Ideas behind the core concepts of analysis world are complex as their implementation and area of descriptions are multifaceted. In its simplest form analytical models are models, generally, prescribed in mathematics that have form of explanations to the solution matrix i.e. changes in equations can be described in said formulation with the help of expressed mathematical analytic function. The nature of the model may give hints about the behavior of said equations with or without a need of a graphical inspection. Although they are very mathematically challenging to obtain, they are one of the key factors of numerical solution, in which descriptive and predictive analysis can be done.
Nonetheless, the market and academia have sided exactly opposite between themselves in terms of arguments on behavioral description and prediction. On one hand, academia suggesting complex solutions to already complex problems of prediction. Although, improvements in AI, ML, and other interdisciplinary data sciences paved the way for easier functional analysis of complex problems, they are still hard to obtain and hard to interpret due to their ever-changing structural nature.
TOn the other hand, agents of the market generally omit the technical parts of the analytics and focus on the rules of thumb. The description and prediction mechanism of agents of the market are most of the time result-oriented notions that are simply answering what is happening? What will happen? questions. However, with the 21st century paradigm, everything got bigger so big data needs far more complex analysis than usual rules of thumb. Therefore, last empirical evidence on big data analysis showed that simple modelling is necessary but not sufficient to state the nature of the situation. Although, market analysis suggests that way, there are lots of models widely used, which are simpler than almost any model but works flawlessly. That is, being consistent among both small samples and larger samples is far more important than being complex.
Hence, whether they are complex or simple, having a model on analytics basis is a great tool for description of nature and prediction of behavior. Thus, having even a basic model tool shows great leaps towards better understanding for structural notions. Moreover, there are some other benefits of general analytical modelling such as :
- Easing away the complexity of the data
- Preserving knowledge for future implementations
- Reusability for infinitely many times as long as assumptions hold
- Automation some task over models
Therefore, having this analytical tool in hand to describe and predict the nature of the behavioral mechanics of the integrated variables. In fact, although there are some key benefits of complex models, simpler ones are as well good at what they are designed for. Alas, the delusion of simpler models are worse than the others comes from the fallacy of mixing that complex models are inclusive and simple models are just, as name suggests, basic. However, accurate interpretation is that whether a model is simple or not it must be all inclusive; that is, it must include all things that are relevant for the job i.e. they must be pragmatic assumptions. Hence, instead of omitting the relevant parts of any model for the sake of simplification, making it inclusive with all relevant variables and coefficients are much better, for all abovementioned benefits of modelling can be seen by it; plus, it paves the way for further implications.
In conclusion, although complex models are mathematically superior to simple ones, the market naturally wants simpler and result-oriented models whether they are theoretical or atheoretical as long as they suggest solutions to initial and further problems.