Think In Terms Of Probabilities, Not Certainties

Each of us only has limited information about the world, plus the future is uncertain by nature. The question is how to make good, informed decisions.

Rather than decide with our limited knowledge, we need to use Probability theory in all decisions. When we acknowledge the unpredictability of the future due to so many unknown variables that can impact an outcome, the best approach is to predict the future by creating practical, useful probabilities.

Confusing? Let me give a common example:

If there is news of a disturbance in Kashmir right now, then your immediate reaction might be to cancel your next month’s Srinagar-bound flight tickets immediately. But Wait!!

When you pause, and reflect upon the number of tourists affected by that single event, you’ll find the actual Probability of you being affected, has barely changed, if at all.

In this example, an informed decision based on Probabilistic thinking would be to follow your original plan and go visit Kashmir. After all, the probability of your being affected, is not changed by some disturbance in a remote area anyway.

What you just heard has a name, Bayesian Thinking. It’s the kind of thinking, where along with current news, you take into account your knowledge of past data as well, to make decisions.

Now, there is a special caveat to Bayesian thinking, called Conditional probability.

Conditional probability is predicting the future based on past events, considering specific conditions under which the past event happened. Any past event can either impact the current probability of event, or it can’t. Events can be independent (like flipping a coin) or dependent (connected events, like rain and carrying an umbrella). It’s all about using what you know, to make smarter predictions!

Warning!! The last concept is something of a “Probability-ception” of Meta-probability, i.e., how accurate are the probability estimates you have. They might not always be as accurate as you think. Let me explain.

For example, when investors confidently predict high returns, they may not achieve those results because they overestimate their accuracy. Conversely, many times they get their predictions wrong due to being overly conservative. This Asymmetry is present in all forecasts, not just investing.

In conclusion, thinking in probabilities means understanding what’s important, estimating the chances, checking our assumptions, and then making decisions. It helps us navigate uncertain real-life situations more confidently.

While we can’t predict the future with 100% confidence, using probabilistic thinking allows us to assess how things will likely unfold and plan our strategies effectively. These 3 learnings will help you do just that:

1. Bayesian thinking: Consider what you already know when making decisions based on current information. Keeping both these in mind, you can then make an informed, statistically better decision than if you rely on your current events alone.
2. Conditional Probability: When you’re thinking about what might happen next based on what happened before, and you have to consider the specific conditions of the past event
3. Meta-probability, or Asymmetry: You have to consider how reliable your probability estimates are