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Decision Theory is multifaceted

Omega, a perfect predictor, flips a coin. If it comes up heads, Omega asks you for $100, then pays you $10,000 if it predict you would have paid if it had come up tails and you were told it was tails. If it comes up tails, Omega asks you for $100, then pays you $10,000 if it predicts you would have paid if it had come up heads and you were told it was heads.

Here there is no question, so I assume it is something like: "What do you do?" or "What is your policy?"

That formulation is analogous to standard counterfactual mugging, stated in this way:

Omega flips a coin. If it comes up heads, Omega will give you 10000 in case you would pay 100 when tails. If it comes up tails, Omega will ask you to pay 100. What do you do?

According to these two formulations, the correct answer seems to be the one corresponding to the first intuition.

Now consider instead this formulation of counterfactual PD:

Omega, a perfect predictor, tells you that it has flipped a coin, and it has come up heads. Omega asks you to pay 100 (here and now) and gives you 10000 (here and now) if you would pay in case the coin landed tails. Omega also explains that, *if* the coin had come up tails—but note that it hasn't—Omega would tell you such and such (symmetrical situation). What do you do?

The answer of the second intuition would be: I refuse to pay here and now, and I would have paid in case the coin had come up tails. I get 10000.

And this formulation of counterfactual PD is analogous to this formulation of counterfactual mugging, where the second intuition refuses to pay.

Is your opinion that

The answer of the second intuition would be: I refuse to pay here and now, and I would have paid in case the coin had come up tails. I get 10000.

is false/not admissible/impossible? Or are you saying something else entirely? In any case, if you could motivate your opinion, whatever that is, you would help me understand. Thanks!

Decision Theory is multifaceted

It seems you are arguing for the position that I called "the first intuition" in my post. Before knowing the outcome, the best you can do is (pay, pay), because that leads to 9900.

On the other hand, as in standard counterfactual mugging, you could be asked: "You know that, this time, the coin came up tails. What do you do?". And here the second intuition applies: the DM can decide to not pay (in this case) and to pay when heads. Omega recognises the intent of the DM, and gives 10000.

Maybe you are not even considering the second intuition because you take for granted that the agent has to decide one policy "at the beginning" and stick to it, or, as you wrote, "pre-commit". One of the points of the post is that it is unclear where this assumption comes from, and what it exactly means. It's possible that my reasoning in the post was not clear, but I think that if you reread the analysis you will see the situation from both viewpoints.

Decision Theory is multifaceted

If the DM knows the outcome is heads, why can't he not pay in that case and decide to pay in the other case? In other words: why can't he adopt the policy (not pay when heads; pay when tails), which leads to 10000?

Decision Theory is multifaceted

The fact that it is "guaranteed" utility doesn't make a significant difference: my analysis still applies. After you know the outcome, you can avoid paying in that case and get 10000 instead of 9900 (second intuition).

Decision Theory is multifaceted

Hi Chris!

Suppose the predictor knows that it writes M on the paper you'll choose N and if it writes N on the paper you'll choose M. Further, if it writes nothing you'll choose M. That isn't a problem since regardless of what it writes it would have predicted your choice correctly. It just can't write down the choice without making you choose the opposite.

My point in the post is that the paradoxical situation occurs when the prediction outcome is communicated to the decision maker. We have a seemingly correct prediction—the one that you wrote about—that ceases to be correct after it is communicated. And later in the post I discuss whether this problematic feature of prediction extends to other scenarios, leaving the question open. What did you want to say exactly?

I was quite skeptical of paying in Counterfactual Mugging until I discovered the Counterfactual Prisoner's Dilemma which addresses the problem of why you should care about counterfactuals given that they aren't factual by definition.

I've read the problem and the analysis I did for (standard) counterfactual mugging applies to your version as well.

The first intuition is that, before knowing the toss outcome, the DM wants to pay in both cases, because that gives the highest utility (9900) in expectation.

The second intuition is that, after the DM knows (wlog) the outcome is heads, he doesn't want to pay anymore in that case—and wants to be someone who pays when tails is the outcome, thus getting 10000.

Goals and short descriptions

I wouldn't say goals as short descriptions are necessarily "part of the world".

Anyway, locality definitely seems useful to make a distinction in this case.

Goals and short descriptions

No worries, I think your comment still provides good food for thought!

Goals and short descriptions

I'm not sure I understand the search vs discriminative distinction. If my hand touches fire and thus immediately moves backwards by reflex, would this be an example of a discriminative policy, because an input signal directly causes an action without being processed in the brain?

About the goal of winning at chess: in the case of minimax search, generates the complete tree of the game using and then selects the winning policy; as you said, this is probably the simplest agent (in terms of Kolmogorov complexity, given ) that wins at chess—and actually wins at any game that can be solved using minimax/backward induction. In the case of , reads the environmental data about chess to assign reward to winning states and elsewhere, and represents an ideal RL procedure that exploits interaction with the environment to generate the optimal policy that maximises the reward function created by . The main feature is that in both cases, when the environment gets bigger and grows, the description length of the two algorithms given doesn't change: you could use minimax or the ideal RL procedure to generate a winning policy even for chess on a larger board, for example. If instead you wanted to use a giant lookup table, you would have to extend your algorithm each time a new state gets added to the environment.

I guess the confusion may come from the fact that is underspecified. I tried to formalise it more precisely by using logic, but there were some problems and it's still work in progress.

By the way, thanks for the links! I hope I'll learn something new about how the brain works, I'm definitely not an expert on cognitive science :)

Goals and short descriptions

The others in the AISC group and I discussed the example that you mentioned more than once. I agree with you that such an agent is not goal-directed, mainly because it doesn't do anything to ensure that it will be able to perform action A even if adverse events happen.

It is still true that action A is a short description of the behaviour of that agent and one could interpret action A as its goal, although the agent is not good at pursuing it ("robustness" could be an appropriate term to indicate what the agent is lacking).

Ok, if you want to clarify—I'd like to—we can have a call, or discuss in other ways. I'll contact you somewhere else.