Today’s a good day to learn about the very interesting, Ludic fallacy!
What is it?
It describes how we mistake the kind of uncertainty found in games for the kind of uncertainty found in actual, real life.
A man considers going to a job interview. He recently studied statistics and utility theory in college and performed well in the exams. Considering whether to take the interview, he tries to calculate the probability he will get the job versus the cost of the time spent.
This young job seeker forgets that real life has more variables than the small set he has chosen to estimate. Even with a low probability of success, a really good job may be worth the effort of going to the interview.
Will he enjoy the process of the interview? Will his interview technique improve regardless of whether he gets the job or not? Even the statistics of the job business are non-linear. What other jobs could come the man's way by meeting the interviewer? Might there be a possibility of a very high pay-off in this company that he has not thought of? 
Where does it occur?
When people think about risk and uncertainty, their ideas are often confined to a particular domain.
But what you don’t understand is that the real risk to your investment has nothing to do with your spreadsheets and your gamification of the odds.
The real risk exists outside the domain (in the real world).
Why do I need to know?
Taleb's argument centers on the idea that predictive models are based on platonified forms, gravitating towards mathematical purity and failing to take some key ideas into account: It is impossible to be in possession of all the information.
Very small unknown variations in the data could have a huge impact. Taleb does differentiate his idea from that of mathematical notions in chaos theory, e.g. the butterfly effect. 
References & Studies: -
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