Ross Ashby, one of the pioneers of Cybernetics, started an essay with the following question:
I would like to start not at: How can we make a model?, but at the even more primitive question: Why make a model at all?
He came up with the following answer:
I would like then to start from the basic fact that every model of a real system is in one sense second-rate. Nothing can exceed, or even equal, the truth and accuracy of the real system itself. Every model is inferior, a distortion, a lie. Why then do we bother with models? Ultimately, I propose. we make models for their convenience.
To go further on this idea, we make models to come up with a way to describe “how things work?” This is done for us to also answer the question – what happens when… If there is no predictive or explanatory power, there is no use for the model. From a cybernetics standpoint, we are not interested in the “What is this thing?”, but the “What does this thing do?” We never try to completely understand a “system”. We understand it in chunks, the chunks that we are interested in. We construct a model in our heads that we call a “system” to make sense of how we think things work out in the world. We only care about certain specific interactions and its outcomes.
One of the main ideas that Ashby proposed was the idea of variety. Loosely put, variety is the number of available states a system has. For example, a switch has a variety of two – ON or OFF. A stop light has a variety of three (generally) – Red, Yellow or Green. As we increase the complexity, the variety also increases. The variety is dependent on the ability of the observer to discern them. A keen-eyed observer can discern a higher number of states for a phenomenon than another observer. Take the example of the great fictional characters, Sherlock Holmes and John Watson. Holmes is able to discern more variety than Watson, when they come upon a stranger. Holmes is able to tell the most amazing details about the stranger that Watson cannot. When we construct a model, the model lacks the original variety of the phenomenon we are modeling. This is important to keep in mind. The external variety is always much larger than the internal variety of the observer. The observer simply lacks the ability to tackle the extremely high amount of variety. To address this, the observer removes or attenuates the unwanted variety of the phenomenon and constructs a simpler model. For example, when we talk about a healthcare system, the model in our mind is pretty simple. One hospital, some doctors and patients etc. It does not include the millions of patients, the computer system, the cafeteria, the janitorial service etc. We only look at the variables that we are interested in.
Ashby explained this very well:
Another common aim that will have to be given up is that of attempting to “understand” the complex system; for if “understanding” a system means having available a model that is isomorphic with it, perhaps in one’s head, then when the complexity of the system exceeds the finite capacity of the scientist, the scientist can no longer understand the system—not in the sense in which he understands, say, the plumbing of his house, or some of the simple models that used to be described in elementary economics.
A crude depiction of model-making is shown below. The observer has chosen certain variables that are of interest, and created a similar “looking” version as the model.
Ashby elaborated on this idea as:
We transfer from system to model to lose information. When the quantity of information is small, we usually try to conserve it; but when faced with the excessively large quantities so readily offered by complex systems, we have to learn how to be skillful in shedding it. Here, of course, model-makes are only following in the footsteps of the statisticians, who developed their techniques precisely to make comprehensible the vast quantities of information that might be provided by, say, a national census. “The object of statistical methods, said R. A. Fisher, “is the reduction of data.”
There is an important saying from Alfred Korzybski – the map is not the territory. His point was that we should take the map to be the real thing. An important corollary to this, as a model-maker is:
If the model is the same as the phenomenon it models, it fails to serve its purpose.
The usefulness of the model is in it being an abstraction. This is mainly due to the observer not being able to handle the excess variety thrown at them. This also answers one part of the question posed in the title of this post – A model ceases to be a model when it is the same as the phenomenon it models. The second part of the answer is that the model has to have some similarities to the phenomenon, and this is entirely dependent on the observer and what they want.
This brings me to the next important point – We can only manage models. We don’t manage the actual phenomenon; we only manage the models of the phenomenon in our heads. The reason being again that we lack the ability to manage the variety thrown at us.
The eminent management cybernetician, Stafford Beer, has the following words of wisdom for us:
Instead of trying to specify it in full detail, you specify it only somewhat. You then ride on the dynamics of the system in the direction you want to go.
To paraphrase Ashby, we need not collect more information than is necessary for the job. We do not need to attempt to trace the whole chain of causes and effects in all its richness, but attempt only to relate controllable causes with ultimate effects.
The final aspect of model-making is to take into consideration the temporary nature of the model. Again, paraphrasing Ashby – We should not assume the system to be absolutely unchanging. We should accept frankly that our models are valid merely until such time as they become obsolete.
We need a model of the phenomenon to manage the phenomenon. And how we model the phenomenon depends upon our ability as the observer to manage variety. We only need to choose certain specific variables that we want. Perhaps, I can explain this further with the deep philosophical question – If a tree falls in a forest and no one is around to hear it, does it make a sound? The answer to a cybernetician should be obvious at this point. Whether there is sound or not depends on the model you have, and if you have any value in the tree falling having a sound.
Please maintain social distance and wear masks. Stay safe and Always keep on learning…
In case you missed it, my last post was The Maximum Entropy Principle: