Ludic fallacy
The ludic fallacy, proposed by Nassim Nicholas Taleb in his book The Black Swan, is "the misuse of games to model real-life situations". Taleb explains the fallacy as "basing studies of chance on the narrow world of games and dice". The adjective ludic originates from the Latin noun ludus, meaning "play, game, sport, pastime".
Description
The fallacy is a central argument in the book and a rebuttal of the predictive mathematical models used to predict the future – as well as an attack on the idea of applying naïve and simplified statistical models in complex domains. According to Taleb, statistics is applicable only in some domains, for instance casinos in which the odds are visible and defined. Taleb's argument centers on the idea that predictive models are based on platonified forms, gravitating towards mathematical purity and failing to take various aspects into account:- It is impossible to be in possession of the entirety of available information.
- Small unknown variations in the data could have a huge impact. Taleb differentiates his idea from that of mathematical notions in chaos theory.
- Theories or models based on empirical data are claimed to be flawed as they may not be able to predict events which are previously unobserved, but have tremendous impact, also known as black swan theory.
Examples
Example: Suspicious coin
One example given in the book is the following thought experiment. Two people are involved:- Dr. John who is regarded as a man of science and logical thinking
- Fat Tony who is regarded as a man who lives by his wits
- Dr. John says that the odds are not affected by the previous outcomes so the odds must still be 50:50.
- Fat Tony says that the odds of the coin coming up heads 99 times in a row are so low that the initial assumption that the coin had a 50:50 chance of coming up heads is most likely incorrect. "The coin gotta be loaded. It can't be a fair game."
In classical terms, statistically significant events, i.e. unlikely events, should make one question one's model assumptions. In Bayesian statistics, this can be modelled by using a prior distribution for one's assumptions on the fairness of the coin, then Bayesian inference to update this distribution. This idea is modelled in the Beta distribution.