I wonder:
if you had an agent that obviously did have goals (let's say, a player in a game, whose goal is to win, and who plays the optimal strategy) could you deduce those goals from behavior alone?
Let's say you're studying the game of Connect Four, but you have no idea what constitutes "winning" or "losing." You watch enough games that you can map out a game tree. In state X of the world, a player chooses option A over other possible options, and so on. From that game tree, can you deduce that the goal of the game was to get four pieces in a row?
I don't know the answer to this question. But it seems important. If it's possible to identify, given a set of behaviors, what goal they're aimed at, then we can test behaviors (human, animal, algorithmic) for hidden goals. If it's not possible, that's very important as well; because that means that even in a simple game, where we know by construction that the players are "rational" goal-maximizing agents, we can't detect what their goals are from their behavior.
That would mean that behaviors that "seem" goal-less, programs that have no line of code representing a goal, may in fact be behaving in a way that corresponds to maximizing the likelihood of some event; we just can't deduce what that "goal" is. In other words, it's not as simple as saying "That program doesn't have a line of code representing a goal." Its behavior may encode a goal indirectly. Detecting such goals seems like a problem we would really want to solve.
I suspect that "has goals" is ultimately a model, rather than a fact. To the extent that an agent's behavior maximizes a particular function, that agent can be usefully modeled as an optimizer. To the extent that an agent's behavior exhibits signs of poor strategy, such as vulnerability to dutch books, that agent may be better modeled as an algorithm-executer.
This suggests that "agentiness" is strongly tied to whether we are smart enough to win against it.
Anyone who does not believe mental states are ontologically fundamental - ie anyone who denies the reality of something like a soul - has two choices about where to go next. They can try reducing mental states to smaller components, or they can stop talking about them entirely.
In a utility-maximizing AI, mental states can be reduced to smaller components. The AI will have goals, and those goals, upon closer examination, will be lines in a computer program.
But in the blue-minimizing robot, its "goal" isn't even a line in its program. There's nothing that looks remotely like a goal in its programming, and goals appear only when you make rough generalizations from its behavior in limited cases.
Philosophers are still very much arguing about whether this applies to humans; the two schools call themselves reductionists and eliminativists (with a third school of wishy-washy half-and-half people calling themselves revisionists). Reductionists want to reduce things like goals and preferences to the appropriate neurons in the brain; eliminativists want to prove that humans, like the blue-minimizing robot, don't have anything of the sort until you start looking at high level abstractions.
I took a similar tack asking ksvanhorn's question in yesterday's post - how can you get a more accurate picture of what your true preferences are? I said:
A more practical example: when people discuss cryonics or anti-aging, the following argument usually comes up in one form or another: if you were in a burning building, you would try pretty hard to get out. Therefore, you must strongly dislike death and want to avoid it. But if you strongly dislike death and want to avoid it, you must be lying when you say you accept death as a natural part of life and think it's crass and selfish to try to cheat the Reaper. And therefore your reluctance to sign up for cryonics violates your own revealed preferences! You must just be trying to signal conformity or something.
The problem is that not signing up for cryonics is also a "revealed preference". "You wouldn't sign up for cryonics, which means you don't really fear death so much, so why bother running from a burning building?" is an equally good argument, although no one except maybe Marcus Aurelius would take it seriously.
Both these arguments assume that somewhere, deep down, there's a utility function with a single term for "death" in it, and all decisions just call upon this particular level of death or anti-death preference.
More explanatory of the way people actually behave is that there's no unified preference for or against death, but rather a set of behaviors. Being in a burning building activates fleeing behavior; contemplating death from old age does not activate cryonics-buying behavior. People guess at their opinions about death by analyzing these behaviors, usually with a bit of signalling thrown in. If they desire consistency - and most people do - maybe they'll change some of their other behaviors to conform to their hypothesized opinion.
One more example. I've previously brought up the case of a rationalist who knows there's no such thing as ghosts, but is still uncomfortable in a haunted house. So does he believe in ghosts or not? If you insist on there being a variable somewhere in his head marked $belief_in_ghosts = (0,1) then it's going to be pretty mysterious when that variable looks like zero when he's talking to the Skeptics Association, and one when he's running away from a creaky staircase at midnight.
But it's not at all mysterious that the thought "I don't believe in ghosts" gets reinforced because it makes him feel intelligent and modern, and staying around a creaky staircase at midnight gets punished because it makes him afraid.
Behaviorism was one of the first and most successful eliminationist theories. I've so far ignored the most modern and exciting eliminationist theory, connectionism, because it involves a lot of math and is very hard to process on an intuitive level. In the next post, I want to try to explain the very basics of connectionism, why it's so exciting, and why it helps justify discussion of behaviorist principles.