[FRIAM] dynamic networks (was: UBI)

[uǝlƃ ↙↙↙]

So at our (newly formed, but hopefully weekly) pub salon, a praying mantis enthusiast [⛧] made the claim that they don't learn. Knowing nothing about the beast, I just assumed he knew what he was talking about and assumed the praying mantis has no significant ontogenic development. But I still made the argument that a non-developmental reinforcement learning most likely takes place, if not within 1 of them, but at least between N of them. Looking it up later, I found this:

Aversive Learning in the Praying Mantis(Tenodera aridifolia), a Sit and Wait Predator
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882761/pdf/10905_2018_Article_9665.pdf

And, more to the point of this thread:

The fishing mantid: predation on fish as a new adaptive strategy for praying mantids
https://jor.pensoft.net/articles.php?id=28067

Trying to parse how Levine's (or the other 2 "level reifiers") work might for reciprocity, I hit a brick wall w.r.t. *open* or deeply embedded participatory reciprocity. The concept of the trophic generalist is *beguiling*, I think dangerously so. I had trouble glossing over the averaging/weighting conveniences. But the registration of trophic generalist or specialist is something I can't get past. Both averaging/weighting and (resource vs. trophic) generalist/specialist seem to encourage us to think the network is definite. In order for us to trust the "levels" as derived measures, we'd need high frequency access to the species, their diets, hunting territories, body mass/size, etc. We'd have to RF tag an entire ecosystem. I've only been to a few ESA meetings. And nothing like that's ever crossed my eyeballs. But my ignorance knows no bounds.

Now, presumably animals don't change that much. So, whatever approximating derived measure we come up with for, say, your standard lake in Texas, would stay that way for awhile and be relatively trustworthy. But when trying to apply it to open reciprocity in, say, social media, parasocial relationships, work environments, PTA meetings, etc., it's difficult for me to believe the derivations would survive even the slightest perturbation, much less something like a pandemic.

If the little Turing Machines we call "praying mantids" can discover a taste for fish, surely even Brett Weinstein and Jordan Peterson can learn to feel "gratitude and selflessness" while reading Deleuze! Right?


[⛧] He's also a Fabian and a huge fan of HG Wells. When I mentioned my ongoing problem with whether or not we can separate the art from the artist (e.g. anarcho-syndicalism vs. Chomsky), I suggested HG Wells may be problematic to some. He was shocked and asked why. But I backed off because of the current Israeli-Palestinian escalation. We'd already almost come to blows about the free will of the praying mantis. 8^D

On 5/12/21 7:41 AM, uǝlƃ ↙↙↙ wrote:
> That's an excellent question. I've only had the chance to glance at those 3 cites. To decide how they could help propagate signals would take more investment. It would be helpful if you could give a short blurb about why each one came to mind as appropriate for reciprocity. I remember you mentioned this or another Levine paper in the context of EricS' Beyond Fitness paper. So, I'm wondering if you mention that one by Levine simply because you're steeped in it?
> 
> Regardless, I'll try to do a closer skim of each over the next week or so.
> 
> On 5/11/21 2:21 PM, jon zingale wrote:
>> I have failed to follow this discussion very closely. That said, to what extent could frameworks like those that underlie spring rank <https://github.com/cdebacco/SpringRank> or gauge-theoretic price as curvature <https://arxiv.org/pdf/0908.3043.pdf> give reasonable characterizations of reciprocity over circuits? To what extent does Levine's <https://www.sciencedirect.com/science/article/abs/pii/002251938090288X> (painfully straightforward) solving for eigenstates?
> 
> 

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