Logic and fuzzy data.

[wellwisher]

Reason is based on applying logic procedures to observational data, to determine cause and affect and to draw conclusions. The object of this topic is to discuss what happens when the data is not clear cut, but is based on fuzzy dice; margin of error, such as derived in statistical studies.

If you have two data points; points, these can only be connected by only a single straight line. If the data points are no longer points, but are more like finite spheres of radius; r, now more than one straight line can touch the data ball, allowing sound logic to diverge into many different conclusions. Fuzzy data appears to be the main source of disagreement in most topics. It is not so much the misuse of logic for each person, but starting with fuzzy dice data and picking a line that still touches the sphere, but diverges.

Here is hypothetical example to show how this works in practical reality. Columbus discovered the Americas in October 12, 1492. Based on records we know this sharp data point. From that date and other knowledge of history at that time, we can infer other things. Say the records were lost or history was revised. Now we need to add a margin of error to this date, say plus or minus 10%; fuzzy dice data. This would now mean America was hypothetically discovered by Columbus somewhere between about 1350 and 1650.

This range for this data will now have an impact on trying to infer the context for this discover. Year 1350, was before the age of exploration, when the Roman Catholic Church was still unified. It was a different world from the time of Columbus. Logically, this side of the fuzzy dice data might imply the Church was driving the age of exploration, a century before it took root. It follows logically and touches the fuzzy data.

On the other hand, if we instead bet on the over; 1650, instead of the under; 1350, this data for our logic equates with the time when many European countries already had colonies in America. Logically, maybe Columbus did not really discover America, since we can prove other colonies were already here in 1650.

This is why I have a beef with casino science and why politics and other manipulative arts; marketing, also prefers to use fuzzy dice data. It can create multiple logic niches, but cannot find common truth offered by sharp data points. Revisionist history, for example, tries to turn what was considered the most important aspects of a time, defined by those who lived at that time; sharp points, into fuzzy dice data; confusion in time, to help funnel sound logic down a bunch of rabbit holes.

This topic came to mind after thinking about transgender and the sincerity of those who argue from that POV. It dawned on me that science originally tried to seek clarity with just two sharp data points; male and female. Now the fuzzy dice affect has been imposed on the once sharp demarcations, and the fuzzy dice data keeps getting larger and larger; he, her, him, so a once single line can now slope any which way, impacting the conclusions of sound logic; multiple rabbit holes.

An interesting application of this observation is religion and science fiction for atheism; modern mythology. Both assume higher divine or alien technical potential. These can add wild cards to reality and create fuzzy dice data for logic. Fuzzy dice data in science may be a form of religion, since it causes divergence with all but one slope in a rabbit hole. Separation of Church and State may now have a universal litmus test; fuzzy data affect.

Statistical math helps create this fuzzy dice affect in that it places the phenomena in a black box, so you cannot tinker and come to a data focus. This can be useful if you are blind in the figurative sense. But it can make you crossed eyed if you can see or like to tinker. The black box keeps all options open. The input and output may be due to squirrels on treadmills, fairies or aliens since we cannot see inside the box. Margin of error sort of places some limits; squirrel or aliens.

 

[lewisnotmiller]

I'm not sure if I'm understanding you correctly, so apologies if this misses the mark.

I'll commonly use upper and lower range limits when estimating complex work.
Based on the limit range, I'm communicating more than one piece of information.

For example, I might quote a process transformation as including 80 hours of effort (+/- 25%). I might estimate other aspects of the same project at (+/- 5%) or (+/- 100%).

Whilst this certainly looks less exact than simply saying 'it will take 80 hours', I'm actually providing more information, not less certainty.

Based on the precision claimed, the client can better manage time and monetary contingency. They have a relative measure of certainty between different pieces of work. And it leads naturally to discussions about risk, and where the lack of precision comes from, including how to mitigate against it (which might then change the precision).

Ultimately, this doesn't always apply to data points. But claiming certainty or accuracy where it's not actually known is more problematic than range indicators, in my experience.

 

[Nimos]

Here is hypothetical example to show how this works in practical reality. Columbus discovered the Americas in October 12, 1492. Based on records we know this sharp data point. From that date and other knowledge of history at that time, we can infer other things. Say the records were lost or history was revised. Now we need to add a margin of error to this date, say plus or minus 10%; fuzzy dice data. This would now mean America was hypothetically discovered by Columbus somewhere between about 1350 and 1650.

This range for this data will now have an impact on trying to infer the context for this discover. Year 1350, was before the age of exploration, when the Roman Catholic Church was still unified. It was a different world from the time of Columbus. Logically, this side of the fuzzy dice data might imply the Church was driving the age of exploration, a century before it took root. It follows logically and touches the fuzzy data.

On the other hand, if we instead bet on the over; 1650, instead of the under; 1350, this data for our logic equates with the time when many European countries already had colonies in America. Logically, maybe Columbus did not really discover America, since we can prove other colonies were already here in 1650.

Sure if the data is somewhat limited, there will be uncertainty. But that doesn't mean that one has to simply jump to the conclusion that one of these is correct. In your example, if we go with the over 1650, but that doesn't match with other known data, then clearly that would be wrong. That is why when working with data which are uncertain one will cross reference it with other data.

From what I understand where you would use this type of observational data is when you are looking for tendencies, so you gather a lot of data where you might have an occasionally extreme diversion from the norm, and then you can draw a line through it based on where the density is the highest and it will give you an estimate of tendency.

Im not sure that is how one would go about doing stuff, like in your example with Columbus, given that there is a specific date, it is just not known and the lack of data is what is preventing us from figuring it out.

 

[Heyo]

Reason is based on applying logic procedures to observational data, to determine cause and affect and to draw conclusions. The object of this topic is to discuss what happens when the data is not clear cut, but is based on fuzzy dice; margin of error, such as derived in statistical studies.

Then you have to widen your definition of logic. Aristotelian logic is binary but reality usually isn't. The nice thing is that the same rules of logic apply to non binary, continuous data as well. The maths gets a little more complicated and the answer isn't one of absolute truth any more. But one can still establish a most likely scenario and put probabilities to the less likely ones.

But for people who like the world black and white this causes problems.