By any chance does anyone here know where I could find the text data from obituaries? I thought might have some luck asking the HN crowd! It's for a research paper I'm working on.
I thought this was kind of a lame article. The point of behavioral economics is to systematically understand the heuristics people are using. That is hard to do!
Trying to describe all the heuristics together is difficult and untestable -- i.e., not that good for the experimental research which most behavioral economists practice. Still it is well-known in the field that this is an open question worth theorizing about and I think many people do[1][2], although there is not a consensus on the "best" theory as far as I know.
The author like then lauds some impressive/hard to conduct/large-scale interventions which are formidable but don't really teach us about economic theory, and in fact neither were published in economics journals. Maybe the field should move in that direction, I am agnostic on the point, but the author's argument wasn't coherent in my opinion.
I don't think most people "lie about it", at least not intentionally.
I think it's more likely they get really caught up in the moment or something among those lines. I am interested in what in what it would be like to go to a concert of a bar with one of these people.
I'm not the OP, but this from the Matt Levine newsletter last week, which I think is what he is getting at:
> 1. If you hold your coins at a regulated exchange, sure yeah the authorities can probably get a warrant and seize your Bitcoins.
> 2. If you hold your coins in your own wallet but you write down your private key in, like, your phone’s notes app, and it backs up to the cloud, then the authorities can probably get your cloud provider to give them access to your notes and read the key and use that to seize all your Bitcoins. (This happened to the alleged Bitfinex hack launderers.)
> 3. If you write your private key on a scrap of paper, the authorities can search your house until they find it and seize all your Bitcoins.
> 4. If you memorize your private key, or just write it down somewhere really safe, then they can arrest you and throw you in jail until you tell them what it is, and then seize all your Bitcoins.
> In a sense we do have this: engineering and finance. Engineering turns good hard science into new tools, machines and weapons, and Finance turns good (predictive) soft science into new ways to make money.
I think this is a common critique, but I also think it is missing the point. What if the question of interest isn't so easily verifiable like in Engineering? Do we just throw up our hands and give up on those questions? [The alternative to good social science is not no social science, it’s bad social science](https://statmodeling.stat.columbia.edu/2021/03/12/the-social...).
Finance is also a bit tautological in this regard. It seems that often prediction models are impossible to disprove (e.g., our arbitrage method doesn't work anymore, the market updated). Yes good for putting skin in the game, but doesn't seem like it does much to advance our long-term understanding of humans.
Do you have any advice for R users on making the transition? Even though I am primarily an R user, these days I have been using a fair amount of JS in my work, and I'm not sure how I can get to the "next level" in my programming that your alluding to. It seems like most of the tutorials are either too rudimentary or way over my head (like the three step drawing meme).
Maybe it makes sense for someone in my position to just go bite the bullet and go through the Eloquent JavaScript even though I feel like I already have a fair grasp of the content? Or learn some key principles of Software Engineering?
I recommend the book Scarcity for anyone interested in learning more about the psychology behind this. It was written Mullainathan and Shafir, both stars in their respective fields.
> "Ivies really wanted to promote social justice, they would let in more poor kids"
> This is not their main priority.
I think you're right to say that this is not their main priority, but I think they actually do quite good letting in more more poor kids (at least better than most people expect).
Does anyone here know of canceling Wired is also this difficult? I was considering starting a subscription after seeing a compelling offer from them, but if it will be this hard to cancel this puts me in the fence.
> The data science field has been flooded with PhDs with nowhere else to go that have no background in engineering, and sadly often have a very poor understanding of both machine learning and statistics.
I am a PhD student in a non-engineering field. I've been taking as many math and stats courses as I can, but what other courses should I be trying to take if I want to excel as a data scientist? Software engineering CS type courses?