In philosophy, Pascal's mugging is a thought experiment demonstrating a problem in expected utility maximization.
A rational agent should choose actions whose outcomes, when weighted by their probability, have higher utility.
[2] The term "Pascal's mugging" to refer to this problem was originally coined by Eliezer Yudkowsky in the LessWrong forum.
[3][2] Philosopher Nick Bostrom later elaborated the thought experiment in the form of a fictional dialogue.
[2] Subsequently, other authors published their own sequels to the events of this first dialogue, adopting the same literary style.
In one example, the mugger succeeds by promising Pascal 1,000 quadrillion happy days of life.
In one of Yudkowsky's examples, the mugger succeeds by saying "give me five dollars, or I'll use my magic powers from outside the Matrix to run a Turing machine that simulates and kills
On the one side, by multiplying an expected utility calculation, assuming loss of five dollars to be valued at
If the person being mugged agrees to this sequence of logic, then they can be exploited repeatedly for all of their money, resulting in a Dutch-book, which is typically considered irrational.
[7][8] Some of the arguments concerning this paradox affect not only the expected utility maximization theory, but may also apply to other theoretical systems, such as consequentialist ethics, for example.
[10] Pascal's mugging may also be relevant when considering low-probability, high-stakes events such as existential risk or charitable interventions with a low probability of success but extremely high rewards.
[7][11] Another approach is to use Bayesian reasoning to (qualitatively) judge the quality of evidence and probability estimates rather than naively calculate expectations.
[6] Other approaches are to penalize the prior probability of hypotheses that argue that we are in a surprisingly unique position to affect large numbers of other people who cannot symmetrically affect us,[note 3] reject providing the probability of a payout first,[15] or abandon quantitative decision procedures in the presence of extremely large risks.