Introduction
The idea for this article, my musing, erupted from two very different sources of inspiration; the first being from the intellectual autobiography by Colin McGinn, ‘The Making of a Philosopher’; while the second is from the fact that I am about to begin studying System Dynamics in my final year at university.
In his autobiography McGinn talks about an idea that has seriously captivated me. He calls it CALM: Combinatorial Atomism with Lawlike Mappings. As its name eludes, the method essentially deals with understanding and conceptualizing entities in question by having them decomposed into atomic elements in order to articulate their mode of arrangement, then are recomposed to ultimately derive the myriad of complex objects found in nature. Note that he defines natural entities as complex systems of interacting parts that evolve over time as a result of various causal influences.
Take these two polar examples for instance (which I have attempted to structure in terms of Entity, Atoms, Laws):
Example #1:
- Entity: physical inanimate object
- Atoms: spatial complexes made of molecules, atoms, and quarks etc.
- Laws: physical forces of nature
(fig 0)
Example #2:
- Entity: Language
- Atoms: sentences, phrases, words
- Laws: grammatical rules
(fig 2)
McGinn references this method of thinking to deduce that philosophy is a condition of ‘terminal puzzlement’ and that one must come to terms with the fact that ignorance is an inseparable part of the human condition — the consequence of this ‘cognitive limit’. More generally, if something conforms to CALM principles we can understand it; else we cannot understand what does not. The former I vigorously believe in, the latter I am still cautiously rationally digesting. Nevertheless, the beauty in this method of thinking is its utter simplicity and impeccably intuitive ability to form structure to otherwise abstract concepts. Which without a doubt reminded me of my readings regarding systems.
Systems theory is an interdisciplinary study of systems and their parts. A system is a cohesive group of interdependent parts that is bounded by space and time, and is influenced by its environment to form a complex whole. It can be defined by its structure and purpose which is expressed by its function. The goal of systems inquiry is to undertake knowledgeable action through the domains of: ‘philosophy’ and ‘theory’ as knowledge, and ‘method’ and ‘application’ as action.
The breakdown of the four domains is as follows:
- Philosophy: the ontology, epistemology, and axiology of systems
- Theory: a set of interrelated concepts and principles applying to all systems
- Methodology: the set of models, strategies, methods, and tools that instrumentalize systems theory and philosophy
- Application: the application and interaction of the domains
What you can clearly see here is the intersection between the CALM method and Systems thinking. I will apply the the CALM method to an arbitrary system:
Example #3:
- Entity: system
- Atoms: parts of the system
- Law: the specific theory regarding the synergy of the parts of the system to become a whole
According to McGinn’s deductions if a system cannot be fitted into the format of Example #3, then it cannot be understood. However, in my humble opinion, total decomposition and recomposition is not possible either (which I do not think McGinn implied). Therefore with that assumption cleared, we will never truly understand everything entirely. Albeit this crude reality, it is needless to say that one does not need total understanding to make use of something. I can drive a car without ever needing to know how the combustion engine nor the electric steering works to fulfill that goal; on the contrary that knowledge would be useful in fulfilling another goal such as in engineering a new vehicle. A functioning knowledge of thermodynamics and mechanics helps but is not strictly necessary (here knowledge of thermodynamics and mechanics are likened to facets of total/complete/perfect understanding). With that said, it follows that a level of understanding exists that sufficiently achieves a declared goal. Systems thinking seeks to apply, the CALM method seeks to understand. This is probably the most distinguishable factor between these two ideas, the parts that do not lie in the intersection.
I can therefore state that there are different levels of zooming in — scopes of detail we can either accept or reject from our understanding to bring in the calm. One can therefore build the philosophy and theory by defining the sufficient level of understanding required to achieve a declared goal, and then designate an optimized methodology to efficiently achieve it. Meanwhile, It is imperative to realize that there are different approaches such as system dynamics, by which one can decompose an entity/system in order to understand then eventually apply that understanding across the board. With the aforementioned clarification, developing a method of understanding and thinking is possible, provided that a level of ignorance is fundamentally accepted by the seeker of that understanding; complementing a level of understanding that is sufficient to achieve the function.
Developments & Investigations
The synthesis of all of what was investigated can be aptly summarized in a series of premature numbered steps:
- Observe or conceptualize a system.
- Decompose by atomizing to a manageable level to create parts.
- Analyze the mode of arrangement of the parts and laws concerning them
- Recompose atoms by building the philosophy and theory based on the sufficient level of understanding required to achieve a declared goal.
- Apply by designating an optimized methodology to efficiently achieve it
While the steps being developed above do bring the semblance of order and structure, it is the devil lurking in the details that must be brought to light. These details take the form of questions:
- How far do you atomize?
