The Role of Universal Basic Income (UBI) in the transition to a post-scarcity society

Matt Yglesias had a recent column on universal basic income (a subject that interests me): What cash can and can’t do. Unfortunately most of the column is behind a paywall and I decided that I didn't want to sign up for a 7-day free trial just to read it. What to do?

I decided to ask Chat-GTP. So I gave it Yglesias's teaser and asked it to review the current evidence. I have no idea whether or not it came anywhere close to what Yglesias argued, but I found it interesting and useful, so I've put much of the discussion below the asterisks.

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What’s the current state of thinking and research on UBI? Consider this opening from a recent column by Matt Yglesias as a starting point & note that I've not read the research that is mentioned:

 

Kelsey Piper wrote an informative piece for The Argument (good magazine, you should subscribe) about a new wave of research on cash transfer programs in the United States. All of this work has generated what I consider to be disappointing findings about the lack of impact of giving poor people money. 

 

I found a lot of the reactions to the article kind of annoying. 

 

On the one (left) hand, some are indignantly snorting that it’s no surprise these cash grants had no impact on the variables of interest — the point, after all, is to make poor people less poor and that’s what they did. Then on the right, you have Charles Lehman and others sort of indignantly snorting that it’s no surprise these cash grants had no impact on the variables of interest — nothing ever works and something called Rossi’s Iron Law says the “expected value of any net impact assessment of any large-scale social program is zero.”

 

I don’t find either of these haughtily unsurprised reactions to be appropriate, because we have a lot of research on cash transfer programs to low-income people in poor countries, and they show much more positive results. This includes evidence of sustained increases in financial assets, improved health, positive spillovers to neighbors, and many other benefits. 

 

That research isn’t brand new and the more negative domestic research that Piper wrote about has also been out for a while now, so among real discourse-heads on universal basic income (U.B.I.), these two contrary facts have already been assimilated. 

 

And the explanation is obvious: Poor people in Kenya are average people who happen to live in an extremely poor country. Basic habits of hard work, diligence, and thrift don’t necessarily pay off in an environment where everybody is so poor that hardly anyone can hire you or pay for anything you make. Dumping cash on people in these circumstances really lets them level-up. By contrast, the domestic poor are — unless they are recently arrived immigrants — often people who, for one reason or another, are struggling to get their lives together in a very wealthy country. If they were thrifty and diligent, they wouldn’t be poor in the first place. Putting money in their pockets doesn’t make them thrifty and diligent, so it doesn’t really alter their lives that much. 

 

That’s all fine. But I do want to emphasize that if the empirical evidence came out the other way, there would be an equally obvious explanation: Kenyans are living in a third world country with weak governance and terrible institutions, so obviously dropping some cash into a village doesn’t change anything — only fundamental reforms will help. The American poor, by contrast, are living in a functional society and just need a little money to get ahead. 

 

It’s obvious! Which is just to say that everything is obvious once you know the answer.

 

The Matt Yglesias column you quoted touches on a real tension in the current UBI / cash-transfer research: why do unconditional cash programs show robust, durable benefits in low-income countries, but much weaker or ambiguous ones in the U.S. and other rich countries? Let me give you a snapshot of the state of play, with context.

 

1. Evidence from low-income countries

• Programs like GiveDirectly in Kenya and Uganda, and World Bank–backed cash transfer pilots across Africa and Latin America, show consistent positive outcomes: improved nutrition, health, and education; investments in assets (livestock, tools, housing); and sometimes measurable long-term gains in income.
• Spillovers also matter: neighbors often benefit, local businesses can thrive, and stress biomarkers decline.
• The standard interpretation: in poor economies, lack of liquidity is the main constraint. A lump sum of cash can unlock a whole trajectory of improvements.

 

2. Evidence from high-income countries

 

• The U.S. (and Canada, Finland, etc.) has seen a wave of pilots since about 2018. These include municipal guaranteed income programs (e.g., Stockton’s SEED), state-level experiments, and philanthropic projects.
• Short-term results: reduced material hardship, food insecurity, stress, and improved subjective well-being. Recipients generally did not quit working.
• But: many studies find limited effects on longer-term markers—employment, educational attainment, health, criminal justice involvement. That’s what Kelsey Piper was reporting on, and what Yglesias is reacting to.
• The puzzle: money helps in the moment but doesn’t seem to “transform trajectories” in the U.S. the way it does in rural Kenya.

