"But there are situations where the approximation isn’t very good, such as when it’s the 9th inning and the game is tied. In that case, a decision that increases the probability of scoring 1 run but decreases the probability of scoring multiple runs is actually the right choice."
I understood that the "full" model includes these considerations.
I was bringing up that edge case specifically because 'winning' is the obvious optimization, but not necessarily the correct one.
Bottom of the 9th bases are loaded and the guy at bat hit's a home run, that's the kind of thing that sticks with people and makes more money in the long run than a bunt that get 1 run.
"But there are situations where the approximation isn’t very good, such as when it’s the 9th inning and the game is tied. In that case, a decision that increases the probability of scoring 1 run but decreases the probability of scoring multiple runs is actually the right choice."
I understood that the "full" model includes these considerations.