The article answers this: For one, the 'papers' are entirely AI-generated.

It's unlikely there's a trivial arbitrage to eliminate homelessness in a city full of engineers.

Thus, cutting all budget for cash transfers could increase homelessness by 80%! (Are those 14,498 now-homeless people eligible for this year's $100k? They can't be, since you already spent it on the 7,973 currently-homeless people).

Model the situation as "X(t) people become homeless at year t, Y(t) people become housed at time t," and you'll see the most important metric is "how much can we decrease X and increase Y per dollar spent"?

"Number of dollars per current homeless" is not really meaningful at all.

"Replace services with direct transfers" would eliminate homelessness for 1 year. Then new people would become homeless, and you'd have no services, because your budget is already committed to the "year 1 homeless-cohort."

The money spent grows quadratically (not linearly) over time.

Is this an artifact of dynamic resizing at small resolutions?

Only Georgists believe that. And it's the rent of the "bare land", not including the housing constructed on it.

It doesn't affect life expectancy much, because most deaths are among the elderly (70% over 80 IIRC).

> A significant degradation of external thermal comfort can also be seen in the simulations, as heat released by AC systems warms the outside air (see figure 3). The temperature increases due to AC depend on the time of day and on the characteristics of the heat wave, mainly its intensity. On average, the duration spent under high heat stress conditions in the streets is increased by about 20 min per day because of AC.

https://iopscience.iop.org/article/10.1088/1748-9326/ab6a24#...

But that wasn't the case for non-algorithmic screening. From the paper:

"By contrast, we find that when first round screening is not mediated by a single screening procedure, systemic rejections are close to the baseline. To support the empirical validity of our baseline, we study homogeneous outcomes in the largest study of first-round screening at U.S. employers to date. Kline et al. [38] generated 83000 synthetic resumes and submitted these resumes to vacant positions at 108 US companies between October 2019 and April 2021, a similar time period to our data. The companies, which are a subset of the Fortune 500,15 collectively employ 15 million workers. We analyze the homogeneity observed in the resulting callback outcomes in their data. We find that the baseline is an effective estimator of the systemic rejection rate for this dataset. As shown in Figure 3, the observed systemic rejection rate is accurately predicted by the baseline and a chi-squared goodness-of-fit test cannot reject equality of the two distributions (2 = 20.05, = 0.69). In other words, while the largest previous study observes systemic rejection rates consistent with employers making statistically independent decisions, the algorithmic hiring data shows significantly correlated outcomes that lead to higher-than-baseline systemic rejection rates."

They find "disparate impact" of pymetrics across racial groups, but it doesn't seem like they controlled for anything.

"Rent seeking" is more about making money by changing the rules instead of providing a service.