reality, creating and using tools to accomplish a task or fulfill a purpose. There is the appreciation that the story of engineering divides into five ages: gravity, heat, electromagnetism, information, and systems. The same specialists appreciate that we are in the age of information, which has turned now into the age of complex systems.

            Complexity describes objects with many interconnected parts and complex systems are a subfield of computer science, called computational complexity. Speaking of the concepts: complicated and complex we can say that it is easy to distinguish one from the other only at the extremes, but there is a middle ground where the distinction becomes unclear and arbitrary. The interactions of interest are non-linear and this non-linearity yields levels of organization and hierarchies. Complex systems exhibit several kinds of behavior such as: self-organization into patterns, chaotic behavior adaptive interaction.

            Analyzing complexity, we can ask how systems can generate perpetual novelty using limited resources. And besides many answers two concepts become important: laws and states. For example, for a system, the course over time appears chaotic rather than deterministic. Though the partial differential equations describing chaotic systems are fully deterministic, the presumed guarantees of determinism: similar starting conditions yield similar state trajectories— no longer hold!

            We must take in account a systemic approach because a system is more than the sum of its parts. It may exhibit adaptive, dynamic, goal-seeking, self-preserving, and sometimes evolutionary behavior. The least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior. Complex behaviors of systems often arise as the relative strengths of feedback loops shift, causing first one loop and then another to dominate behavior! What can we do?

            Today, many authors recommend: Listen to the “Wisdom of the Systems”, stay humble— Stay a Learner!

            Working with systems, often reminds me of how incomplete my mental models are, how complex the world is, and how much I don’t know! Sometimes, I think to defy the Disciplines because seeing systems whole requires even more than being “interdisciplinary”. Even so, we will contemplate the inevitable gaps between what we know, what we do, and why things go wrong. But, as we explore modern ideas of systems complexity, we will see that the gaps are filled by risk. Risk is at the heart of major engineering questions. How do we know what is safe? How safe is safe enough?

            The simplest way of minimizing the risk of failure of a physical ‘hard’ tool is to use a large safety factor, but some tools have become so very complex.     Engineers have started to work and manage the risks in layers or levels. Strictly specializations in IT&C, for example, pose new risks since the relationships between levels and, more importantly, between the ways of understanding of specialists in those levels, are not straightforward.

            Now, in the 21st century, we are entering into the age of systems with a potential for new risks through interdependencies we may not fully understand. For example, we now know that some (but not all) physical processes are chaotic, in the sense that, whilst they may appear to be reasonably simple, they are inherently difficult to predict. We have discovered that they may be very sensitive to very small differences in initial conditions and may contain points of instability where paths diverge.

            Energy, entropy, and exergy are examples of how the challenges of the 21st century require the engineering disciplines that are much better at integrating their expertise to find synergy. As a result, some engineers have begun to think differently – they use what many call ‘systems thinking’ and an engineering team is an example of a specific soft system.

            If we are to make and maintain highly reliable and sustainable complex systems, then we need more of our specialist engineers to be systems thinkers that can deal both with the detail and the big picture – a synergy from the integration of reductionism and holism.

            Speaking about our research activity, I can confess that it was influenced by scientific trends, by our professors, our colleges and in the last thirty years by our enthusiastic PhD students.

            Are our scientific activities in consensus with actual transitions from “the age of information to the age of (big) systems in engineering? Difficult to answer!

            But, from twenty years we promote the idea that engineering’s systems are nonlinear ones and usually can have chaotically behaviors and their associated processes need special studies’ methods. And these ideas are in consensus with the worldwide research trends in communications and information technology.