You seem to be focused on the individual level? I was talking about learning on the level of interpersonal relationships and up. As I explain here, I believe any network of agents does Hebbian learning on the network level by default. Sorry about the confusion.
Looking at the large scale, my impression is that the observable dysfunctions correspond pretty well with pressures (or lack thereof) organizations face, which fits the group-level-network-learning view. It seems likely that the individual failings, at least in positions where they matter most, are downstream of that. Call it the institution alignment problem if you will.
I don’t think we have a handle on how to effectively influence existing networks. Forming informal networks of reasonably aligned individuals around relatively object-level purposes seems like a good idea by default.
Hmm. I don’t think I agree that network/group learning exists, distinctly from learning and expectations of the individuals. This is not a denial that higher levels of abstraction are useful for reasoning, but that doesn’t make them ontologically real or distinct from the sum of the parts.
To the extent that we can observe the lower-level components of a system, and there are few enough of them that we can identify the way they add up, we get more accurate predictions by doing so, rather than averaging them out into collective observations.
For this example, the organization “cares” about prosaic things like money, because it’s constituents do. It may also care about it in terms of influence on other orgs or non-constituent humans, of course.
Are you ontologically real or distinct from the sum of your parts? Do you “care” about things only because your constituents do?
I’m suggesting precisely that the group-network levels may be useful in the same sense that the human level or the multicellular-organism level can be useful. Granted, there’s more transfer and overlap when the scale difference is small but that in itself doesn’t necessarily mean that the more customary frame is equally-or-more useful for any given purpose.
Appreciate the caring-about-money point, got me thinking about how concepts and motivations/drives translate across levels. I don’t think there’s a clean joint to carve between sophisticated agents and networks-of-said-agents.
Side note: I don’t know of a widely shared paradigm of thought or language that would be well-suited for thinking or talking about tall towers of self-similar scale-free layers that have as much causal spillover between levels as living systems like to have.
Are you ontologically real or distinct from the sum of your parts? Do you “care” about things only because your constituents do?
Nope. Well, maybe. I’m the sum of parts in a given configuration, even as some of those parts are changed, and as the configuration evolves slightly. Not real, but very convenient to model, since my parts are too numerous and their relationships too complicated to identify individually. But I’m not any more than that sum.
I fully agree with your point that there’s no clean joint to carve between when to use different levels of abstraction for modeling behavior (and especially for modeling “caring” or motivation), but I’ll continue to argue that most organizations are small enough that it’s workable to notice the individuals involved, and you get more fidelity and understanding if you do so.
You seem to be focused on the individual level? I was talking about learning on the level of interpersonal relationships and up. As I explain here, I believe any network of agents does Hebbian learning on the network level by default. Sorry about the confusion.
Looking at the large scale, my impression is that the observable dysfunctions correspond pretty well with pressures (or lack thereof) organizations face, which fits the group-level-network-learning view. It seems likely that the individual failings, at least in positions where they matter most, are downstream of that. Call it the institution alignment problem if you will.
I don’t think we have a handle on how to effectively influence existing networks. Forming informal networks of reasonably aligned individuals around relatively object-level purposes seems like a good idea by default.
Hmm. I don’t think I agree that network/group learning exists, distinctly from learning and expectations of the individuals. This is not a denial that higher levels of abstraction are useful for reasoning, but that doesn’t make them ontologically real or distinct from the sum of the parts.
To the extent that we can observe the lower-level components of a system, and there are few enough of them that we can identify the way they add up, we get more accurate predictions by doing so, rather than averaging them out into collective observations.
For this example, the organization “cares” about prosaic things like money, because it’s constituents do. It may also care about it in terms of influence on other orgs or non-constituent humans, of course.
Are you ontologically real or distinct from the sum of your parts? Do you “care” about things only because your constituents do?
I’m suggesting precisely that the group-network levels may be useful in the same sense that the human level or the multicellular-organism level can be useful. Granted, there’s more transfer and overlap when the scale difference is small but that in itself doesn’t necessarily mean that the more customary frame is equally-or-more useful for any given purpose.
Appreciate the caring-about-money point, got me thinking about how concepts and motivations/drives translate across levels. I don’t think there’s a clean joint to carve between sophisticated agents and networks-of-said-agents.
Side note: I don’t know of a widely shared paradigm of thought or language that would be well-suited for thinking or talking about tall towers of self-similar scale-free layers that have as much causal spillover between levels as living systems like to have.
Nope. Well, maybe. I’m the sum of parts in a given configuration, even as some of those parts are changed, and as the configuration evolves slightly. Not real, but very convenient to model, since my parts are too numerous and their relationships too complicated to identify individually. But I’m not any more than that sum.
I fully agree with your point that there’s no clean joint to carve between when to use different levels of abstraction for modeling behavior (and especially for modeling “caring” or motivation), but I’ll continue to argue that most organizations are small enough that it’s workable to notice the individuals involved, and you get more fidelity and understanding if you do so.