john_horton

This might be of interest: https://www.expectedparrot.com/content/johnjhorton/grant-let... It uses a collection of models to extract the text from a handwritten letter by US Grant. Probably overkill for something nicely printed.

Hey thanks for noticing - here's the MIT licensed library it's based on: https://github.com/expectedparrot/edsl

thanks! B/c it got some positive reaction here, I did a little thread on how you can turn this flow into an API: https://x.com/johnjhorton/status/1823672992624242895

Very cool - I've been working on an open source python package that lets you do some similar things (https://github.com/expectedparrot/edsl).

Here's an example of the Enron email demo using the edsl syntax/package & a few different LLMs: https://www.expectedparrot.com/content/6607caa1-efc5-439f-85...

Link to the package: https://github.com/expectedparrot/edsl

Got it - so it is the "performativity critique" - the idea that the LLM "knows" economic theories and responds in accordance with those theories. I don't think that's very likely because a) econ writing is presumably a tiny, tiny fraction of the corpus and (b) it would imply an amazing degree of transfer learning e.g., it would know to apply "status quo bias" (because it ready the papers) to new scenarios. But as the paper makes clear, you can't use it to "confirm" theories but rather use it like economists use other models - to explore behavior and generate testable predictions cheaply that you can go test with actual humans in realistic scenarios. The last experiment in the paper is from an experiment in a working paper of mine. There's no way the LLM knows this result, but if I had reverse the temporal order (create the scenario w/ the LLM, then run the experiment), it could have guided what to look at. That's likely what's scientifically useful. Anyway, thanks for engaging.

I'm sorry I don't follow - is your claim, that, say, an AI agent exhibiting status quo bias in responding to decision scenarios (e.g., a preference for options posed as the status quo relative to a neutral framing - Figure 3) that the reason this happens, empirically, is because the LLM has been trained on text describing status quo bias? E.g., like if an apple fell to the ground in an game, it was because the physics engine had been programmed w/ laws of gravity?

Are you referring to this: "What kinds of experiments are likely to work well? Given current capabilities, games with complex instructions are not presently likely to work well, but with more advanced LLMs on the horizon, this is likely to change. I should also note that research questions like what is “the effect of x on y” are likely to work much better than questions like “what is the level of x?.” Consider that in my Kahneman et al. (1986) example, I can create AI “socialists” who are not too keen on the price system generally. If I polled them about who they want for president, there is no reason to think it would generalize to the population at large. But if my research question was “what is the effect of the size of the price increase on moral judgments” I might get be able to make progress. That being said, it might be possible to create agents with the correct “weights” to get not just qualitative results but also quantitatively accurate results. I did not try, but one could imagine choosing population shares for the Charness and Rabin (2002) “types” to match moments with reality, then using that population for other scenarios." --- To clarify, this about what research questions are likely to work well here, not what questions posed to LLMs will work well.

It is so implausible that the training process that creates LLMs might learn features of human behavior that could then be uncovered via experimentation? I showed, empirically, that one can replicate several findings in behavioral economics with AI agents. Perhaps the model "knows" how to behave from these papers, but I think the more plausible interpretation is that it learned about human preferences (against price gouging, status quo bias, & so on) from its training. As such, it seems quite likely that there are other latent behaviors captured by LLMs and yet to be discovered.

that's a great suggestion - thanks!

yeah - so I think this is worth exploring. Given how many tokens you can jam in the prompt even w/ GPT3, I think could do some pretty complex game play, at least compared to what is typical in the lab e.g., I think could easily have it remember how 100 or so other agents behaved in some kind of public goods game.

oh hey - it's my paper! If anyone is interested in exploring these ideas, feel free to get in touch (@johnjhorton, https://www.john-joseph-horton.com/). FWIW - I think it would be really neat to build a Python library w/ some tools for constructing & running experiments of different kinds. I think the paper only scratches surface of what's possible (esp. once GPT4-ish has an API).

I'm one of the authors of the original paper the article is based on. Here's a link to the actual research: https://www.john-joseph-horton.com/papers/schumpeter.pdf

Yeah - I think this is just inherent in the transmission of academic work to a broader audience. Nuance and caveats get stripped away, in part because the journalists themselves aren't capable of assessing these features.

It wasn't dishonest - they didn't have the data for those multi-site employers b/c of how Washington state keeps records. The authors are upfront about this and try to address the limitation by adding a a survey component---they talked to these employers to see if their responses to the MW were different.

Full disclosure: I worked at oDesk for 2 years as their staff economist and still consult with the company (I'm now a professor at NYU Stern).

A few points on issues raise in this thread:

(1) I can assure you---and I really should do some blog posts on this---but client/employers are not nearly as price sensitive as people believe. When you try to model employers choosing who to hire from their pool of applicants, you need to work really hard in specifying the model to get demand curves to slope downward. In other words, "price" often gets the wrong sign, meaning it looks like the higher the bid, the more likely an applicant is to get hired. What's going on is that clients can and will pay more for better, more experienced developers and these developers bid accordingly. However as a freelancer, finding those kinds of employers can be a challenge.

