The WelderDestiny Compass

Theory Of Knowledge - Issue #074

Wednesday, June 27, 2018 / Perth Australia / By Niekie Jooste

In this edition of "The WelderDestiny Compass":

• Theory Of Knowledge
• Data Information Knowledge
  
• Horse Race Or Beauty Contest
• Knowledge In The Machine Age
  

Theory Of Knowledge

To prevent readers from boredom, I will finish this week with our series on entrepreneurial skills for the machine age. From next week we will head in another direction for a while.

Just to remind you, the idea is not to try to communicate the typical entrepreneurial skills like finance or marketing. That you can easily get by doing a google search. No, the idea is to try and predict which skills may give us the edge once the machine age gets into full swing, and we are mostly micro entrepreneurs, trying our best to create our own jobs in "Nichetopia".

Today we touch on the "theory of knowledge". As with most of the subjects that we discuss, this is a big topic. A topic that some people specialize in. The idea is not to be specialists in the theory itself. For us, we want to be able to distill a few valuable concepts that we can use to give us an edge in this new world heading our way.

If you would like to add your ideas to this week’s discussion, then please send me an e-mail with your ideas, (Send your e-mails to: compass@welderdestiny.com) or complete the comment form on the page below.

Now let's get stuck into this week’s topics...

Data Information Knowledge

We have spoken a lot about data in The WelderDestiny Compass. Data are merely bits of isolated measurements of some kind or other. As an example, the height of all men within a country like Australia would be a set of data.

We have also spoken about techniques in which we can change data into information. As an example, if we plotted the number of men with heights within certain distinct bands against the heights, then we will obtain a bell curve.

In this way, we can use the data we have to draw some conclusions about height distributions of men within Australia. This is information that we could use for some or other purpose.

Knowledge is the next level. Knowledge is when we have managed to interpret the information we have gathered in such a way that we can formulate a theory of how things work. So, for instance we might develop a theory about how the height of men have changed over a period, and use that to predict how the height of men are going to change into the future.

In essence, knowledge is achieved when a theory is developed that can be used for prediction. The quality of the knowledge is measured by how good our predictions turn out to be.

If we are in the realm of the natural sciences such as engineering, then we can normally develop a theory which gives us consistent answers. As long as the inputs are the same, then the outputs should be the same.

As an example, after studying the strength behavior of steels, we can eventually end up with equations for how to calculate the stresses in steel structures. This we can use to design bridges or buildings. While there will obviously be variations in material strength, overall we should be able to consistently design bridges that do not fall down.

If the knowledge we have gained can result in changes of the system we are measuring, and thereby actually make the theory less accurate than it would have been before the knowledge was known, then we are in the realm of complex systems. We have spoken about complex systems a number of times before, so we will not go into it too much, but if somebody is trying to assure you that following a certain course of action will result in great wealth, just ask yourself this question: If everyone did this, would it still work? If the answer is no, then beware!

Horse Race Or Beauty Contest

The really interesting part of the theory of knowledge happens in the realm of complex systems, so we will take a look at that in a little more detail. To get the point across, let us consider two examples.

In the first example, let us say that we want to predict the outcome of horse races. Typically we would develop a theory where we included information like the form of the horse, the form of the jockey, the reputation of the trainer, the breeding of the horse, the track conditions and the distance to be run.

Based on these conditions we would rate all the different horses, and come to a conclusion about which horse would probably win. Seeing as a horse race is a complex system, there is no way that the outcome will always be the same, but for a situation like a horse race, we would typically follow a fundamental approach to developing our theory.

For our second example, let us consider a beauty contest. In this case, we could again build a theory on fundamental factors like height, facial features, weight, hair color and length etc. We could however approach this from a totally different angle.

We could try to predict the winner by basing our theory on what the judges like. At that point, the features of the ladies in the contest are less important than what we believe the judges may prefer. If there are different judges, then our prediction for a winner of the beauty contest may change. In this case we are using a trend approach to developing our theory.

This distinction is important, because whenever we are tying to formulate predictions for complex systems, we need to ask ourselves whether we are in a horse race scenario or a beauty contest scenario. Based on this, we would then be able to much more effectively formulate our theory to maximize our probability of success.

Knowledge In The Machine Age

Artificial intelligence and robots are seen as the big enemies coming to take our jobs away. They can also be seen as the next client base! These machines are pretty much useless without a lot of data, information and knowledge.

In the machine age, data is the new oil. Data can be sold and traded. Data is valuable for the machines to operate. Data on its own is however not the end result. Data is the input required to develop the information and by extension to develop the theories and knowledge for prediction.

For us to have an edge in the machine age, we need to be able to understand the theory of knowledge. Once we understand the theory of knowledge, we can better judge how reliable predictions are that people are trying to "sell" to us. It will also help us to better judge how to go about developing our own theories about how the world works.

Understanding the theory of knowledge also helps us to be realistic, and possibly just a little skeptical about claims made by so called experts where complex systems are concerned.

Yours in welding

Niekie Jooste

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