Meet "The Palantir Guy"
Asked 148 UVA female students: which 30-year-old makes most appealing husband?
C) Shy, supersmart physics major making $400K at Palantir: 58%
A) Socially adept ex-frat brother making $200K at BofA: 29%
B) Sensitive, smart SAHD making $35K: 6%
Emotional intelligence is nice. Brains & good paycheck nicer for my UVA students.
🚨UPPER EAST SIDE: PaIestine Action, a UK based direct radical action network vandalized and violently destroyed the windows of Capital One Bank in 86th Street.
Look at Europe yesterday to see what will be in NYC today.
#7ottobre, tre anni dopo. Stamattina, nel quartiere ebraico di Roma, lo #Shofar ha suonato per le vittime del #pogrom di #Hamas: il richiamo che da millenni raduna il popolo di #Israele.
Mai dimenticare.
Mai perdonare.
I colpevoli dovranno pagare per tutto.
The @NobelPrize in Chemistry to Henri Kagan and Kenso Soai for solving a “mystery” that had puzzled scientists: How can some molecules, e.g. amino acids, exist as 2 variants that are mirror images while proteins in living organisms are made from only one of those mirror images?
Dear Nassim @nntaleb,
I’ve seen you refer a few times to @stephen_wolfram's recent finite-state-machine experiments with the Prisoner’s Dilemma, and in particular to his finding that some of the more complicated strategies vastly outperform Tit for Tat. Since I think you and I may still be speaking past each other about what bothers me here, I wanted to spell out my position carefully.
I wrote out this long note with the help of ChatGPT. The ideas and arguments are mine; the language is ChatGPT’s, with my approval.
1/ First, I’m not trying to defend the Prisoner’s Dilemma, game theory, or simple models as adequate descriptions of the real world. Maybe they are adequate, maybe they aren’t. That’s a separate question.
What I’m interested in here is much narrower. I want to compare simulations with simulations — Axelrod’s, Nowak and Sigmund’s, and Wolfram’s — and ask what their results actually show.
2/ Stephen does something interesting and very much in his characteristic style. Instead of hand-picking a few strategies and having them compete, he enumerates all finite-state strategies of a given size and has every strategy play every other strategy in an iterated Prisoner’s Dilemma.
He then ranks the strategies by how well they did against this initial, uniform field of opponents. Some complicated strategies substantially outperform Tit for Tat.
Fine. I believe the computation. But then, essentially, he stops.
3/ This is the key point for me. There is no next generation in which the successful strategies become more abundant and the unsuccessful ones become rarer. There is no repeated selection changing the population of opponents.
Stephen has performed what amounts to one round of an evolutionary tournament: everybody plays everybody, their scores are calculated, and we see who did best against the original population. But the population itself is not then allowed to evolve.
4/ That matters enormously because the original population contains every strategy in equal abundance, including lots of terrible ones: unconditional cooperators, easily exploited strategies, and other “suckers.”
A strategy that is particularly good at exploiting those weak strategies can score spectacularly well in that first tournament.
5/ But suppose we now use those scores to form the next generation. The suckers become rarer or disappear. The successful strategies become more common. Everybody now faces a different distribution of opponents.
So you run the tournament again. And again. And again.
What succeeds in round 1 need not succeed in round 10, or round 1,000, because the strategies themselves create the environment in which subsequent selection occurs.
6/ This isn’t merely a hypothetical objection. Decades ago, Martin Nowak and Karl Sigmund took a closely related methodological approach. They considered an entire parameterized family of strategies rather than a few hand-picked favorites, started with a broad distribution across strategy space, and then did the crucial thing that Stephen does not do here: they let the population evolve.
7/ What happens is fascinating. Early on, nasty strategies can flourish by exploiting weak ones. But as the weak strategies disappear, the ecology changes, and reciprocal strategies can prosper.
With errors present, more forgiving strategies can emerge. If the population becomes too forgiving, exploiters can invade again. The result can be extraordinarily long evolutionary transients and cycles.
8/ So there may be no meaningful sense in which one strategy simply “wins.” Success is frequency-dependent: it depends on which other strategies are currently abundant, and those abundances themselves change through selection.
@AdrianoAguzzi My PhD advisor, Ed Spiegel, used to say that all the shortlisted candidates for a faculty job were often equally "good." To choose, we should drop references to all kinds jokes, starting with Marx Bros, and hire the one who gets them. That's who you want as a longtime colleauge.
The best analogy I can think of here is a chess game where one player is legit good and the other knows nothing more than the rules for how each piece can move.
All of Carlson’s disingenuous techniques are on display here. Half-truths, quotes of context, taking what the other person says and twisting it. Mehdi has a similar playbook.
You have to be exceptionally good even to hold your own, and Rees Mogg owns him.
New Jersey’s Salem nuclear plant is preparing to produce a critical isotope used to sterilize billions of medical devices. The project could supply at least half of U.S. demand while reducing the nation’s dependence on a small network of foreign producers.
Some NPR podcasts started getting mysterious comments on Spotify. They made no sense to the staff reading them – until someone from a younger generation cracked the code.
Hear the story: https://t.co/0gLhP4L8rL
@ycinnewyork@neetu_arnold I've had the pleasure of teaching and working with hundreds, probably thousands, of students from Stuyvesant, Bronx Science and Brooklyn Tech.
It ain't broke, so don't "fix" it!