Thoughts on General Systemology
David Rousseau’s General Systemology has provided me with a powerful new set of frameworks for rigorously assessing the state and maturity of systems science as an academic discipline. I won’t be doing a full review of the book, but I’d like to highlight three important concepts that stood out to me.
We must be precise when we refer to a “General Systems Theory "(GST)
We can use a disciplinary knowledge base to compare the work and findings of different systems scientists in a structured manner
We can, and should strive to propose and test concrete scientific principles of systems
GST and GST*
Ludwig von Bertalanffy, father of General Systems Theory, used the term in two very different but related contexts.
On the one hand, he proposed the existence of GST in the “narrow sense.” GST in this context refers to “a theory encompassing the universal principles applying to systems in general.”
On the other hand, he called for the establishment of GST in the “broad sense.” GST in this context refers to the establishment of a new “meta-discipline” devoted to deriving and formulating general systems principles that could be used to empower all scientific disciplines dealing with systems.
Many in the systems science community are (rightfully) skeptical that a grand unifying theory exists or will be developed in our lifetimes. However, most believe that the pursuit of identifying general, universally applicable principles is a worthwhile endeavor.
David proposes that we formally distinguish between these two usages of the term. GST the unifying theory can be identified as GST*. GST the meta-discipline can be referred to as Systemology. I think this is a reasonable proposal and believe the systems science community would benefit from making some sort of formal distinction like this.
Systemology’s Knowledge Base
My favorite concept from the book was that of a knowledge base for systemology as a discipline. This knowledge base would give us a structured way to organize the various findings of systems scientists and compare them with each other.
David argues that developing a knowledge base could:
Provide a framework for making an inventory of current knowledge holdings
Be used to develop a classification framework for indexing disciplinary knowledge
Provide the foundation for a maturity model of the discipline
Serve as a guideline for knowledge development
Such a knowledge base would be of special value for nascent disciplines like systems science. It would “help put the work of different researchers into context relative to each other, identify connections that suggest opportunities for productive synthesis or collaboration, and support the formulation of strategically prioritized research agendas.”
By focusing our attention on the foundational questions, we can avoid wasting resources attempting “late stage” theory building, while the early stage theories needed to support them are still immature or non-existent.
I’ve had a strong intuition that many of the different grand theories, models, and approaches I’ve seen proposed by various members of the ISSS should be seen as complementary. Now I have a principled and reasoned way of thinking about this.
Three Scientific Principles
Finally, David provides three scientific principles of systems. While I’m not in a position to rigorously assess them or test their utility, I appreciate that they are presented in such a way that it’s easy to imagine that with the proper resources, one could go about testing their validity using empirical methods.
#1 The Conservation of Properties Principle
“Emergent properties are exactly paid for by submerged ones.”
This principle asserts that “the energy associated with an emergent property in system formation is exactly matched by the sum of the energies lost by the parts participating in that systemizing interaction.”
It provides an empirical standard for demonstrating that an observed system property is an emergent one.
#2 The Principle of Universal Interdependence
“System properties represent a balance between bottom-up emergence and outside-in submergence.”
This principle asserts that “systemic properties are determined by a balancing act between the bottom-up influence due to the parts and the outside-in influence of the super-systemic context.”
It suggests that modeling a system’s real potential requires looking not only at the system itself, but also at influences from the environment.
#3 The Principle of Complexity Dominance
“Complexity buffers autonomy.”
This principle asserts that “in systematizing interactions, complex parts pay proportionately less towards emergent properties of the whole than simpler parts do. The impact of submergence on a part is proportional to the complexity differential between the part and the whole.”
It implies that when modeling systems or planning systemic interventions, we must account for the relative complexity between a system of interest and its environment.
Donella Meadows’s Thinking in Systems helped me understand why society seems incapable of addressing systemic problems and introduced me to the wonderful world of systems.
George Mobus’s Principles of Systems Science and Systems Science: Theory Analysis, Modeling, and Design gave me a set of practical general principles and methodologies that I’ve been able to use to start analyzing a variety of systems of interest.
David Rousseau’s General Systemology feels like another important missing piece of the puzzle. It has provided me with a principled way of thinking about the general structure of academic disciplines, how disciplines tend to mature over time, and how the systems science community can learn from these lessons in order to accelerate the advancement of the discipline.