No, you're just bringing up a separate issue from the one implied by the parent.

Your own risk tolerance, the values of your life, the opportunity costs outside of this one -- those are real complexities but we ignore them to answer a question about how best to maximize your bankroll within the current situation.

If you have 100 dollars..and you lose 50% of it on a fair coin bet, you now have $50.

Say you play again..You win. Youll only have $75 ($50 times 150%.)

Thats the basis of risk analysis. That losing and winning arent equal for finite bankrolls just because their probabilities are equal.

The actionable takeaway for me is to try and solve this problem with some machine learning approaches, since it's simple enough that we compare to exact solutions, but also interesting enough.

The takeaway from a social point of view is that even people working in finance are not very good at playing these games individually. (The companies they work for might still be doing much better as a collaborative effort, and with more simulations and backtesting used.)

In the Haghani & Dewey 2016 experiment log utility is violated in two ways:

- A $250 cap on payouts.

- The fact that $250 payout to participants was on TOP of participant's current wealth, making utility approximately linear.