“kill all humans, then shut down” is probably the action that most minimizes change. Leaving those buggers alive will cause more (and harder to predict) change than anything else the agent might do.
There’s no way to talk about this in the abstract sense of change—it has to be differential from a counterfactual (aka: causal), and can only be measured by other agents’ evaluation functions. The world changes for lots of reasons, and an agent might have most of it’s impact by PREVENTING a change, or by FAILING to change something that’s within it’s power. Asimov’s formulation included this understanding: A robot may not injure a human being or, through inaction, allow a human being to come to harm.
I agree it doesn’t make sense to talk about this kind of change as what we want impact measures to penalize, but i think you could talk about this abstract sense of change. You could have an agent with beliefs about the world state, and some distance function over world states, and then penalize change in observed world state compared to some counterfactual.
This kind of change isn’t the same thing as perceived impact, however.
While I see the appeal of having an umbrella description of past approaches, I don’t think we explain the goal of impact measure research in terms of the average proposal so far, but rather, by what impact is. As I argued in the first half of Reframing Impact, people impact each other by changing the other person’s ability to achieve their goal. This is true no matter which impact measure you prefer.
I think that proposals generally fail or succeed to the extent that they are congruent with this understanding of impact. In particular, an impact measure is good for us to the extent that it penalizes policies which destroy our ability to get what we want.
Possibly, but I think the “amount of change to the world” is broader umbrella term that covers more of the methods that people have been proposing.
“kill all humans, then shut down” is probably the action that most minimizes change. Leaving those buggers alive will cause more (and harder to predict) change than anything else the agent might do.
There’s no way to talk about this in the abstract sense of change—it has to be differential from a counterfactual (aka: causal), and can only be measured by other agents’ evaluation functions. The world changes for lots of reasons, and an agent might have most of it’s impact by PREVENTING a change, or by FAILING to change something that’s within it’s power. Asimov’s formulation included this understanding: A robot may not injure a human being or, through inaction, allow a human being to come to harm.
Yep, been dealing with that issue for some time now ^_^
https://arxiv.org/abs/1705.10720
I agree it doesn’t make sense to talk about this kind of change as what we want impact measures to penalize, but i think you could talk about this abstract sense of change. You could have an agent with beliefs about the world state, and some distance function over world states, and then penalize change in observed world state compared to some counterfactual.
This kind of change isn’t the same thing as perceived impact, however.
While I see the appeal of having an umbrella description of past approaches, I don’t think we explain the goal of impact measure research in terms of the average proposal so far, but rather, by what impact is. As I argued in the first half of Reframing Impact, people impact each other by changing the other person’s ability to achieve their goal. This is true no matter which impact measure you prefer.
I think that proposals generally fail or succeed to the extent that they are congruent with this understanding of impact. In particular, an impact measure is good for us to the extent that it penalizes policies which destroy our ability to get what we want.