What is Bayesian theory within the field of Competitive Intelligence analysis?

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What is Bayesian theory within the field of Competitive Intelligence analysis?

This article ask’s what is Bayesian theory within the field of Competitive Intelligence analysis? We explain what Bayesian theory is and why it is a valuable concept within Intelligence analysis and beyond. Put simply, Bayesian theory helps you understand the probability of events occurring.

Bayes’ theorem is a mathematical formula that allows you to calculate the likelihood of an event happening based on prior knowledge. This is useful for understanding how likely something will happen, given the current information in your possession.

Bayesian theory considers all available information to calculate the likelihood of different outcomes. Allowing you to make more informed decisions based on the most likely scenario.

It starts from the assumption that we don’t know the actual situation your business faces. But we do have some information that can help us make better guesses. You can then use the Bayesian theory to calculate the likelihood of different outcomes based on your current information. Allowing you to make better decisions, even in the face of uncertainty.

What Are Probabilities?

In Bayesian theory, a probability is a number between 0 and 1, representing the likelihood of an event occurring. If we think of a coin flip, then the possibility of heads is 50% since there’s a 50% chance of getting either a head or tail.

Understand Probability And The Law Of Total Probability.

Bayes’ theorem states that P(A|B) = P(B|A)*P(A)/P(B). If A is true, then B must also be true. If B is false, then A cannot be true.

Calculate Marginal Probabilities.

If you’re familiar with probability, then you’ve probably heard of conditional probabilities. A conditional probability is simply the probability of one event happening, given that another event has occurred. 

For example, let’s say there are two events, Event A and Event B. Let’s also assume that event A occurs with a probability of 0.5. In contrast, Event B occurs with a probability of 1.0. Then, the probability of Event A occurring is 0.5. So event A will happen half the time. 

However, the probability of Event B occurring is 100% because every time Event B occurs, Event A must also occur

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Why Do We Need To Understand Probability?

In order to make decisions, we need to understand probabilities. This means knowing what the chances are of something happening. For example, if you are trying to decide whether to go out tonight, you may ask yourself, “What’s my probability of being mugged?” Finding out that the probability of being mugged is low (say, less than 5%), then you’ll probably go out. On the other hand, if you find out that the chance of being mugged is high (say, more than 60%), then you won’t go out.

Calculate Joint Probabilities

Now, let’s take things a bit further by calculating joint probabilities. This is where we calculate the probability of both events occurring together. For example, let’s say Event A occurs with a 50% chance, while Event B occurs only when Event A occurs. Then, the probability that both Events A and B occur at the same time is equal to the product of the individual probabilities of each event. So, the probability of both events happening at the same time is 0.25 * 0.50 = 0.125.

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Calculate Likelihood Ratios

Let’s try another example. Say that Event A has a 90% chance of occurring while Event B has a 20% chance of occurring. If event A occurs, then the likelihood ratio is 9/10. If event B occurs, then the likelihood ratio is 1/20. Therefore, the likelihood ratio is 9 / (9 + 1) = 0.95.

The Russian invasion of Ukraine

Take the Russian invasion of Ukraine. Many analysts and commentators and undoubtedly many of the Ukrainian people believed that Russia was just bluffing. They were just putting pressure on NATO to cease their expansion to the east.**

Right or wrong, that was their prior proposition. 

Then comes some incepted military communications, satellite images of columns of tanks turning south. And people on the ground (and OSINT Twitter experts) report unusual troop movements. Undoubtedly, Western Intelligence will have useful sources reporting Russian thinking, expectations and intentions.

You put it all together, and suddenly, what appears to be there is indeed the final stages of a Russian ground forces exercise is, in fact, an invasion. 

Once the probability of invasion had increased to certainty, it was down to western governments and NATO to make their decisions. All this Intelligence gave just enough time to get organised. We are surprised how western governments were surprised about the Russian actions. However, that’s what they were saying publically but privately was a different matter. 

The essence of Bayesian theory

So, the essence of Bayesian theory is that whenever new evidence arrives, you can use that methodical way of thinking to alter your degree of belief in your previous proposition. 

But that kind of way of thinking about Intelligence but being prepared to adjust your estimates in the light of new information. That’s the secret to Bayesian theory, and a bigger secret to Bayesian theory is only using it when necessary. Most information about your future market and competitors is already out there and usually really obvious and in your face. 

What is Bayesian theory within the field of Competitive Intelligence analysis?

In conclusion, Bayesian theory is a valuable tool for understanding the likelihood of events occurring. It helps analysts make better predictions and understand the underlying factors contributing to those events. 

**Note that many other analysts believed that Russia would invade. Perhaps because they understood Putin’s thinking a little more, or they were listening to the right information from the right sources. Also, unlike the 1960’s Cuban missile crisis, the western powers failed to offer Putin a ladder to climb back down from the brink.

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