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A Powerful Logic Hack

Hacks are trending, so why not a hack for logic?

Developing a spy mindset means identifying your intelligence gaps and collecting information to make logical decisions with the right degree of confidence.

Logic is either deductive or inductive. Deduction seeks valid conclusions based on the available information (think Sherlock Holmes analyzing evidence). Induction seeks degrees of confidence based on repeated observations (think Isaac Newton observing falling apples).

The rules for deduction are clear and finite, so the logic hacks come from mastering the rules and developing your intuition over time. For example, great quarterbacks learn to read a defense and call audibles.

The rules for induction are less clear and more statistical. Two challenges are assessing the degree of confidence in the underlying activity and deciding which degree of confidence you require to make decisions. When are the odds sufficiently in your favor?

For example, suppose multiple companies are assessing whether to build a car factory, with the contract going to the first company to sign on the line. There are several variables to consider, such as projected sales, market conditions, tax incentives, labor laws, and so on.

In this scenario, you will never reach 100% confidence, but if you wait too long, you might lose the opportunity.

Suppose you are seeking 92% confidence. First, is it possible to know when you have reached 92% confidence? Your calculations are only as good as your models – GIGO. Second, is it possible to know that 92% confidence is the right target number? What if your competitor opts in at 85%, makes the first move, and wins the game?

According to induction, the ongoing collection of confirmatory information allows us to make decisions with a higher degree of confidence. Seeing another white swan increases the odds of all swans being white. However, ongoing collection also faces the problems of cost, marginal return on investment, and opportunity cost.

Therefore, in addition to collecting information to increase your degree of confidence, you should also collect information to falsify the option.

Enter Karl Popper, philosopher of science.

Popper is credited with the idea of defining scientific claims in terms of falsification. In short, claims are scientific if and only if we can state what evidence would falsify the claim. Claims that cannot be falsified are not scientific.

For example, if someone claims that Elon Musk is building Teslas on the dark side of the moon, this is a scientific claim because we could falsify it by taking satellite photographs.

For logic, this thought process usually takes the form of (queue Latin) modus ponens and modus tollens. Both are based on the idea of causality: If A happens, then B happens.

For modus ponens, consider the following logical argument:

If it rains, the streets will be wet.

It rained.

Therefore, the streets are wet.

This is a valid argument – cause and effect. Now consider the following argument:

If it rains, the streets will be wet.

The streets are wet.

Therefore, it rained.

This might be true, but the argument is invalid because the streets could be wet for other reasons, such as a flood or a fire hydrant. The logic flows from the rain causing wet streets.

Shifting to modus tollens and Popper falsification, we are allowed to move in the reverse direction only by postulating dry streets as proof that it did not rain.

If it rains, the streets will be wet.

The streets are not wet.

Therefore, it did not rain.

The claim about rain causing wet streets is scientific because we could falsify the claim by observing dry streets. A word of caution, however. If you woke up at 10:00 am and observed dry streets, is it possible that it rained a few hours earlier, but the water evaporated due to the heat? Time matters.

Returning to our example of opening a car factory, what might falsify the claim that the investment is a good move? For example, suppose that the profitability of the project hinges, among other things, on tax incentives promised by the state. What if you have solid information that the changes to the tax law will not be approved? In theory, this single datapoint might be sufficient to falsify the project’s viability.

The key takeaway is that as you contemplate future business decisions, you should make sure that each decision scenario is falsifiable and then make sure to collect enough information to test the falsification scenario.

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