- When do you stop atomizing?
- What is a manageable level?
- How complex can your system be?
- How to know if your system works or not?
- Which approaches to use and when to use them?
- How are the laws, relationships, and interdependencies defined?
- Is everything a system?
One way to find answers to the variety of questions posed above, is to identify exactly what is a system, part, law, and approach.
The Identity of: Systems, Parts, Properties, Laws and Approaches
Systems
A system is an entity made up of a group of interacting, interdependent interacting parts that form a complex whole (and evolve over time).
Evolution of a system:
- Dynamical system: is a systems that evolve over time
- Continuous: a system that evolves smoothly
- Strobophopic: a system that evolves in a discrete manner
Hypothetically, anything can be a system if it can be broken up into parts, then recomposed. There is also no limit to the complexity of a system. Different approaches have different means to deal with complexity and thus the gauge of complexity is somewhat managed.
Parts
A part is an atomic element that interacts with other parts in a system with a specifically articulated mode of arrangement so as when recomposed it can form a complex whole.
Interactions between parts are bi-directional and consequently define the relationship and dependencies.
Properties
A system can possess multiple properties, such as:
- Boundaries: barriers that define a system and can distinguish a system from others
- Equifinality: the ways a system can reach the same goal/function through different paths
- Open/Closed: a state of a system in relation to its environment. Open allows for flow of information, energy, and/or matter between the system and environment. Closed does not allow this flow- hence isolating the system from its environment.
- Micro/Macro/Exo/Meso/Chrono
- Isomorphism: features shared across systems
Laws
A law is a set of principles or rules that describes a phenomena between the parts but distinguishes between phenomena that occur by chance.
It also describes phenomena between states of the system as it evolves over time.
A state is a set of variables used to describe the behaviour of the system at a particular time. This is also called ‘degrees of freedom’.
Laws are therefore either:
- Deterministic: past and future of the system is known, reversible
- Nondeterministic/Stochastic/Probabilistic: future is not known with certainty, nonreversible
Suppose a system is spatiotemporal and is a dynamical system; it possesses the following characteristics: states and degrees of freedom.
Here’s a previous article I’ve written that explores dynamical laws:Interpretations of Dynamical Laws
THE BEGINNING OF ALL LAWSmedium.com
The relationship between these states are also governed by laws.
Approaches
An approach is a way of dealing with the system and the whole process of decomposing and recomposing. It defines the how, the why, and how far to decompose. It also lends a framework on how to structure the laws.
To illustrate, I will recall a subject I am required to study to complete my mechanical engineering degree called ‘Theory of Machines’. Here we learnt about the mechanical elements, parts, links, mechanisms, and degrees of freedom. In essence, we studied mechanical objects and systems and what they are. Not how they interact functionally and behaviourally which is done by cybernetics or system dynamics. The way of investigating the how is by either of the aforementioned approaches which vary in philosophy and theory, then ultimately by methodology.
Conclusion
Drawing on McGinn’s CALM method to assure understanding , the investigation and identification done throughout this article regarding systems, it is time to put it all of it together with the loosely stated steps that were being developed.
Recreated Steps for Systems Thinking
Step 0: Observation and/or conceptualization of the entity in question is devised.
Step 1: Declare a goal/function of this system’s inquiry.
Step 2: Thus a manageable level by which to atomize can be determined by defining the sufficient level of understanding required to achieve the declared goal. This also implies a sufficient level of ignorance that must be accepted.
Step 3: Decompose by atomizing to the manageable level to create well-defined parts.
Step 4: Analyze the mode of arrangement of the parts and laws concerning them. Here is where the choice of the approach to be used is made to analyze the functioning and behaviour of these parts.
Step 5: Recompose atoms by building the philosophy and theory which is derived from the approach. This is where the parts become the complex whole once again, but in the resolution of the sufficient level of understanding that was defined in step 2.
Step 6: Apply by designating an optimized methodology to efficiently achieve the declared goal/function of the system.
Step 7: Borrowing the conclusions from CALM conjecture, that if an entity in question conforms with the principles of the CALM method then we can understand it; else we cannot understand what does not; therefore we get a starting point to what can and cannot be understood and therefore what can or cannot be decomposed, analyzed, then recomposed. This keeps our ignorance in check by allowing us to recognize when huge unexplainable leaps are made between ideas.
In conclusion, in this article I’ve tried to relate what I’ve read on the CALM method as well as systems theory and thinking to casually fuse a framework/model that allows us to know when and how to understand a wide scope of entities. I hope this was a fun and interesting read. I’d love to know if this mental model was of any use. Any and all comments are more than welcome!