Charlie Munger on Psychology of Human Misjudgement

This is a truly exceptional video of Charlie Munger doing a talk at Harvard University. I have learned so much from Munger so far. Hope it never ends! I find these biases interesting and might write about some in later posts.

Bayes’ theorem

English: Tree diagrams illustrating how Bayes’ theorem relates probabilities associated with two variables A and B. (Photo credit: Wikipedia)

This will be my first post regarding math, or more precise probability theory. The great Bayes’ theorem will be taken on a closer look. I have kind of forgotten about it, I’m ashamed to say. I am more interested in learning so I have no problem with copying stuff from other places. Im not a believer of redoing everything in this world so therefore this section is copied from Wikipedia where there is an great example. It follows:

Suppose someone told you they had a nice conversation with someone on the train. Not knowing anything else about this conversation, the probability that they were speaking to a woman is 50%. Now suppose they also told you that this person had long hair. It is now more likely they were speaking to a woman, since most long-haired people are women. Bayes’ theorem can be used to calculate the probability that the person is a woman.

To see how this is done, let

represent the event that the conversation was held with a woman, and

denote the event that the conversation was held with a long-haired person.

It can be assumed that women constitute half the population for this example. So, not knowing anything else, the probability that occurs is

Suppose it is also known that 75% of women have long hair, which we denote as

(read: the probability of event given event is 0.75).

Likewise, suppose it is known that 30% of men have long hair, or

,

where is the complementary event of , i.e., the event that the conversation was held with a man (assuming that every human is either a man or a woman).

Our goal is to calculate the probability that the conversation was held with a woman, given the fact that the person had long hair, or, in our notation, . Using the formula for Bayes’ theorem, we have:

where we have used the law of total probability. The numeric answer can be obtained by substituting the above values into this formula. This yields

i.e., the probability that the conversation was held with a woman, given that the person had long hair, is about 71%.

Filters Against Folly

I am currently reading Garrett Hardin‘s Filters Against Folly. So far it is really interesting.

I’m gonna do this post as a work in progress to what I learn in the book. It is basically a book on how to develop your thinking.

Hardin is talking about different filters for the layman as an protection against being fooled by the expert and divides academia into natural sciences and soft sciences. He discuss three types of thinking:

1. Literate (Understanding words)
2. Numerical (How much and proportions)
3. Ecolate (And then what…)

Literate:

Words are used to promote thought or prevent thought. You should watch out for words such as infinity, must, non-negotiable, forever, never which tries to deny others from their right of responding.

Faced with conflicting views, one must  ask what operations is

implied by these statements.

Numerate:

Coming soon

Ecolate:

Coming soon

Wisdom of Crowds II Final Thoughts and Lessons

The Wisdom of Crowds (Photo credit: Wikipedia)

I’m now done with the book. I will give it 3.5 stars. It did not give very much knowledge, but for someone who is missing elemantary skills in psychology, and human misjudgment it is a good introduction. I especially liked the second half of the book about the structure of corporations (and how to get an effective board) and democracy. What was missing was more analysis on psychological effects that work together in the same direction. In his examples Surowiecki did mostly one focus on one and give that stand alone point all credit of the outcome.

Main point in Wisdom of the Crowds:

Criterias to be met for building a smart crowed (from Wiki):

Criteria	Description
Diversity of opinion	Each person should have private information even if it’s just an eccentric interpretation of the known facts.
Independence	People’s opinions aren’t determined by the opinions of those around them.
Decentralization	People are able to specialize and draw on local knowledge.
Aggregation	Some mechanism exists for turning private judgments into a collective decision.

Based on Surowiecki’s book, Oinas-Kukkonen captures the wisdom of crowds approach with the following eight conjectures:

1. It is possible to describe how people in a group think as a whole.
2. In some cases, groups are remarkably intelligent and are often smarter than the smartest people in them.
3. The three conditions for a group to be intelligent are diversity, independence, and decentralization.
4. The best decisions are a product of disagreement and contest.
5. Too much communication can make the group as a whole less intelligent.
6. Information aggregation functionality is needed.
7. The right information needs to be delivered to the right people in the right place, at the right time, and in the right way.
8. There is no need to chase the expert.

This applies to groups but I think it also applies to every individual’s mind. You should gain knowledge from different fields in science and and structurally force yourself to look from different perspectives and focus on disconfirming facts (number 4 in the list above). If you do that I feel certain that the results will be better in analysis.

Wisdom of Crowds

The Wisdom of Crowds (Photo credit: Wikipedia)

I’m currently reading this book. There are some interesting points in this book and I have only heard good things about it. The thesis of the book is that the crowd often (under certain conditions) will do much better than most or any individual. The classical example is the candy jar. How many candies are there in the glass jar? Some people will be close in estimating and some will be highly wrong, but the average usually end up extremely close to the real number of candys. To me, this really simple form is not that interesting. The interesting part is more complex stuff; I liked the discussion about descentralized or centralized gruops and governments.

But so far (I have only read 150 pages) I think the author has claimed too much and often fails in ceteris paribus analysis. Already, many times I have find other explanations and interacting terms (mainly from psychology) and especially comments on the stock market. With that much data, of course you will find something that will support your hypothesis.

What I so far has taken from the book is that you really should diversify your thinking and use checklists so that you in your own mind creates a diversified group, which is shown in this book will come to better results in analysis.

More ideas and thoughts on this book might be included in another post.

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This is my first blog post ever. Hopefully not my last.

The mission of this blog is for me to write about things I learn, whether it is in economics, psychology or anything from a biography or hard sciences.

And additionally; I like quotes so I might finish off with one or two in my posts.

”There is no substitute for hard work” Thomas Edison