Certainty is an illusion in a universe of probability: 1 or 0

in #life6 years ago (edited)

Measuring and analyzing

One theme you probably see in my posts which might reveal a bit about my thinking process is that I tend to collect data and analyze data. The truth is that this process of data collecting and analyzing is essentially what the mind does. The good news is that all of the universe is able to be represented as data. Anything which can be perceived can be represented in language as data.

What do we really know?

Mathematics is essential in the formation of hypotheses, theories, and laws[109] in the natural and social sciences. For example, it is used in quantitative scientific modeling, which can generate new hypotheses and predictions to be tested. It is also used extensively in observing and collecting measurements. Statistics, a branch of mathematics, is used to summarize and analyze data, which allow scientists to assess the reliability and variability of their experimental results.

So what are we really doing when we analyze data? The truth is we are really just running calculations. In science as knowledge acquisition we have epistemology. We have the notion of truth, belief and justification. For people interested in history the birth of the search for truth and knowledge began in Egypt. Over time it evolved and today we consider science to be a mechanism by which knowledge can be organized and generated. Specifically in science the predictions (predicted result or behavior) must be testable.

The measured approach

The measured approach to life or more specifically to contemplation is to attempt to weigh things on a scale. This measured approach in my opinion is central to the concept of ethics where every decision is weighed against all other possible decisions.

Two interesting concepts:

  • Search space
  • Decision optimization

When a mind must make a decision from a map of options each with different weights we may call this an aspect of decision. If a person has no prior knowledge then every decision may as well be random. By the consequences (effects) the mind can learn. Put a different way if we think of decisions as being mapped on a tree where all known possible decisions rest somewhere on this tree then we can visualize and analyze our decisions.

It is my opinion that people in leadership positions, parents, or people who are educators, should teach decision analysis by showing how to create a decision tree.

Today most people do not utilize the decision tree because it takes a lot more time and effort to make decisions in this way. On the other hand very critical decisions when made in this way can be later justified when those decisions are questioned by others. A parent or stakeholder should be encouraged to question the decision of the CEO, or the child. In the case of the parent the questioning of the decision can help the child to learn to improve their decision analysis skills and decision making process for the future. This is important because leadership or management positions in society may require numerical style decision making.

This video below from MBABullshitDotCom shows the utility of decision tree analysis:

As we can see from the video any manager or anyone making a critical decision can rely on this mapping technique. In fact it is fairly simple to make an excel spreadsheet as shown in the example video below:

And this final video below:

So back to the search space concept where if you have an ability to traverse the entire search space (review each decision 1 by 1) then you can sort after reviewing. This sorting is how a mind can prioritize. Some factors we can include in the weights are risk, probability, and potential reward or potential cost. Conditional probability and probability space are also useful concepts.

Conclusion

We live in a universe of uncertainty. We simply have no way to know with certainty the outcome of any action we take. In fact it is probable that certainty simply does not exist. We only have the ability to measure probabilities and determine based on that what is the most likely outcome of any decision or choice. For this reason there are important data sets which we require for statistical thinking.

To clarify, we can map either in our heads or visually the probabilities, known risks, expected rewards, likely consequences, etc. Even something as simple as picking stocks and building a stock portfolio involves measurements. An example of a heuristic is 1/n which is to simply divide your risk across many assets.

1/N rule shows what we can call an optimization rule. It is an example of decision optimization by using a naive optimization strategy. It isn't requiring complex calculation in this instance but it shows that you don't often need to apply complex calculation to improve decision making. Teaching simple heuristic rules can help with improving decision making.

The development of these heuristics in my opinion is very related to if not the basis for the development of ethics. Simple rules which in most cases lead to more favorable outcomes. These heuristic rules are effective for a world of uncertainty where time is limited. The time to make a decision is indeed limited as we do not live for 1000 years and we may not have the ability to run the calculations to narrow down the search space to find the perfect decision. Just as in chess where people who have played the game for many years can figure out what not to do, what positions not to be in, what rules to follow in order to have the best chance of winning, it is the same with heuristic rules.

References

  1. https://en.wikipedia.org/wiki/Probability_space
  2. https://en.wikipedia.org/wiki/Conditional_probability
  3. https://en.wikipedia.org/wiki/Decision_tree
  4. https://en.wikipedia.org/wiki/Decision_theory
  5. https://en.wikipedia.org/wiki/Statistical_thinking
  6. http://www.macroresilience.com/2010/07/08/heuristics-and-robustness-in-asset-allocation/
  7. https://www.investopedia.com/articles/stocks/11/naive-diversification-vs-optimization.asp
  8. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2889279
  9. https://en.wikipedia.org/wiki/Heuristics_in_judgment_and_decision-making
  10. https://www.theguardian.com/law/2011/oct/02/formula-justice-bayes-theorem-miscarriage
  11. https://en.wikipedia.org/wiki/Science#21st_century
  12. https://www.worldscientific.com/worldscibooks/10.1142/8503

Hsu, P. H., & Cao, Z. (2016). Asset Allocation Strategies, the 1/N Rule, and Data Snooping.

Sort:  

Decision making is a necessary part of life and the certainty of a decision to produce the needed result is based on the template used in it's analysis. Nothing is 100% certain in life but with the right analysis, we can choose the decision with the highest probability of success. Though, the analysis is where the work is in decision making and only a very few people will be willing to go through such stress. This is a well written piece. Thanks

A responsible person seeks to make the best decisions possible with the available information they have. The processes and methods of making precise decisions are quantitative rather than qualitative. That is to say that the numerical style of decision making is based on exact methods (mathematics). The problem I highlight in my post is that there is a time scarcity problem where to do a complex calculation might cost years while the decision has to be made in days. This means in many cases the cost of using the exact methods may outweigh the benefit.

To overcome this we have heuristics which are simple rules. These simple rules offer optimizations which work most of the time in most situations. So I used the example of rule: 1/N. This is an asset allocation rule which is super simple but also highly effective in practice.

Naïve diversification is a choice heuristic (also known as "diversification heuristic"[1]). Its first demonstration was made by Itamar Simonson in marketing in the context of consumption decisions by individuals.[2] It was subsequently shown in the context of economic and financial decisions. Simonson showed that when people have to make simultaneous choice (e.g. choose now which of six snacks to consume in the next three weeks), they tend to seek more variety (e.g., pick more kinds of snacks) than when they make sequential choices (e.g., choose once a week which of six snacks to consume that week for three weeks). That is, when asked to make several choices at once, people tend to diversify more than when making the same type of decision sequentially.

Understanding of this rule has massive implications because it might provide insight onto the behavior of crypto investors.

References

  1. https://en.wikipedia.org/wiki/Naive_diversification

Naive diversification
Naïve diversification is a choice heuristic (also known as "diversification heuristic"). Its first demonstration was made by Itamar Simonson in marketing in the context of consumption decisions by individuals. It was subsequently shown in the context of economic and financial decisions. Simonson showed that when people have to make simultaneous choice (e.g.

certainty is asymptotic like randomness, or speed of light, or absolute zero... uncertainty we call 'free will' and its asymptoicity is the promise of eternal freedom and wealth.

Coin Marketplace

STEEM 0.35
TRX 0.12
JST 0.040
BTC 70391.42
ETH 3572.68
USDT 1.00
SBD 4.74