I grew up in a city with insanely high taxes on cars and roads (Singapore). But you could get anywhere easily with the bus or MRT. In a rush? Your Grab taxi can get you there quickly and efficiently. I’m not sure why it’d be better to make everyone’s day worse instead. Does that really make the world a fairer place?
Yea you’re exactly right, there’s a tragedy of the commons situation right now. You could either decrease the demand or increase the supply to fix this problem, and it seems pretty impossible to increase the supply (build a bridge across the Hudson? That’s crazy). So here we are.
I’m a fan of charging market efficient rates for shared goods. The congestion situation in the Holland Tunnel is awful and bleeds out into various streets of Manhattan as well. The cost of sitting in crawling traffic with aggressive drivers cutting around is probably much more than an extra $20.
I make $400k/yr as an immigrant, and boy do I feel exploited :)
Edit: Sorry for being sarcastic and flexing here. I’m friends with many IMO and IOI medalists (per the paper) who are studying/working in the US and I think it’s an amazing opportunity for them. Where I grew up in Singapore, tech salaries are way lower, and as a new grad I think I easily 3xed what I could’ve made back there. I’m not representative of all immigrants but I believe I can represent a class of highly skilled immigrants who appreciate the opportunity. It’s extremely insulting to call people like me exploited, because the next line is - I’m bringing down your wages.
I think there can be prior that a professional looking ad can generate more clicks. Your argument shows a lack of statistical understanding - conditional on this data, the Bayesian approach would be to update the prior (whether A is better, or they’re equally as good) with the data collected. With such a small dataset, you might end up with a belief that there’s a 60% probability that B is better than A, but that’s not significant enough to conclude that B is in fact better than A, as you still have a lot of uncertainty.
With a prior that A is superior, you may still end up believing that A > B after updating, because there’s just so little data.