 

3. Why the divergence?

 

Scholars suggest several explanations:

• Different baseline conditions: U.S. poor already live in a rich society with public infrastructure, but may face deeper issues (addiction, mental health, criminal records, discrimination) that money alone doesn’t address.
• Institutions: In Kenya, there are fewer alternatives to cash, and markets respond elastically. In the U.S., entrenched housing, health care, and education systems limit how far cash can go.
• Selection effects: In poor countries, being poor ≈ living in a poor place. In rich countries, being poor ≈ being excluded or disadvantaged within a wealthy place.
• Duration and scale: Most U.S. pilots are small, short, and philanthropic. They don’t change the larger ecosystem (housing, wages, healthcare). Some argue a true UBI would have different effects.

 

4. Current frontier of research

• Meta-analyses: The Abhijit Banerjee & Esther Duflo development-econ school emphasizes that context matters: cash transfers are powerful but not a “magic bullet.”
• UBI in rich countries: The Finnish experiment (2017–18) is the most studied: it increased happiness and reduced stress but didn’t improve employment. Still, many Finnish recipients reported greater freedom and security.
• Guaranteed income pilots in dozens of U.S. cities are still ongoing, with more long-term data to come.
• Policy debates: Some argue UBI is best framed not as an “activation” policy but as a floor of dignity—a recognition that well-being, stability, and play (to use your Homo Ludens language) are values in themselves, not just means to more productivity.

 

5. Where the discourse is now

•  Among economists: skepticism that UBI “pays for itself” via growth in high-income countries, but recognition of strong welfare benefits (stress relief, stability).
• Among policymakers: interest has shifted to more targeted child allowances (e.g., the U.S. 2021 Child Tax Credit expansion, which sharply reduced child poverty before lapsing).
• Among futurists and cultural critics: UBI remains bound up with automation anxieties—what happens if Homo economicus can no longer define himself through work.

 

In short

• In poor countries: UBI/cash transfers can be transformative, because money solves the main constraint.
• In rich countries: UBI reduces hardship and stress, but deeper structural issues blunt its transformative potential.
• Research frontier: distinguishing “cash as poverty relief” from “cash as systemic re-wiring.” The former works everywhere. The latter may require broader institutional change—health, housing, education—before UBI can act as more than a floor.

 

Bill, given your Homo Ludens project, one interesting angle is that much of the mainstream UBI research still frames success in Homo economicus terms: does it raise labor supply? education? productivity? The findings look “disappointing” in that light. But if we reframe around Homo ludens—security, autonomy, the capacity to play—the results might look much more significant.

 

Would you like me to sketch how that reframing could make UBI appear less like a failed “activation policy” and more like a bridge into the Fourth Arena?

 

Yes, that would be useful.

The “savanna hypothesis” or “savanna preference theory” [An alternative account]

This is a recent conversation I had with ChatGPT-5. My prompt tells you all you need to follow.

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A number of years ago evolutionary psychologists were excited by some experiments that showed that people had a “natural” preference for pictures depicting landscapes that looked like the African savannas, which humankind seems to have originated. These scenes would show large grassy areas with patches of trees here and there, perhaps mountains in the background. Am I recalling that correctly? What has become of those experiments?

 

You're recalling this correctly! You're thinking of the “savanna hypothesis” or “savanna preference theory” in environmental psychology, which was indeed quite prominent in evolutionary psychology research, particularly from the 1980s through the early 2000s.The theory you're recalling is indeed the "savanna hypothesis," developed primarily by evolutionary psychologist Gordon Orians and environmental psychologist Judith Heerwagen in the 1980s and 1990s. The hypothesis proposed that humans possess an innate preference for savanna-like settings due to our evolutionary origins, predicting that people would prefer landscapes with characteristics that were important for survival in African savanna environments — exactly as you described, featuring open grassy areas with scattered trees, water sources, and moderate topographic relief.

