thanks for the article
I'm (?not?) surprised to learn (from this article and followups to it)
about hyperscanning and the mature(ing) study of "*“multi-brain”
neuroscience*, which treats social interaction as a *co-regulated
dynamic system" * but it is fascinating to discover that this is as
quantitative as it has become and that folks are trying to effectively
study collective/symbiotic cognition/intelligence/consciousness.
On 10/23/25 7:15 am, glen wrote:
ERP-based interbrain causal model reveals closed-loop information
interaction in interpersonal negotiations
https://www.sciencedirect.com/science/article/pii/S1053811925005440
"This causal model provides a mechanistic explanation of how
proposer-responder pairs perceive and adapt to each other’s decisions,
facilitating shared attention and behavioral coordination in
reciprocal, asymmetric negotiations."
On 10/10/25 11:55 AM, glen wrote:
Well, as a fan of ChatGPT, I'm sure you've submitted this article to
it and have a ChatGPT-shaped extruded opinion of it. >8^D I may have
a similar one, having gotten analyses from Gemini and Claude. My
initial worry was later identified by Claude, but not Gemini: no
diversity analysis of the 384 undergrads ... not even male vs female
numbers? Maybe I missed it, though I did look at the 2 Supp docs. I
stopped short of researching estimates of diversity at Beijing
Normal. Pffft. Both Gemini and Claude said their methods were high
quality. Is it high quality to *not* report such things? No.
Anyway, the analogy of their bipartite game with small scale
organizations like teams and leagues is pretty good, I guess. Though
not at all perfect. I'd argue that something like Dunning-Kruger
would apply. We had a forward on our soccer team who seriously
thought he was better at *everything* than every other player on the
field. And, to be fair, he was better at everything *except*
fairness. When he was allowed to dominate, we won. When he wasn't
allowed (or wasn't playing), we did about average.
But we all hated that m0th3rfvck3r, and his @ssh0l3 dad. But we won
... a LOT. So ... for a team of middle schoolers who weren't
financially invested in the outcome of such games (yet we suffered
privately at home when we lost a game, including both psychological
and physical abuse) ... You tell me, what does "fair" mean?
On 10/10/25 8:21 AM, Steve Smith wrote:
Listening to Trevor Noah in an extended interview (1:26:00) with
Bernie Sanders "Who Owns America?" I was browsing my "to read"
queue(s) and tripped over the following paper of relevance to a
point Trevor was making about the inherent fairness in sports.
Trevor and Bernie exchanged examples of how Shaq's physical prowess
lead to new rules which handicapped his most acute capabilities and
how Golf (of all socially irresponsible sports) uses the (literally
named such) handicap system to allow individuals of widely different
levels to (potentially) play together.
I'm generally NOT a fan of either (organized-competitive) sports nor
politics as they are practiced because despite all the aspirations
and claims in both domains (what is Democracy if not an aspiration
to fairness?) the dominant theme seems to be "how can I game the
game?". Which suggests that game-theory is the meta-level at which
the dynamics can be studied (and adjusted?) to match our aspirations?
More objective (and smarter) people here might be able to suss out
more specific implications of this paper on the
Socio-Economic-Political domain than I am here...
*Coordination of network heterogeneity and individual
preferences promotes collective fairness*
https://www.cell.com/patterns/fulltext/S2666-3899(25)00141-2
Summary
There are intensive debates about whether heterogeneous networks
promote prosocial behaviors such as fairness and cooperation.
Theoretical models predict that network heterogeneity plays a
positive role, but this prediction has not been validated by
experiments. We reconcile this debate by conducting experiments with
two-stage ultimatum games on networks. In the first stage, we
identify responders with strong fairness preferences, referred to as
leaders. In the second stage, when leaders occupy high-degree nodes
in a heterogeneous network, their ability to motivate fairness among
neighboring proposers is amplified, and collective fairness is
facilitated. We propose an evolutionary game model and an
agent-based simulation framework that capture the microscopic
mechanisms underlying the networked experiments. Our experiments,
model, and simulations suggest that network reciprocity is
achievable but requires coordinated interactions between different
prosocial inclinations
of individuals and social network structures.
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