I would like to see a post on this concept included in the best of 2018, but I also agree that there are reputational risks given the author. I'd like to suggest possible compromise - perhaps we could include the concept, but write our own explanation of the concept instead of including this article?
Smoking lesion is an interesting problem in that it's really not that well defined. If an FDT agent is making the decision, then its reference class should be other FDT agents, so all agents in the same class make the same decision, contrary to the lesion which should affect the probability. The approach that both of us take is to break the causal link from the lesion to your decision. I really didn't express my criticism well above, because what I said also kind of applies to my post. However, the difference is that you are engaging in world counting and in world counting you should see the linkage, while my approach involves explicitly reinterpreting the problem to break the linkage. So my issue is that there seems to be some preprocessing happening before world counting and this means that your approach isn't just a matter of world counting as you claim. In other words, it doesn't match the label on the tin.
I know that you removed the $1000 in that case. But what is the general algorithm or rule that causes you to remove the $1000? What if the hospital cost $999 if you chose $1 or $1000 otherwise.
I guess it seems to me that once you've removed the $1000 you've removed the challenging element of the problem, so solving it doesn't count for very much.
Admittedly they could have been clearer, but I still think you're misinterpreting the FDT paper. Sorry, what I meant was that smoking was correlated with an increased chance of cancer. Not that there was any causal link.
I really don't like the term jumbled as some people would likely object much more to being labelled as jumbled than as a contextualiser. The rest of this comment makes some good points, but sometimes less is more. I do want to edit this article, but I think I'll mostly engage with Zack's points and reread the article.
Yeah, there's definitely a tension between being a social-linguistic construct and being pragmatically useful (such as what you might need for a planning agent). I don't completely know how to resolve this yet, but this post makes a start by noting that in additional to the social linguistic elements, the strength of the physical linkage between elements is important as well. My intuition is that there are a bunch of properties that make something more or less counterfactual and the social-linguistic conventions are about a) which of these properties are present when the problem is ambiguous b) which of these properties need to be satisfied before we accept a counterfactual as valid.
Yeah, I agree that I haven't completely engaged with the issue of "corrupted hardware", but it seems like any attempt to do this would require so much interpretation that I wouldn't expect to obtain agreement over whether I had interpreted it correctly. In any case, my aim is purely to solve counterfactuals for non-corrupted agents, at least for now. But glad to see that someone agrees with me about socio-linguistic conventions :-)
My issue is that you are doing implicit pre-processing on some of these problems and sweeping it under the rug. Do you actually have any kind of generalised scheme, including all pre-processing steps?
The problem is usually set up so that they gain utility from smoking, but choose not to smoke.
In any case, you seem to have ignored the part of the problem where smoking increases chance of the lesion and hence cancer. So there seems to be some implicit normalisation? What's your exact process there?
Also, why are you ignoring the $1000 checkup cost in the cosmic ray problem? That's the correct way to reason, but you haven't provided a justification for it.