Amazon, Wal-Mart, MacDonalds, giant banks, insurers, et c., analyze their competition, suppliers, and consumers in great detail with computer data and models. Where’s the widespread automated buyer’s agent for consumers, to balance that power so strongly that it shoves profit margins the other direction, so it’s not just megacorps pushing ever closer to some absolute limit of the last tenth of a cent they can wring out of us? Fucking nowhere, they’d never allow that kind of access to the data we’d need, the best we get is “CamelCamelCamel”, not some Siri-alike we can casually tell to go find the best price/quality intersection on the market for [product] and have that show up a couple days later.
And we’ll never get it. Just an ever-tightening noose of perfect price discrimination and risk calculation that goes only one way. Short of legislating radical levels of availability (and reliability and consistency—standards and minimums) for price and product information, anyway, and I’m pretty sure that’s on nobody’s radar.
What’s lacking is the collective willpower to develop social organizations, which could orchestrate and fund such efforts. There’s a complete lack of organization among the public.
The problem is neither technical nor legislative: people just don’t care to, and are happier accepting the status quo.
It’s even argued that the path of technological progress would not change even if you there were aliens, AI bots, any entity with the instinct of self-preservation, intelligence and replication. The mechanisms of the macro-system comprised of human biology + technology are explained in his book Anti-Tech Revolution in great detail.
It was a hard and bitter conclusion to come as a software dev, but I can never again unsee the fact that all technology is inherently evil. For a shorter but enlightening read, see his original manifesto “Industrial Society And Its Future”: https://www.washingtonpost.com/wp-srv/national/longterm/unab...
Actuaries have to be scientifically accurate with respect to risk. If they underestimate the risk, the premiums don't cover the losses. If they overestimate them, they lose in the market.
So I asked her if a red sports car should REALLY be that much more expensive to insure than a grey family car.
She told me, yes, the pricing actually correlates very well with the risk.
The only time when pricing is NOT correlated with the risk is AFTER an accident. Statistically after an accident, drivers become safer, but the insurance companies will not lower the rates charged to reflect it.
The other schema is one where you're modeling manipulation effectiveness, to probe and predict how you can best (most effectively with the least resources) achieve some change in the trajectory. Engineering a person. It seems like we really only think of interventions as trying to return someone to some baseline condition, rather than to try to improve people beyond the baseline, to invest in them.
The paper has a bit at the end about using it for personalized interventions but it feels performative, like lip service. I know one of the authors professionally in a distant sense so I think they're probably genuine about it, but I can't help but wonder how the paper might be different if intervention was actually their focus. Wouldn't they be modeling changes in trajectories, the deviations or something?
Also, I would wager that upon release, suddenly everybody says they eat healthy and exercise daily.
I've tried to explain to him that people aren't actually very unique in large models, and there's probably several signals Google does have from his other searches to conclude he's interested in those two cities.