When we operate in the world, we operate through models of the reality we envision in order to deal with various contexts and dynamics. We prefigure and learn models through imitation (initially of the caregiver, later of people assumed to be significant), and especially in childhood we do not ask ourselves why or whether they are the best ways to do, deal with or improve contexts and dynamics. Initially, we assume any prefigured model as true and adherent to reality, and do so uncritically. From the initial models, we later connect hundreds of models by adapting them to the context and linking them to previous models. Many behavioural errors largely depend on incorrect, or misinterpreted, basic models. The more time passes, the less capacity we have to create new ways. Gradually we lose the ability to learn different models, until we can no longer break out of the cage of super-interactions between models we have developed. It is true that, in an intelligent person, those models that become more and more crystallised become deeper, richer and more efficient. But it is also true that they still tend towards circularity. But circularity is not a bad thing if we have a very wide variety of mental models to draw from. The more variety and enthusiasm we learn, and the more we link the various models together, the more potential for solving, understanding and creating even better models we have (I stress that this is the real meaning of 'learning', not 'learning by heart' or performing without criticism). It goes without saying that all the work is considerably laborious and requires a deep understanding first of the model, then of the reality to which that model fits. But even this understanding includes a model optimised to promote my deep understanding. But let us see, in a very simplified and abstract way, a purely indicative and non-mathematical graph of how an implementation between models works.

Now, let us look at the graph. As we can see, the original model is rich in focal points, but poor in intra-connections. The first step needed to implement a new model is to understand the focal points with which we interpret and bind the models together. Understanding the basis is essential for understanding the subsequent connections and learning how to extend the new model as dynamically and deeply as possible. Once we understand the basis, we need to understand the new model that we want to learn. It does not matter, at this time, what the model is: we can take a model based on mathematics, chemical laws, a type of philosophy or psychology, even extrapolate one from literary authors and so on. The important thing is to have a broad basic understanding of what that model is. Now, by similarity, let us integrate commonalities and broaden the connection between parts. We put this joining of dots into practice in the world, integrating them in turn with other micro-models and enriching them with personal experiences. It is important to note that a large part of this integration occurs at a non-conscious level, but remains largely optimisable by observing and maintaining a good level of attention to changing events. It is also appropriate to point out that optimisation levels also depend on how intelligent someone is (no, not on IQ, that is a bullshit full of noise that is used to measure deficits, not intelligence).

This is an introduction that I will slowly (you can't do very high quality things by posting often, sorry), augment with simple and complex models, and moreover I will post case studies where I will apply this to reality.

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