@dcm@social.sunet.se @dcm@bookwyrm.social @NicoleCRust @dsmith @uh

Maybe it would help to articulate ‘the mainstream picture’, causal or otherwise, on the buttons example as you see it? What is the story with respect to “a connected network emerged”, and it “emerged when there were x many links”?

@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.

@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.

@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 …..