Somebody becomes rich just by luck

Whenever one is concerned with rare events, events with small probability of occurrence, the Poisson distribution shows up in a natural way. - Falk, Hülser, Reiss

Lets spread some rice corns on a table, each cell represents a person.

Do we expect equal wealth for everyone, given that all have the same virtue and skill?

The Poisson distribution tells us 16 persons get nothing, the probability that you are one of them is 26%.
Over fifty percent get zero or one corn - means they are poor.
Thirtyfour percent are "two or tree corn" people, forming a middle class . 

A person on the sunny side of life get four and two very lucky ones get even 5 corns, They are the rich five percent of society 

#corn	#people	P(X=corn)
0	16	26%
1	14	35%
2	10	23%
3	6	11%
4	1	4%
5	2	1%	upper-crust

Random distribution of wealth leads to substancial inequality. Some people just make it, while most struggle. Is this the secret formula of "the american dream"?

Representativeness Heuristic

Finally I concede myself to start with "Judgment under uncertainty: heuristics and biases" even though not finished with the development part of the Bayesian Network book.

The introductory chapter starts promising: the first cognitive antipattern (bias) discussed 

Representativeness

When people are asked to judge if:
A belongs to class B
A originates from B
A follows from B

they use representativeness, or similiarity ommiting factors that should influence our judgement

Steve is very shy and withdrawn, helpful, but with little interest in people. He has a need for order and passion for detail.

How do people asess if Steve is engaged in a particular occupation (for example farmer, salesman, librarian, physician)?
They compare the description with the stereotype of e.g. a librarian.
Now have a look at the first fallacy with that: we ommit prior probability of outcomes, in fact we should consider that there are many more farmers than librarians before using the stereotype approach.

We fail to take into account prior probability, especially when given worthless evidence, neither wrong nor suggestive but simply worthless. With prior knowledge of a population of two laywers per engineer and the description

Dick is a 30 years old man. He is married with no children. A man of high ability and high motivation, he promises to be quite succesfull in his field. He is well liked by his colleagues.

The experiment shows that overall, the sample judged the chance of Dick being an engineer is fifty fifty!
Prior probabilities play an important role in Bayesian Networks, thus fixing our biased judgement.

"Over Christmas, Allen Newell and I created a thinking machine"

A quote honoring Pittsburgh and its "son" Herbert Simon, who also said (in 1971!): What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention. Actually I desperately need his biography, he seems to be a crucial proponent with the study of human decision-making, behavioral economics and AI.

Best lecture I saw today.