I don't think chess is a particularly good example. Given some finite amount of search, chess engines use heuristics to estimate the winning probability of some position (or otherwise give it a score), but as Winning isn't enough says:
Presumably we should only trust a heuristic based on its past performance insofar as we have some reason to think that similar mechanisms that caused it to work previously are at play in our current problem
Which is the case for chess, in which heuristics work as well as they do because they have been trained (in modern engines) on a large amount of games, so that whatever new situation they encounter can be considered to be in distribution. If this isn't the case, engines can fail spectacularly. And in reality we can't assume the future will be in distribution.
Action A is whatever action is in the first position, and action B in the second, in whatever list of actions we have. When I say, "let’s swap the consequences of A and B", that's equivalent to saying "let's consider a second scenario, that need not have anything else to do with the first one, except that the consequences of the action in the new first entry are identical to those of the second entry of the original scenario (and vice versa), and the number of actions and sets of outcomes stay the same". We are considering consequentialists, and, by definition, as far as their decisionmaking procedure is concerned, two scenarios where the actions in each entry have identical (uncertain/unaware) consequences are the same scenario. Though, if it helps, you can imagine we have two buttons, labelled A and B, so that swapping their consequences can be achieved by swapping whatever their cables are connected to.
I don't think chess is a particularly good example. Given some finite amount of search, chess engines use heuristics to estimate the winning probability of some position (or otherwise give it a score), but as Winning isn't enough says:
Which is the case for chess, in which heuristics work as well as they do because they have been trained (in modern engines) on a large amount of games, so that whatever new situation they encounter can be considered to be in distribution. If this isn't the case, engines can fail spectacularly. And in reality we can't assume the future will be in distribution.
Action A is whatever action is in the first position, and action B in the second, in whatever list of actions we have. When I say, "let’s swap the consequences of A and B", that's equivalent to saying "let's consider a second scenario, that need not have anything else to do with the first one, except that the consequences of the action in the new first entry are identical to those of the second entry of the original scenario (and vice versa), and the number of actions and sets of outcomes stay the same". We are considering consequentialists, and, by definition, as far as their decisionmaking procedure is concerned, two scenarios where the actions in each entry have identical (uncertain/unaware) consequences are the same scenario. Though, if it helps, you can imagine we have two buttons, labelled A and B, so that swapping their consequences can be achieved by swapping whatever their cables are connected to.