Scaling vs. Multiplying
Our understanding of the past is actually quite limited. We have a very scanty collection of data and we string it together with elaborate stories. The stories are not knowledge. They have little if any predictive value. We have no idea if anything we claim occurred at all. Even if we can make a model into which past data fits, there is no guarantee that it will project into the future.
Most of what we have are very small systems that we have some grasp of and we take those systems and scale them up for a larger task. What we found in the space race is the Russians multiplied a simple model until it was so complex that it failed. The Americans scaled a simple model thus maintaining the simplicity and they succeeded. I think that is the one of the secrets to success. Don’t multiply a system, scale it.
I think there is something relevant to that in nature as well. Nature has scales of order. You move from light, plasma, ion, gas, liquid, organic, solid, metallic, crystalline scales. Each of these scales has a maximum, a medium, a minimum–zero, one, infinity. The infinity state of one scale is the zero state of the next. Yet there are exceptions to this as well. Such as the triple point of water.
The change of phases can be considered in all phases of the life cycle. Discovery, definition, design, development, deployment, decoration and discharge are all phases. They are changes in state. Each has a zero, one and infinity.
If we look at binary numbers it can become apparent. We start off with a single digit, zero. Zero reaches its tipping point, one. It then proceeds to an infinite state where another digit is added, a second zero and the first digit becomes zero again as well. The next phase change requires the first digit to become one, then the second digit to become one before a new infinity state can be reached, a third digit at zero and the reset of the first and second digit to the zero state. But what are we really accomplishing?
It may seem we are increasing the size of the system, but in reality the system is remaining the same. What we are doing is not multiplying, but dividing the system. Consider the system as a line which we are continually multiplying by one half. We can even look at reproduction as the taking of two halves and uniting them into a new system. The new system is then continually halved. As the system halves it reaches higher and higher levels of order and becomes a “complex” organism. In other words, maturation of an organism is a continual series of phase changes. Evolution itself is a continual series of phase changes of organic molecules. Each major speciation being a successful transition from one phase to the next stable phase. Humanity is just an organic phase in an entropic sequence. We are just a niche in the mammalian dominance of the earth.
But let’s return to my description of phases as I see them. The first phase I describe, I call discovery. Discovery is the phase where we consider all the exceptions an existing model has and conceive of a new level of order that handles the exceptions. The second phase I described, I call definition. There are always more exceptions than we could ever handle. In order for a goal to be achievable we have to set a boundary on the exceptions we will address. Definition defines the boundary containing the exceptions to be handled by the current iteration of the system. The next phase I described is design. Design is the effort to make the system that handles the exceptions consistent and as simple as possible. The development phase follows by considering the available materials and their limitations and the ability to convert those materials into a physical representation of the system. The deployment stage is the assembly of the system from the available materials in the field. The duty stage is the usage of the system in the field. The decoration stage is the evaluation of the performance of the system, both its acceptions and exceptions. The discharge stage is the removal of the system from the field for replacement by the next iteration of the system.
1. Discovery
2. Definition
3. Design
4. Development
5. Deployment
6. Duty
7. Decoration
8. Discharge
Now, this seems like a simple sequential process and it is. However, it can be viewed in many ways and in each way it can be plotted as an ideal path. It fits just as well on a linear, exponential, logarithmic, algorithmic, state, statistical, parabolic, sine, radial or any other graph. It is an ideal plot, however it is only good for a discovered set of exceptions within a defined scope for which the system design provides affordance and the materials of the developed system can constrain and the deployment is skilled for and the duty is trained for and the decoration can account for and the discharge can maintain a history of. If in any case the granularity is wrong there will simply be too little detail, zero, or too much, infinity and the plot will not be perfect. All too often this is the case and the reason systems tend to fail more than they succeed.
Below I have taken Sun Tzu’s fundamental factors and given them a three part scale.
However, this model is not definitive, but simply stable. The vocabulary is manageable.
What I am getting at is two dimensional representations of systems are inherently flawed. Systems are composed of multi-dimensions. They have to be represented in a multi-dimensional space. Language has to be understood not as a chaotic structure, but as an attempt to describe this multi-dimensional space. Stable vocabularies can be created. The current problem with our language is its imprecision and inaccuracy. However a bigger problem is denigrating people who do not use language with precision and accuracy. Language is just a tool. Literacy is just a tool. Everyone has a tool they have mastered.
