INFORMATION IS NOT UNDERSTANDING

We’ve all met people who can jam a lot of information in their heads and even recall it with speed, but at the same time they seem to lack the understanding to apply that information. There are a number of problems that result from information without understanding, the biggest dilemma being context.

Here are a few common ways we express this dilemma:

• Information is not understanding
• Correlation is not causation
• Fact is not context

Let me give you an example of a fact that doesn’t answer the context problem.

Example: Busy people are bad friends

It might be true that busy people make for bad friends. That could be a fact. But the context for that fact is missing right? For example:

• The phrase “bad friends” requires a shared definition
• Sufficient detail is lacking about what makes a busy person into a bad friend
• The focus on “busyness” might not actually be the central factor in causing them to be bad friends

The trouble is that while the fact might be true, it is implying context and causation, leading you to believe you have understanding, when really you don’t. Let’s look at a popular Year 2020 example.

Example: Poor people are disproportionately affected by COVID-19

The correlary fact of wealth may be true. But we are missing too much information in context for any real understanding to form helpful ideas. For example:

• We need to start with a shared definition for “poor” so we can test that fact
• We need to identify the causal factors, not just the correlated ones
• The focus on “poor” might not actually be the central factor in causing them to be more succeptable

Let’s test the above to see if the informative fact leads to understanding:

Question test: We need to start with a shared definition for “poor” so we can test that fact

As long as we can show that either a certain level of “poverty” is positively correlated to COVID-19 succeptability, or that a range of increasing levels of poverty are increasingly succeptable, then we can verrify we have a proper definition. If the metrics show this, then we at least have a true fact. (In this case, I believe we do.)

Question test: We need to identify the causal factors, not just the correlated ones

If we could simply increase wealth as a factor of poverty, would it reduce COVID-19 succeptability? I think this would be tough to prove, but more importantly, asking the question raising other good questions that might lead us to look at proverty attributes other than wealth, like:

• Saving money
• Prioritizing investing in personal health insurance
• Taking employment in the service industry versus pursuing training and employment in less volatile market segments

Question test: The focus on “poor” might not actually be the central factor in causing people to be more succeptable

In this example, “poverty” is the classification of the correlated fact. But as we move deeper, from simply correlating identifiers that trend in the same direction, to the factors that make up the identifier definition - now we might discover the real causation factors. The goal should always be to move beyong the correlation identifiers to find the real causation factors. Moving the needle on those causation factors is where real change for good happens. Addressing only the identifiers means leaving out the context. Let’s look at deeper causation experiments that we could do by reframing the fact claim to add context:

Individuals working in volatile industries more succeptable to economic downturns are more likely to lose their primary source of income and as a result lower the priority of acquiring health insurance, making them more succeptable to the impact of viral disseased like COVID-19.

A statement like this points at factors that may cause higher levels of succeptability rather than just implying the cause to be poverty itself. If poverty was the problem, then throwing more cash at it would solve the problem. But if the real cause was lose of income, not prioritizing insurance, and the high cost of insurance, then we might come up with a lot of causal factors that minimize the risk in those factors:

• Educational scenarios (school, intership, apprenticeship, etc.) for higher-paying, more stable job industries
• Personal education on financial planning
• Personal education on healthy living

While ideas like making health insurance free might feel like a good solution, one could apply the same logic to pharmaceutical drugs, but free can also lead to abuse, rising costs, more unhealthy lifestyles saved by free health insurance, etc. In the end, we are not solving anything. We would just be masking a larger set of real factors.

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