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Mar 31, 2022 15 tweets 11 min read Read on X
1/n 🧵 ICYMI "Scaling of Hunter-Gatherer Camp Size and Human Sociality"

New paper (@UChicagoPress) led by José Lobo (@ASU) & co-authored by a team incl. SFI's @BettencourtLuis (@miurbanchicago) & Scott Ortman (@CUBoulder), result of an SFI working group:
journals.uchicago.edu/doi/10.1086/71… Image
2/n
This new research deploys #ScalingLaw insights to understand the population-density driven transition from temporary hunter-gatherer settlements to permanent sedentary city life.

Image
3/n
Previous research on the impact of densification on the transition into city life has yielded conflicting results, some suggesting that hunter-gatherer groups actually thin out as they scale. (In what ways might this reflect transitions in the scaling of microbial organisms?) Image
4/n
Why do hunter-gatherer groups decline in population density before the transition to sedentism? This research suggests the benefits of city life include ways of relating inaccessible to nomadic foragers, which both enable and reward greater density. Image
5/n
As explored by SFI Prof Deborah Gordon's work on ant colonies as distributed computation, the structure of human communities reflects their adaptation to stable/unstable environmental variables — how organisms manage #risk & #uncertainty, agility vs. specialization tradeoffs. Image
6/n
One factor for decline in density as hunter-gatherers band together in greater numbers appears to do with with the modular association of close family groups with other families, with buffer zones between them... Image
7/n
This may have to do with how relatedness dilutes and food sharing/conflict resolution becomes more burdensome as hunter-gatherer bands scale — without more advanced trust/sharing/resource management technologies, dense but loosely-related groups become increasingly unwieldy. ImageImage
8/n
Building a model for understanding the average relationships among properties across hunter-gatherer camps, the team identifies temporality, fission-fusion dynamics, and a correlation between relatedness and proximity. Energy capture as a function of density also figures in. ImageImageImage
9/n
How does the density of hunter-gatherer camps affect the resource exploitation efficiency? Up to a point, info sharing increases per capita energy capture...but then competition yields diminishing returns. (These dynamics have long been offered as a fission-fusion driver.) ImageImage
10/n
There is a different relationship between population density & social production; this model reveals a "sweet spot" that opens up for permanent settlements in nutrient-dense environments, where the conflicting demands of energy capture & cultural richness come into balance. ImageImageImage
11/n
There are, of course, tradeoffs to cohabitation at any scale: the benefits of association vs. the costs (stress, crime, disease) of living in close proximity.

(As historian Helen Cam put it, "Civilization is the art of living together w/ people not entirely like oneself.") Image
12/n
This paper formalizes the relationship between the innovation of regulatory & transport infrastructure & the costs of living together in close proximity — shedding light on the relationship between coastal settlements, agriculture, money, privacy, police, & population size. ImageImage
13/n
In summary, key takeaways from this paper — with a gesture toward a question implied by but unasked in this paper:

In what ways can this worked be applied to major organizational transitions in other systems, such as the evolution of complex cells & social organisms? ImageImage
14/n
Note on the data used in this study:

"To test these expectations, we use a database representing 1,760 hunter-gatherer camps from 112 different cultural groups and a variety of regional and ecological settings (Whitelaw 1989, 1991, 1994)." ImageImageImageImage
15/n
There's much more to explore in this paper. Read the whole thing in Current #Anthropology (@UChicagoPress) — and for more on settlement #scaling, listen to our #ComplexityPodcast episodes with @BettencourtLuis (ep. 4), Scott Ortman (ep. 47), and Geoffrey West (eps. 35, 36). ImageImage

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More from @sfiscience

Mar 10, 2023
🧵 "The Smell of Inhibition. A Code in the Nose?"

ICYMI, this week's SFI Seminar by Fractal Faculty Stuart Firestein (@Columbia) on "what started out ass a very simple-seeming problem [re: #olfaction] and turned out to be very complicated":

"Everything we know about the world comes through these little holes in our head and the skin covering our body, processed through tissue specialized to interpret it."

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🧵 "#Possibility Architectures: Exploring Human #Communication with Generative #AI"

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@LaboratoryMinds re: work led by @clairebergey:
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"I think what really drives [the popularity of the #multiverse in #scifi] is regret... There's a line in @allatoncemovie where #MichelleYeoh is told she's the worst version of herself."

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- SFI Fractal Faculty @seanmcarroll at @guardian
theguardian.com/science/audio/…
"At the level of the equations there is zero ambiguity, but the metaphors break down. The two universes it splits into aren't as big as the original universe. The thickness of the two new universes adds up to the thickness of the original universe."
Read 4 tweets
Dec 14, 2022
"Compositionality in Vector Space Models of Meaning"

Today's SFI Seminar by @marthaflinders, streaming:


Follow this 🧵 for highlights!
"Scientists gather here
Santa Fe Institute, oh so near
Inquiring minds seek truth"

#haiku about SFI c/o @marthaflinders & #ChatGPT

...but still, #AI fails at simple tasks:
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Dec 13, 2022
"Humans are prone to giving machines ambiguous or mistaken instructions, and we want them to do what we mean, not what we say. To solve this problem we must find ways to align AI with human preferences, goals & values."
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quantamagazine.org/what-does-it-m…
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- Stuart Russell (@UCBerkeley) as quoted by @MelMitchell1 in her latest @QuantaMagazine article
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Read 6 tweets
Dec 12, 2022
"Training Machines to Learn the Way Humans Do: an Alternative to #Backpropagation"

Today's SFI Seminar by Sanjukta Krishnagopal
(@UCBerkeley & @UCLA)

Starting now — follow this 🧵 for highlights:
Image
"When we learn something new, we look for relationships with things we know already."

"I don't just forget Calculus because I learned something else."

"We automatically know what a 'cat-dog' would look like, if it were to exist."

"We learn by training on very few examples." Image
1, 2) "[#MachineLearning] is fundamentally different from the way humans learn things."

3) Re: #FeedForward #NeuralNetworks

"You choose some loss function...maybe I'm learning the wrong weights. So I define some goal and then I want to learn these weights, these thetas." ImageImageImage
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