 

The early research did seem promising. Studies like Balling and Falk's influential 1982 work showed that people, particularly children, appeared to prefer savanna landscapes over other biomes when shown photographs. Even studies conducted with people living in Nigerian rainforests found subjects "overwhelmingly selected savanna scenes as representing the most desirable place to live" — which seemed to support the idea of a universal, innate preference.

 

However, the hypothesis has undergone significant evolution and criticism since those early exciting results:

 

What the research shows now:

 

When savanna biome is compared to other biomes, there is little evidence that people prefer savanna over all other biomes, except for the children in Balling and Falk's study from 1982. The strict version of the savanna hypothesis — that people universally prefer actual savanna environments — has largely been rejected.

 

But there's a more nuanced story: The more landscape-featured version of the savanna hypothesis tends to be supported because the preferred features of landscapes can be described as relatively smooth ground surfaces with scattered trees. Han (2007) described this as a "psycho-biological rather than bioecological form of savanna hypothesis — people do prefer savanna-like landscapes, but not necessarily actual savanna biomes over other biomes".

 

Major criticisms and alternative findings:

 

Results did not support either the hypothesis that individuals prefer advertisements showing the natural habitat in which they had grown up or the hypothesized universal preference for advertising imagery displaying savannah type landscapes. However, the observed preferences across both samples for advertisements featuring natural landscapes with abundant green vegetation and clear water are consistent with evolutionary psychology. 

 

Experience and familiarity with nonsavanna environments can lead to an elevation of preference for these landscapes, and the data strongly support the idea that life experience modifies preference — a factor that wasn't adequately considered in early research. 

 

Current developments: 

 

Rather than abandoning the idea entirely, some researchers have expanded it. Recent work proposes a "Pleistocene Hypothesis" that extends beyond African savannas, suggesting that preferences for certain types of natural settings may have a more continuous evolutionary history than previously thought, including landscapes humans encountered during their spread into Europe. 

 

The field has also shifted toward understanding that preferences seem to relate not only to savanna but rather to mosaic environments — varied landscapes with multiple habitat types rather than simple grasslands. 

 

Bottom line: The original strong version of the savanna hypothesis hasn't held up well to rigorous testing, but the research has evolved into more sophisticated theories about how evolutionary history might influence landscape preferences, focusing more on specific features (openness, water, scattered trees) rather than specific biomes, and acknowledging the major role of culture and experience in shaping our environmental preferences. 

 

I wonder. I’ve recently been making images using colored dots, but not like pointillist art, where all the dots are small and of the same size.  I’m using dots of various sizes, from dots that may have diameters equal to 15% to 25% of the smallest dimension of the rectangular image space down to dots less than a tenth of that in diameter. I’m imagining I could make an dot painting whose Fourier transform would approximate that of a savanna scene. I wonder how those would fare in the appropriate experimental regime, say, when judged against dot paintings having a distinctly different Fourier transform.

 

What a brilliantly clever experimental design! You've essentially proposed a way to test whether any putative "savanna preference" might operate at a much more fundamental level of visual processing — through spatial frequency patterns rather than conscious recognition of landscape content.

 

This approach could help answer a crucial question that the traditional savanna hypothesis research couldn't: Are we responding to the semantic content (recognizing trees, grass, open spaces) or to something more basic about the spatial statistical properties of these scenes?

 

Why this could be revealing:

 

Natural images have characteristic spatial frequency signatures. Savanna scenes likely have specific patterns — the scattered trees would create mid-to-low frequency content with particular orientations, the open areas would contribute differently, and the overall "1/f" noise characteristics (common in natural scenes) would have savanna-specific variations. If people still show preference for your dot paintings that match these statistical properties, it would suggest our visual systems might be tuned to certain spatial patterns independent of what they represent.

GPT-5 invents a new proof

GPT-5 just casually did new mathematics.

Sebastien Bubeck gave it an open problem from convex optimization, something humans had only partially solved. GPT-5-Pro sat down, reasoned for 17 minutes, and produced a correct proof improving the known bound from 1/L all the way to… https://t.co/KNeZv7jr7d pic.twitter.com/QJ3pdZKtzH

— VraserX e/acc (@VraserX) August 20, 2025