George Mobus' Research Interests

Multi-disciplinary Research Areas

Though my day job remains working in Computer Science & Engineering, my range of interests in research topics is quite broad. Very recently I have begun to consolidate my research interests in systems science in these topics into an integrated concept I am calling System Language.

Over the years I have pursued research in these areas:

• Energy Systems and Biophysical Economics - The true basis of wealth production and the limits to growth
• General Systems Science - Identifying and categorizing the major principles of systems that allow us to build better models
• Autonomous Adaptive Agents - Robots that can learn aspects of their environments that support or threaten their missions
• Global Issues - Background investigations in global warming, resource depletion, population (ecological footprint), biodiversity losses, political and economic ideologies, and the threats these pose to the future of humanity and the natural world
• Sapience: The Brain Science Behind Intelligence and Wisdom - Exploring the background of how the prefrontal cortex is involved in decision making and especially the subconscious components of judgement and intuitions that shape our rational thoughts more than we realize
• Hierarchical Cybernetic Systems: Brains and Management - How the science of cybernetics can be expanded to understand operational level, coordination level, and strategic level decision making and system sustainability
• Evolution and Evolvability of Systems - How auto-organization, emergence, and selection operate to produce more complexity in systems so long as free energy is available
• Modelling All of the Above - Looking for a modelling language that would allow building models of all of these kinds of systems/phenomena in a more natural way

I had known for a long time that all of these are related in the sense that they are united by systems science. The textbook that Mike Kalton and I wrote consolidates numerous topics that are systems related, such as network theory, complexity theory, evolution, and so on, under one over-arching umbrella, systems science writ large. We found that the principles outlined in the book can be used to apply all of these topics to any, but especially complex, adaptive and evolvable (CAES) systems to gain deeper understanding.

Last summer, in preparing my talk for an international conference on systems science, the theme of which was “Governing in the Anthropocene”, an idea occurred to me that what I had actually been doing, working in all of these various topics, was looking for a common language that could be used to describe the functional and structural nature of systems. It further occurred to me that this language was the same one that our brains use internally (subconsciously) to organize our perceptions and conceptions of the world — to guide our thinking.

Introducing System Language

System language is a computational construct that operates in brains to construct models of other systems — one system, a brain, modelling other systems in the world. Brains have evolved to contain more elaborate models of an organism's environment. Vertebrate brains have a capacity to learn models from experience. That capacity can be shown to increase exponentially as mammals and birds evolved, and finally as primates evolved into us. All brains model other systems that are of survival importance. The human brain can model all systems regardless of their relation to our immediate survival potential.

The language contains the usual pragmatics (context), semantics (aboutness), and syntax (grammar) of other languages, both natural and artificial. But its lexicon is a matter of the media in which it operates. Within our brains the symbols are neuronal networks, their firing rates and synchronization and their affective outputs to muscles and hormonal systems of the body. In the human brain we can bring to consciousness images of systems (like objects and situations) for manipulation in, for example, “What-if” games, which is exactly what we do when we simulate some phenomenon in a computer-based model.

My current research, then, comes down to developing a computer-based lexicon (e.g. data structures and algorithms) to fit a systems-based syntax (e.g. input flows - processes of conversion - output flows), and semantics. All of this to be placed within the pragmatics of a hierarchically organized information processing system embedded within and managing “The System of Interest” within a non-stationary, evolving environment. In other words, with this language we should be able to construct models of the world as it is, not just to obtain a small sample of the dynamics of a phenomenon (the usual output of mathematical and most simulation models), but to view the evolving history of a whole complex system situated in an environment.

With this language I expect to be able to apply it as a tool with which to do systems analysis on many of the subject areas mentioned above. I expect to be able to construct models of whole systems in which we can let the environment undergo non-stationary changes and see how the system of interest evolves in response. We can build very complex systems involving adaptive agents as decision makers in a complete hierarchical cybernetic management systems. My hypothesis is that the language of systems covers structural and functional aspects of every kind of system we can discover. That is why, I posit, we humans are able to recognize the organization in the world, why we were and are able to do science itself. Our internal system language predisposes us to see the systems “out there” and understand them because we are a system using the language of systems ourselves.

The current work is focused on developing a formal description of the language (using traditional computer language formalisms). The next phase will involve building an interpreter and runtime engine (evolvability requires that the program written in this language be able to modify itself at runtime).

If this project proves successful we will be able to analyze and model whole complex systems in ways never before possible. It will provide researchers with a kind of informational microscope with which to “see” systems details of structure and function in unprecedented clarity. I should help researchers into issues like climate change and its ecological/sociological impacts develop anticipated scenarios in a search for solutions to mankind's greatest challenges. Wish me luck.