The nature of analysis
Description of the process of analysis in four steps.
Step 1.
Dividing the object of analysis into smaller sub-objects using relevant knowledge. For this model, let’s say there’re 2 levels of complexity. Therefore by dividing the main object X into sub-objects X1, X2 … Xn we end up having quant-like objects (they can’t be divided into smaller objects).
Example. The object X is an apple, falling from a tree in the field on a sunny day. Using our knowledge of trees, apples and sunny days we can divide this object into sub-objects: apple, tree, falling process, earth, air, gravity, sunlight etc.
Step 2.
Highlighting the interactions between all pairs of sub-objects: Y1 between X1 and X2, Y2 between X2 and X3 etc. Going up to Ym.
Example: friction between air and apple, gravitation pulls apple down to earth, sunlight shines on apple — these are examples of interactions between sub-objects.
Step 3.
Now we have a simple model with objects and interactions inside of it. In order to understand how it works we can change the sub-objects parameters to see how it will influence the parent object X.
The more complicated approach will tell us that changing X1 will affect X through all the Y interactions with sub-objects that are paired with X1:
Sometimes it’s hard to understand how change will affect the object, so the best way to change parameters would be to maximize / minimize them:
We push sub-objects to their extreme attributes to understand how they’ll change the parent object.
Example: let’s say the gravity is set to minimum amount — zero. Then, the apple simply wouldn’t fall down. Or if we remove air, there would be no friction. Or, let the apple size be enormous — it will stay on the tree, because it already will be lying on the ground etc.
Step 4.
Introducing external factors to learn the system reaction.
External variables Z1, Z2 … Zn that will influence the model could really be everything that might be useful to generate a response from an object X. It could be a new sub-factor that has been put inside the object or some new global conditions that the object itself could be moved into.
Example: the air starts to move — wind is blowing on the apple. The apple now won’t wall within a straight line because the wind will blow it away (affected by air friction).
Conclusion.
The model is quite simple: it assumes that people are equally smart and have the same level of motivation to analyze the object. And all people are using the same mechanism of analysis. But you know that in real life it’s not like that. The model makes this small difference by having the variable R, which is relevant knowledge that is used to divide the parent object into sub-objects and expose all interactions between those. So, in this model the relevant knowledge is directly proportional to ability to analyze.
