## Intrinsic Risk

If you were told that someone had flipped a coin 60 times and had found that heads were the results 2/3 of the time, you might have several reactions.

- You might doubt whether the coin was a real coin or whether it was altered.
- You might suspect that the person who got that result was doing something other than a fair flip.
- You might doubt whether they are able to count or whether they actually counted.
- You doubt whether they are telling the truth.
- You start to calculate the likelihood of that result with a fair coin.

Once you take that last step, you find that the story is highly unlikely, but definitely not impossible. In fact, my computer tells me that if I lined up 225 people and had them all flip a coin 60 times, there is a fifty-fifty chance that at least one person will get that many heads.

So how should you evaluate the risk of getting 40 heads out of 60 flips? Should you do calculations based upon the expected likelihood of heads based upon an examination of the coin? You look at it and see that there are two sides and a thin edge. You assess whether it seems to be uniformly balanced. Then you conclude that you are fairly certain of the inherent risk of the coin flipping process.

Your other choice to assess the risk is to base your evaluation on the observed outcomes of coin flips. This will mean that the central limit theorem should help us to eventually get the right number. But if your first observation is that person described above, then it will be quite a few additional observations before you find out what the Central Limit Theorem has to tell you.

The point being that a purely observation based approach will not always give you the best answer. Good to make sure that you understand something about the intrinsic risk.

If you are still not convinced of this, ask the turkey. Taleb uses that turkey story to explain a Black Swan. But if you think about it, many Black Swans are nothing more than ignorance of intrinsic risk.

**Explore posts in the same categories:**Black Swan, Data, Risk, risk assessment, Uncertainty

**Tags:** risk assessment

November 26, 2010 at 1:46 pm

Like good Bayesians, we may start with the assumption the coin is fair.

As experience emerges, we may shift belief from 50-50 to some other view; this is the job of credibility theory.

Even deeper, we must consider the cost/benefit of being wrong, in deciding which way to bet.