For the purposes of the theorem an outcome is just a meaningless element of a finite set over which we can set probability distributions. Whether or not the theorem applies to some actual physical agent does depend on how we define what an outcome is. Notice that the definition of reflective stability requires an agent to behave a certain way in all succession setups, and therefore we must consider all scenarios. So, if “I climb the nearby mountain” is an action, and “I don’t climb the nearby mountain and the weather is sunny” is an outcome, it must be possible to create a scenario where climbing the nearby mountain has an 100% chance of resulting in not climbing the nearby mountain, and the weather being sunny. You must choose some partition of trajectories of the world into outcomes such that it is in principle possible to create any scenario, and if your agent is a reflectively stable consequentialist with respect to that partition, the theorem says that it will also be an expected utility maximiser with respect to it. Partitioning the trajectories is actually forced by the fact that reality is continuous but the theorem only works with a finite set of outcomes.
Consequentialism is also not as clearly false about people as you make it out to be (although it is false). “I climb the nearby mountain” is clearly not an action that can be taken in an arbitrary situation, whereas we are assuming that the set of available actions is exactly the same in all scenarios. What is always available is some discretization of the set of possible signals we can send to our muscles at some instant. In the example I gave of a corporation, the first action is not “choose the first possible successor”, it is just action 1, which in that scenario results in choosing a successor, but in some other scenario can result in choosing a different successor, or maybe walking two steps right. You can choose different levels of granularity depending on the kind of thing you are trying to describe. In the case of climbing a mountain, I think that considering it as an atomic action, or even as an action at all is not the right way of seeing things. The vast, vast majority of all the possible actual action sequences someone can take when in front of a mountain simply result in them falling to the ground. Achieving the goal of climbing a mountain is not trivial, and it requires different actions depending on what happens to be in front of them, so it should be considered a consequence instead.
You can of course insist that you care directly about whatever you define an action to be, and that therefore it must be considered as a part of the outcomes too if we want to be viewed as consequentialists. I think that’s probably not unreasonable, but it does break the theorem. Purely behavioral theorems are insufficient to describe human values. Reasoning clearly about how someone can both want to climb a mountain and reach the top will require thinking about the mechanisms in common between both kinds of goals, which will break the setup in many other ways.
So, if “I climb the nearby mountain” is an action, and “I don’t climb the nearby mountain and the weather is sunny” is an outcome, it must be possible to create a scenario where climbing the nearby mountain has an 100% chance of resulting in not climbing the nearby mountain, and the weather being sunny.
Okay, but this action/outcome combination is logically impossible because it would require both climbing and not climbing the mountain. According to Jeffrey’s theory, “I don’t climb the nearby mountain and the weather is sunny” can’t be an outcome. Because, if is an action, only events of the type and are are outcomes, but or are not. This makes sense, since and form a logical partition of , i.e., they are 1) mutually exclusive and 2) jointly logically equivalent to .
However, this is not the case if we (unlike Jeffrey) regard outcomes as consequences, since consequences generally don’t imply their causes (including actions). The same set of macroscopic end states (outcomes) could have been produced by different initial states (causes). This has to do with the general increase in entropy over time, a basic fact about causality. Stirring or shaking a drink are two different actions, but they may result macroscopically in the exact same causal outcome. Likewise, the outcome of a broken egg doesn’t imply the action which broke it.
In that realistic view, possible outcomes are not jointly logically equivalent to their actions, which would mean two agents who assign the same expected utilities to the outcomes of a scenario need not assign the same utility (or probability) to their corresponding actions. They may inherently like some action for its own sake, and that needn’t be reflected in different evaluations of the outcomes—in contrast to Jeffrey’s theory, where the “outcomes” always imply their actions.
Consequentialism is also not as clearly false about people as you make it out to be (although it is false). “I climb the nearby mountain” is clearly not an action that can be taken in an arbitrary situation, whereas we are assuming that the set of available actions is exactly the same in all scenarios. What is always available is some discretization of the set of possible signals we can send to our muscles at some instant.
I think pure “muscle signals” is almost never what is meant in decision theory when talking about “actions”. An action is usually something like “taking an umbrella”, which is not available always. And strictly speaking, someone might have a stroke or a temporary paralysis and lose some of his muscle signals.
