Make the Right Choice (Decision Making Skills No. 13)

My name is “Michael Mauboussin” and I'm head of global financial strategies at Credit Suisse. And today I'd like to talk about how to improve decision making.

Now in the world of science there's actually a very interesting framework to understand how theories develop and improve over time. The first phase is called “Observation”.

Where you just figure out what you want to talk about, you agree on the terms, and you observe a lot of information.

The second phase is called “Classification”.

Where you take all that information and you sort it into buckets, into classifications.

The third step is figuring out definition, like what causes what to happen.

Let me give you one example from the world of flight.

So for centuries, humans wanted to know how to fly. That's the observation phase. When they went into the real world, they said, "Well, it looks like the things that fly tend to be things with wings and feathers." So what did the first humans do? They fashioned wings, stuck on feathers, went to a really high spot, jumped, flapped, and then crashed. That was the anomaly. So that's not the right answer. Had to go back to the basics.

Turns out around the early 1700s there was a scientist named “Daniel Bernoulli” who worked on fluid mechanics and as part of that he started to understand this concept called the airfoil. That's something that provides lift and it turns out that is one of the keys to flight. So as people understood the notion of the airfoil and lift came along into the early 1900s and the “Wright Brothers” said, "That's the key if we can combine that with a little bit of propulsion technology and some steering, we can create an airplane that flies."

It has nothing to do with wings and feathers. It has all to do with the airfoil.

So now we have a sense on how we improve understanding causality. Now we can turn to the second topic, which is how do we improve our forecasts? And the key idea here is something called the inside versus the outside view. Here's an example from the world of mergers and acquisitions when two companies combine. The inside view usually means that companies are very optimistic. They can see all the positives of the deal, the strategic value, and all the financial savings that are going to accrue.

But if you look at the outside view, what you see is about 60% of the time, the stock of the acquiring company fails to go up. In fact, it usually goes down. So, not with saying there's a lot of optimism on the inside view, the outside view often gives you a much more nuanced and often less optimistic point of view.

There's a third area that's really important, which is how do we take new information that comes in and integrate it with our point of view. Typically, we don't really take into consideration new information. The first major barrier to that is something called confirmation bias.Once you've decided on something and you think this is the right way to think about it, new information that comes in, either you blow it off and just disregard it or don't pay attention to it, or if it's ambiguous, you interpret in a way that's favorable to you. Now the next problem we all have this is what's called pseudo and subtly diagnostic information. So pseudo diagnostic means information that really isn't very relevant, but you think it is. Subtly diagnostic is information that really is relevant and you kind of don't pay attention to it. One of the things people look at in an M&A deal is whether it adds to earnings. Well turns out that doesn't really correlate very highly with whether it's a good deal or a bad deal.

Other things correlate much higher. For example, are you getting more than what you pay for? That's much more subtle, but much more relevant in terms of determining whether it's a good deal or a bad deal. So the key in all this is to say, we have this torrent of information coming in. How do I sort that and figure out what really should lead me to increase or decrease my probabilities of a particular event happening?

So that's really the third part of decision making. The first is understanding causality much more effectively. The second is understanding how to think about forecasting using the inside versus the outside view. And the third is how to take new information in, sort it properly, and apply what's relevant to improving your understanding of the future.