This piece assumes much of the reader, but gives much more in return.
Sadly I don’t imagine the AI overlords will even understand this eulogy.
doomslide.substack.com/p/aga…
Joshua B. Plotkin retweeted
We are searching for a new colleague in our Department of Biology at University of Oregon!
This is a super special opportunity-- an endowed chair position in Genetics/Genomics. Come join us!
I'm happy to answer any questions folks might have
careers.pageuppeople.com/726…
Joshua B. Plotkin retweeted
Applications are open for the NITMB Fellows program!
NITMB is seeking early-career researchers who have an interest in transforming biological research and inspiring new mathematical discoveries
Apply by November 15, 2026: nitmb.org/nitmb-fellows-prog…
Opportunity: Penn MindCORE Fellowship!
MindCORE seeks to recruit outstanding postdoctoral researchers for our Research Fellowship for Postdoctoral Scholars. Housed within the School of Arts & Sciences of the University of Pennsylvania, MindCORE is an interdisciplinary effort to understand human intelligence and behavior.
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Replying to @Deniz_Genlik
“If they can collapse Math Departments, the higher education system will collapse too…”
U funny.
Thank goodness.
(Clarification would be required in my department, for real. Also, my standard reaction to claims that algebraic geometers say are obvious..)
Replying to @cremieuxrecueil
Grandma dogs too!
nature.com/articles/s41562-0…
I can only infer that’s a joke. Because the idea of a committee on the role of mathematics writ large, and the impact of AI, but lacking applied expertise …
Replying to @AndrewCurran_
Is this ok though?
Replying to @pmddomingos
Yes.
Replying to @ChrSzegedy
Replying to @OpenAI
Replying to @willdepue
“Literally denominated in same currency as the unit” lol
An evolutionary origin of collective decision making in humans and machines. New paper, with Guocheng Wang and Qi Su:
arxiv.org/abs/2610.05676
Human institutions and machine-learning algorithms both decide by layered, non-linear voting. Engineers train networks with back-propagation. Institutions have no engineer.
We find that rewarding individuals only when their vote is pivotal, at every layer, lets collective intelligence evolve by peer imitation alone. That reward scheme plays the role of back-propagation in ML.
This result gives a naturalistic, evolutionary account of decision making, by human institutions and machines alike.
...and if Wolfram does read it: iterating his tournament would make a nice project for one of his summer programs.
Likewise, Steve. And talking into the void is a respectable strategy here: TFT also cooperates first and hopes for the best.
Replying to @stevenstrogatz
My pleasure. Excited to see your interest and Wolfram's too.
Nothing is more fun than starting to explore these things from scratch. So I almost hesitated to fill in the history, except that it really does vindicate the robustness of Axelrod; and allow us to pose new questions!