Moreover, even for actual muscle signals, someone might simply have a preference for moving his legs in some situation (someone after a long flight, say, or a dancer, or an excited child), apart from any expected consequences/outcomes. Which would already violate consequentialism.
(I agree that climbing a mountain is not usefully described as an action if there is a realistic chance of failure, in which case it would have to be modeled as an outcome.)
For the purposes of the theorem an outcome is just a meaningless element of a finite set over which we can set probability distributions. Whether or not the theorem applies to some actual physical agent does depend on how we define what an outcome is. Notice that the definition of reflective stability requires an agent to behave a certain way in all succession setups, and therefore we must consider all scenarios. So, if “I climb the nearby mountain” is an action, and “I don’t climb the nearby mountain and the weather is sunny” is an outcome, it must be possible to create a scenario where climbing the nearby mountain has an 100% chance of resulting in not climbing the nearby mountain, and the weather being sunny. You must choose some partition of trajectories of the world into outcomes such that it is in principle possible to create any scenario, and if your agent is a reflectively stable consequentialist with respect to that partition, the theorem says that it will also be an expected utility maximiser with respect to it. Partitioning the trajectories is actually forced by the fact that reality is continuous but the theorem only works with a finite set of outcomes.
Consequentialism is also not as clearly false about people as you make it out to be (although it is false). “I climb the nearby mountain” is clearly not an action that can be taken in an arbitrary situation, whereas we are assuming that the set of available actions is exactly the same in all scenarios. What is always available is some discretization of the set of possible signals we can send to our muscles at some instant. In the example I gave of a corporation, the first action is not “choose the first possible successor”, it is just action 1, which in that scenario results in choosing a successor, but in some other scenario can result in choosing a different successor, or maybe walking two steps right. You can choose different levels of granularity depending on the kind of thing you are trying to describe. In the case of climbing a mountain, I think that considering it as an atomic action, or even as an action at all is not the right way of seeing things. The vast, vast majority of all the possible actual action sequences someone can take when in front of a mountain simply result in them falling to the ground. Achieving the goal of climbing a mountain is not trivial, and it requires different actions depending on what happens to be in front of them, so it should be considered a consequence instead.
You can of course insist that you care directly about whatever you define an action to be, and that therefore it must be considered as a part of the outcomes too if we want to be viewed as consequentialists. I think that’s probably not unreasonable, but it does break the theorem. Purely behavioral theorems are insufficient to describe human values. Reasoning clearly about how someone can both want to climb a mountain and reach the top will require thinking about the mechanisms in common between both kinds of goals, which will break the setup in many other ways.
I hope this made sense.
Okay, but this action/outcome combination is logically impossible because it would require both climbing and not climbing the mountain. According to Jeffrey’s theory, “I don’t climb the nearby mountain and the weather is sunny” can’t be an outcome. Because, if
However, this is not the case if we (unlike Jeffrey) regard outcomes as consequences, since consequences generally don’t imply their causes (including actions). The same set of macroscopic end states (outcomes) could have been produced by different initial states (causes). This has to do with the general increase in entropy over time, a basic fact about causality. Stirring or shaking a drink are two different actions, but they may result macroscopically in the exact same causal outcome. Likewise, the outcome of a broken egg doesn’t imply the action which broke it.
In that realistic view, possible outcomes are not jointly logically equivalent to their actions, which would mean two agents who assign the same expected utilities to the outcomes of a scenario need not assign the same utility (or probability) to their corresponding actions. They may inherently like some action for its own sake, and that needn’t be reflected in different evaluations of the outcomes—in contrast to Jeffrey’s theory, where the “outcomes” always imply their actions.
I think pure “muscle signals” is almost never what is meant in decision theory when talking about “actions”. An action is usually something like “taking an umbrella”, which is not available always. And strictly speaking, someone might have a stroke or a temporary paralysis and lose some of his muscle signals.
Moreover, even for actual muscle signals, someone might simply have a preference for moving his legs in some situation (someone after a long flight, say, or a dancer, or an excited child), apart from any expected consequences/outcomes. Which would already violate consequentialism.
(I agree that climbing a mountain is not usefully described as an action if there is a realistic chance of failure, in which case it would have to be modeled as an outcome.)