[FRIAM] collective fairness

[Steve Smith]

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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