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?
> 
> 

-- 
↙↙↙ uǝlƃ

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