To help deal with this search problem, we asked employers up front to state their relative preferences over price and quality. For example, employers could state they were looking for high quality at a high price, or less experienced workers at a lower price. During the experimentation phase, we randomized whether these employer/client preferences were revealed to applying freelancers. We found that we could induce substantial sorting by freelancers to job/employers of the "right" type, raising wages and project sizes at the high end. This feature is now universal and helps freelancers get in front of clients that are a good fit for them.

(2) After very extensive experimentation, oDesk did in fact impose a minimum wage. It was set at price point that improved the quality of people getting hired, but was not so high that jobs weren't being filled. Obviously, this level of minimum wage doesn't touch the high-end of the market, but picking minimum wages is a real balancing act and setting it too high can definitely price some work out of the market. It also "pulls up the ladder" and prevents new workers from getting started in the market, which oDesk understandably wants to avoid.

(3) There is a problem with too many low quality applications. The problem is similar to what's going on in college admissions---because it's cheap to apply, people apply to almost every school, whether or not it's a good fit. oDesk recently started using the Elance "Connects" system that imposes a meaningful quota on applications, which in turn seems to be improving application quality. It is hard problem though, because if you set the quota to high, you get the bad spam equilibrium, and if you set it too low, you choke off liquidity.

(4) There is a problem with inflated reputations---it's a general problem in online marketplaces, particularly those with bilateral reputation systems. However, oDesk has done something quite clever which seems to be working well, which is collecting private feedback from both workers and clients after a contract and then eventually disclosing non-identifiable aggregates of those ratings to future employers/workers. These ratings are far more truthful on a host of measures, are harder to subvert by begging for good feedback and so far, aren't getting inflated.

If you're looking for a discussion of running economics-style experiments on MTurk (as well as some advice on keeping online experiments internally valid): http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1591202

That's correct - when the client makes a hire, she can decide whether or not to allow offline hours.

Maybe the fact that moment conditions are often first order conditions for some agent's optimization problem is why rationality was mentioned, but I'm just speculating.

well put.

FYI - Here's a link to an interactive illustration of the deferred acceptance algorithm that I posted a few years back on HN: http://mathsite.math.berkeley.edu/smp/smp.html

Their work has nothing to do with central planning in the sense of committee deciding on production quotas---it's actually about how to allocate indivisible resources (school positions, kidneys, residency slots etc.) in situations where using money isn't feasible and/or the market has broken down due to problems like congestion or lack of market thickness.

The short story is that hand-curation does not seem to work at a very large scale, so we need to create tools for automated discovery (e.g., search, recommendations, controlled skill vocabulary etc.) and automated but credible signals of ability and trustworthiness (e.g., feedback system, skills tests, verified identities etc.). In a nutshell, the challenge is trying to take the things Josh was doing by hand and turn them into data-driven features.

Note: I'm the staff economist at oDesk & I'm on the research/data science team.

Agreed about the 244% - apologies. I glossed over the relative part which is kind of important here. There's still been growth, but it hasn't been that dramatic.

Interesting article, but one quote from the sociologist was way off the mark:

"The historical decline in self-employment and the concomitant rise and dominance of large oligarchic corporations (including chains and franchises) have created barriers of entry for starting and sustaining small businesses and sharply reduced the entrepreneurial path to mobility."

I don't know what time period he's talking about, but self-employment has grown dramatically in the US between 1969-2006, by about ~244% http://en.wikipedia.org/wiki/Self-employment

A conceptually similar paper by another MIT economist looked at how the IP protection afforded to different genes at the time of sequencing affected follow-on research. Genens that were immediately in the public domain stimulated about about 20-30% more research/product development than comparable Celera-sequence genes that had IP protection. The paper is here: http://economics.mit.edu/files/6803

If anyone has any questions about the data itself, I'm happy to answer/elaborate.

There's an understandably human tendency to highlight our accomplishments and bury our failures, which can give inexperienced outsiders/novices in some field a warped perception of what's "normal," exacerbating their self-doubt. I think it's admirable that he posted the letters and was honest about how they made him feel.

There's a famous paper in economics that looks at the long-term labor market outcomes for people that were laid-off because of plant closings versus those were just laid-off: http://ideas.repec.org/a/ucp/jlabec/v9y1991i4p351-80.html

The plant-closing people generally fair better: the idea is that the market/future employers infer that the firm used their discretion when making regular layoffs (using it as a chance to get rid of the low performers) but that no negative inference is warranted in the case of plant closings.

If you were doing SEO/SEM writing or something like that, then maybe, but for these kinds of jobs, you can charge a premium if you know what you're doing & deliver value. Many employers are cheap, but fewer are stupid---they do want to get their projects done.

If you do a pretty targeted job search, there's a decent number of these kinds of jobs on oDesk (full disclosure, I work at oDesk).

Machine learning: https://www.odesk.com/jobs?nbs=1#filter/?qs=machine-learning...

Hadoop: https://www.odesk.com/jobs?nbs=1#filter/?qs=hadoop&st=0

Data Science (as you'll see, not everyone knows what this term means): https://www.odesk.com/jobs?nbs=1#filter/?qs=data-science&...

Algorithms https://www.odesk.com/jobs?nbs=1#filter/?qs=algorithms&s...