“learn” in the sense that their behavior adapts to their environment.
I want a new word for this. “Learn” vs “Adapt” maybe. Learn means updating of symbolic references (maps) while Adapt means something like responding to stimuli in a systematic way.
Not quite what we were trying to say in the post. Rather than tradeoffs being decided on reflection, we were trying to talk about the causal-inference-style “explaining away” which the reflection gives enough compute for. In Johannes’s example, the idea is that the sadist might model the reward as coming potentially from two independent causes: a hardcoded sadist response, and “actually” valuing the pain caused. Since the probability of one cause, given the effect, goes down when we also know that the other cause definitely obtained, the sadist might lower their probability that they actually value hurting people given that (after reflection) they’re quite sure they are hardcoded to get reward for it. That’s how it’s analagous to the ant thing.
Suppose you have a randomly activated (not dependent on weather) sprinkler system, and also it rains sometimes. These are two independent causes for the sidewalk being wet, each of which are capable of getting the job done all on their own. Suppose you notice that the sidewalk is wet, so it definitely either rained, sprinkled, or both. If I told you it had rained last night, your probability that the sprinklers went on (given that it is wet) should go down, since they already explain the wet sidewalk. If I told you instead that the sprinklers went on last night, then your probability of it having rained (given that it is wet) goes down for a similar reason. This is what “explaining away” is in causal inference. The probability of a cause given its effect goes down when an alternative cause is present.
In the post, the supposedly independent causes are “hardcoded ant-in-mouth aversion” and “value of eating escamoles”, and the effect is negative reward. Realizing that you have a hardcoded ant-in-mouth aversion is like learning that the sprinklers were on last night. The sprinklers being on (incompletely) “explain away” the rain as a cause for the sidewalk being wet. The hardcoded ant-in-mouth aversion explains away the-amount-you-value-escamoles as a cause for the low reward.
I’m not totally sure if that answers your question, maybe you were asking “why model my values as a cause of the negative reward, separate from the hardcoded response itself”? And if so, I think I’d rephrase the heart of the question as, “what do the values in this reward model actually correspond to out in the world, if anything? What are the ‘real values’ which reward is treated as evidence of?” (We’ve done some thinking about that and might put out a post on that soon.)
This is fascinating and I would love to hear about anything else you know of a similar flavor.
Seconded!!
Better terminology for the phenomenon of “making sense” in the above way?