Interesting how consistent the estimated mean has stayed over time.

When I first learned about social status as a concept, I somehow got the mistaken impression that any kind of status seeking is amoral. This caused me harm because I didn't want to violate any social boundaries, and trying to avoid violating status seeking behavior hobbles your ability to find and follow up on opportunities.

I think status seeking can be zero sum, and in such cases it should be avoided (like playing school with the intention of becoming valedictorian).

Status seeking can be positive sum while consisting of iterated zero sum games (like playing in a tennis club).

Status seeking behavior in positive sum environments generally consists of good things, like working harder at the gym.

The concept is extremely useful to keep in mind when designing environments. What constitutes status seeking should be legible, enable and encourage prosocial behavior, and allow social norms to be learned in a healthy way. Losing in iterated zero-sum games is often a common factor in environments with this attribute, since losing is then an expected outcome of playing, and the game can altered so that an individual loss is seen as providing a gain in knowledge, and continuing to play becomes the source of reward. 

This can be actively implemented into zero-sum social situations by setting up a situation to expose oneself to frequent but non-comprimising losses. Like starting debates to entertain others with the intention of being roasted.

For next time: https://www.lesswrong.com/posts/LfrNFfJFcqnG9WuFf/activated-charcoal-for-hangover-prevention-way-more-than-you ;)

Your timeline was off, but I think your original comment will turn out to have had the right idea. Given the leaps from GPT-3.5 to GPT-4 to Devin to Claude 3.5-Sonnet w/scaffolding, marginal seeming updates to models are turning out to be quite substantial in effective capability.  It's hard to create evaluation harnesses for fuzzy, abstract things like the syntax complexity models can handle, and those abilities do transfer to using the models to automate their own post-training tasks, e.g. like what the self-alignment backtranslation paper's scaling charts showed. The better the model, the better they accomplish these tasks with worse task definitions and less help. The piles of prompts necessary for current agents will be less and less necessary, at some point generated on the fly to meek descriptions like "make a code agent to do ${task}" by the models themselves. Whatever human effort will go into the next generation of unprincipled scaffolding will provide yet greater returns to future models. These factors combined, I expect SWE-Bench progress to be discontinuous and rapid, as it has been so far. 

A very naive extrapolation using polynomial regression from SWE-Bench scores suggests ≥80% by November 2025. I used model release dates for my x-value. Important to note models may be contaminated too.

What about 3.5 pushes it over the threshold to you that was missing in previous models?

Avoidant behavior is more interesting to think of in reverse: why do people do anything in the first place?

Procrastination (in a serial way, i.e. burnout) is due to a failure to respond to the normal incentives people act on in your situation. It can be solved by finding another motivation for the activity.

Emotional or cognitive pain is something we don't treat usually as a learning signal as we treat other sources of pain. This is troublesome since it underpins many self-destructive behaviors and all of our neurotic thinking. Sometimes, gut-wrenching dysphoria is a signal you've touched a hot stove (realizing you said something hurtful), and other times it's a major reaction to what's substantively a small insult.

After having been debilitated for a few days many times following the latter kind of pain, I think the right approach is to run head on into desensitization based on their different causes. Eventually, the logical assessment of "this isn't a threat" will correspond to the perceived reality.

As an example, I used to get offended by a lot of minor politically-charged statements before I read Paul Graham's essay on heresy, then realized the pain of getting offended makes you stronger – it's even fun to seek – and felt it much less intensely after that. The hinge of that phenomenon was the realization that offense comes from things we fear might be true – but what's actually the case can't hurt you since it's always been that way, knowing can only help you make better decisions. In a similar vein, overblown neuroticism comes from the possibility that an insult might be true. Accepting things we don't want to believe about ourselves as data can only help us improve.

I used to get depressed about genetic determinism. It's a two-sentence thought that eliminates your perceived capacity for change.

However, while some predictive models can be built, and many things do revert to the mean –  those are tendencies. You only get a pattern from a behavior that repeats. Some things don't. If you're looking for an overarching cause of life-outcomes, you necessarily cancel out individual variation. 

No study on demographics that includes a section called "the one-off thing that happened to one guy once in defiance of what usually happens". 

Those models don't know what you want. They can't account for your decision to find it. 

Most importantly, they can't account for a systematic effort to find it.

Hide and cover clocks to stop procrastinating. There's no five minutes or five years "from now" that's not just "now" – but quantified time creates the illusion you perceive the future. This creates an emotional relationship to approaching deadlines. If you can't see time in your environment (at least when you want to work), the pain of experiencing "the future" immediately subsides but so does the idea you can put things off.

Useful links: Overcoming Bias, Dr. K, J Krishnamurti, Jeffery Kaplan