@UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @dsmith @uh that sort of behaviour of the buttons depends on how they interact causally with each other, I would imagine, and this in some cases and under certain conditions generates a sort of network pattern, which just is the ensemble of those causal interactions. Something along these lines would be the typical causal picture?
@dcm@social.sunet.se @dcm@bookwyrm.social @NicoleCRust @dsmith @uh in the example, they aren’t interacting at all. It’s just someone going around randomly tying bits of string between pairs of them.
@UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @dsmith @uh I see, then I don't think I understand the example: don't they interact through weight, friction, etc., once they are connected by the string?
@dcm@social.sunet.se @dcm@bookwyrm.social @NicoleCRust @dsmith @uh they do, but the thing that’s at issue is why they are suddenly all connected. What ‘caused’ that?
@UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @dsmith @uh I looked for videos of this experiment to try and understand it better, but I don't seem to have found one.
I would think that the idea would be that what causes that is just the ensemble of causal relations between the buttons: weight, friction, etc with just the right values to make that happen. But my grasp of the case is not great, so I'm not sure.
@dcm@social.sunet.se @dcm@bookwyrm.social @NicoleCRust @dsmith @uh it’s just a thought experiment, Dimitri. You could replace it with anything that can be described with a network: say a bunch of new people move into a neighbourhood, they bump into each other randomly, two at a time, and become acquainted. When they meet an acquaintance, they pass on new information about the neighbourhood. At some point, when enough of them have become acquainted (ie formed pairwise ties) info will spread to everyone
@UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @dsmith @uh ah, the description on the book seemed to suggest it was an actual experiment/demonstration, since it talks about connecting physical elements to each other and then mentions, implying a partial contrast, results from a simulation of the scenario.
But in the neighbourhood case, what is the puzzle? Information spreads by people meeting each other, right?
@dcm@social.sunet.se @UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @uh
Yes, but more than that. The importance of "pairwise ties" deserves to be highlighted. Bidirectionality within those subsystems offers robustness to the system as a whole, protecting the dynamic from a freshly introduced disruption, like a new neighbour moving in.
"What caused that?" we might ask. Ongoing interactions? Low turnover in the neighbourhood? Families growing up together?
@dcm@social.sunet.se @UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @uh
DST asks us to accept one little add-on premise: effects can turn around and influence their causes.
Accepting that premise offers more than a re-description of the familiar causal picture. It washes away boundaries that have long cursed investigators: prenatal/postnatal, heredity/environment, discipline#1/discipline#2, et al.
These reconceptualizations are considered advances because they inspire new integrated and highly coherent views of our world.
@dsmith @UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @uh that additional premise is though a normal thing for any causal system with feedback loops and similar, right? This is also accommodated in non-DST accounts.
@dcm@social.sunet.se @UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @uh
I'm good with that, but I'm no longer so sure what you mean by the "mainstream causal view" you have mentioned. To me, the mainstream causal view is more linear, unidirectional, reductionist, and a better fit for our traditional variable-controlling experimental method.
As well, to group "any causal system with feedback loops" with DST and be as explanatorily successful, we'll absolutely need to involve constraints. Can't do without 'em.
@dcm@social.sunet.se @dcm@bookwyrm.social @NicoleCRust @dsmith @uh
yes, but the contrast is between the case where information spreads to everyone. (network is connected) and where it does not (network is not connected).
This is where the phase transition as a function of number of pairwise links comes in…
@dcm@social.sunet.se @dcm@bookwyrm.social @NicoleCRust @dsmith @uh
and it works with the social network just like with the button network, because it’s a mathematical constraint …..
@UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @dsmith @uh urgh, this is annoying, for some reason I didn't see those earlier posts!
@UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @dsmith @uh right, I don't think the mainstream view (if it really is so) denies the pattern, it just denies that it is anything over and above the complex causal relations that constitute it in any specific case. The pattern is just a type that can be realised by different token causal systems that have the relevant features necessary for being tokens of the type. That doesn't though make causal statements involving the type false.
@UlrikeHahn @dcm@bookwyrm.social @NicoleCRust @dsmith @uh so a mathematical 'constraint' is just a way of generalising over different causal systems organised in such a way that they instantiate certain causal structures well captured/approximated by the mathematical formalism at a certain level of abstraction and idealisation.
@dcm@social.sunet.se @dcm@bookwyrm.social @NicoleCRust @dsmith @uh Dimitri, I’m still not sure what you are taking the causal relations in the system itself. (buttons, neighbourhood network) to be exactly?
