Ancient Science: Modelling reality with Dhyanam
Modelling real world events as mathematical models is used for prediction or forecasting and is an obsession of humans. We believe mathematics is a universal language. We try to find a mathematical representation of any and everything in reality. Yet, it is strange that we fail to recognise the fact that none of these mathematical models come any near to representing the complexity of the variables involved in the unfolding of events in reality around us. I find that the main reason is the difference in ideology behind the two. Mathematical models are oriented towards getting a precise result whereas reality is oriented towards working with a combination of precise results and fuzzy sets. Further, continuity, a primary requirement for reality, is represented in mathematical models by small discrete changes(e.g., delta-x/delta-y, small change of x when there is a small change in y) and limits(e.g., lim x->0). So, the continuity of change of some parameter ‘x’ as it progresses from some point x1 to another point x2 as a dependance on some other parameter ‘y’, as y traverses its own changes can only be represented by small discrete changes of x and y. Also, the continuity of the model when x stops progressing when it reaches a point x2, but plays a role in the subsequent state of the system can only be represented as multiple simultaneous progressions using constructs such as simultaneous equations. This loses continuity as it transitions from one state to the next. This also makes representing reality more and more complex. Practically, reality is always such that, until an outcome has already “become”, all outcomes are in play at various fuzzy levels of continuous realisation. Once it has “become”, it is a precise result that plays a role in other subsequent fuzzy realisation. It should also be noted, that the process of attaining precision for the “become” is fuzzy. We only realise it after a period has passed after it has “become”.
An example of this can be seen in driving. When we drive, the brain analyses and gives us fuzzy results rather than precise numbers. If we want to change lanes, we do not calculate the exact speed of the cars around us, exact position of cars relative to us or any such precision data. To be able to effectively change lanes, we need to have fuzzy information of the relative speed of the cars around us in terms of answers to the questions, “will the speed intersect our path when we merge into their path, if their speeds remain same”. Along with this we analyse the acceleration at which they are gaining in on our car and will this force an intersect of the other cars with our car and so on. With a precise numbers associated with the car’s speed or acceleration such as “the other car is travelling at 40miles per hr” or “gaining 5miles every 10minutes”, the brain cannot translate to action and decision. In-fact such precision tends to interfere with our decision making. The precision is useful in a computer environment when in a virtual mode we are rendering a car moving. But to use in reality, it has to be a fuzzy representation which is in our brain. Yet, once our car has moved into a position, that precise result makes a difference to the next action to be taken, match speed with the speed of car in front of us. There is a continuity prior to the event of changing lanes as it changes into the event of changing lanes and once changed to continue in the changed lane.
What we fail to realise is “the mathematical models are just an external rendering of an internal model that we already have, for a situation based on various factors”. It is purely an effort by us to consciously represent that which has sub-consciously already formed, to act as a method to share and communicate with the external world outside us, our models of the world. In the case of driving, we need not share the model created of the state of vehicles on the road around us. We just need to use the model to draw conclusions to decide the action to be taken. Hence we do not create mathematical models out of the model, we just use it. But, let’s take another example: “predicting stock price movement in a market”. Unlike the car driving scenario, the input data change, to make a decision to buy or sell, is in a mathematical representation rather than a representation that is an integral part of reality that can be sensed by our inherent existence. Thus, we are forced to depend on external models to make this decision and fall back to inadequate mathematical models which are based on inadequate data variables. Subsequently we find that the buy or sell of shares becomes a gamble.
The model of reality around us is already in the form of thoughts. The rendering of the body, the sense organs, matter, the rules that govern our reality, everything, is a direct result of that model that is created with thoughts. The various relations between different observables and their impact plus effect on an already occurring event is present as a relation of these thoughts. We find that using this model we have the capability to predict outcomes and the path followed to these outcomes, analyse and anticipate seemingly unrelated future events and prepare for the eventuality with certain level of probability. The model that we have created with thoughts represents all the continuity and associations as a whole, with the outcomes that become input subsequently as they progress. While we seem to classify events into various categories, these are soft categorisation and the impact of one category of model on other category of model is automatically handled. When this is the case, the question is, do we need another language to express this model, especially when, that which is expressed is just a partial model of this whole model? Are we not losing information and impact? Rather than trying to express these models externally for communication, would it be prudent for us to find what triggers a model to be created in a certain way and just translate or communicate those triggers and let the model form in the other person to whom we are communicating? For example, when we say the word “red colour”, all we are doing is invoking a certain trigger for the model within the other person to simulate the thoughts related to the “red colour”. Similarly, it should be possible for us to find triggers for complex events also, that form a consistently similar model of thoughts across similar minds.
Our thought models are already solvers of complex real situations, where inherently the inputs are taken, modelled and an outcome reached. It is based on this that we act or react or respond. So, why find external mechanisms to represent the model? Would it not be easier for us to find ways to extend the thought model we already have in us for what we want it to achieve? The question is, how can this be done? Modern science’s answer to this is to represent and solve the model external to the thought model. But ancient science answer to this is integral to the thought model and involves understanding and modifying these thought models.
To do this, we need to understand and know what thoughts are and how these models are formed. Further, to be able to modify these models of thoughts, we need to rest our awareness in the controller or controlled thoughts which direct these model thoughts. When our awareness is not present at those controller thoughts, but is present at the level of the data of the model of thoughts (inputs, outputs and outcome), that is when we believe that we have no control over the model, when in fact there is an involuntarily, out-of-the-box control of these models. Our current level of awareness is at the level of data and hence is accompanied by a lot of noise.
This level of awareness and control of thoughts can be compared to controlling a car using the steering wheel, accelerator and brakes. At this level of control, the control is limited to the limitations of the controlling element. For example the wheel turning radius limits the level of control over the car’s ability to turn fast. Noise is introduced in our ability to detect and understand the angle at which the wheel is positioned w.r.t the steering wheel. Compare this to standing and walking. The thought models that control the body is automatically trained to adapt and allow us to balance and perform the action of walking. Even in driving a car we can get to this level of control, if we can let the thought model form for the various changes in car that is detected when performing various actions on the controlling elements namely steering wheel, clutch, accelerator and brake. If we stayed at the level of seeing the car as an external model to us with steering wheels and clutches and accelerators, then driving a car is filled with a lot of problems and accidents.
So, how do we get to the level of forming and controlling the thought model. This explains the reason for ancient science to promote “dhyanam”. “Dhyanam” is typically translated to “meditation”. But in fact, dhyanam should be translated to “focus”. It should also be recognised that while commonly many words such as “tapas”, “dhyanam” are considered to be the same and generically translated to just “meditation” or “contemplation”, this may not be true when we consider this above context. To be able to bring the awareness to the “controller thoughts” rather than being in the “data created by these thought models”, we need to focus our awareness away from the data and into the controller thoughts. This can be done by “dhyanam” or focus. The process by which this is done is similar to seeing various perspectives embedded in an optical illusion. To change the perspective, the eye has to focus on the image at various different points and either blur or sharpen the image to be able to switch perspectives. Similarly, in this case too, the awareness has to be focussed away from data and into the controller thoughts. “Contemplation”, “reflection” etc are distinctly different from this kind of focus. Contemplation is basically focussing on a single data or outcome. This can only be an instrument to change focus as opposed to focussing itself.
Once the awareness is at the level of the controller thoughts, other thought models can be directed appropriately to solve, include, exclude or changed to achieve the required outcome.