## LESSWRONGLW

Arjun Panickssery

# Wiki Contributions

Is this word long or short? Only say "long" or "short". The word is: {word}.

To test out Cursor for fun I asked models whether various words of different lengths were "long" and measured the relative probability of "Yes" vs "No" answers to get a P(long) out of them. But when I use scrambled words of the same length and letter distribution, GPT 3.5 doesn't think any of them are long.

Update: I got Claude to generate many words with connotations related to long ("mile" or "anaconda" or "immeasurable") and short ("wee" or "monosyllabic" or "inconspicuous" or "infinitesimal") It looks like the models have a slight bias toward the connotation of the word.

What's the actual probability of casting a decisive vote in a presidential election (by state)?

I remember the Gelman/Silver/Edlin "What is the probability your vote will make a difference?" (2012) methodology:

1. Let E be the number of electoral votes in your state. We estimate the probability that these are necessary for an electoral college win by computing the proportion of the 10,000 simulations for which the electoral vote margin based on all the other states is less than E, plus 1/2 the proportion of simulations for which the margin based on all other states equals E. (This last part assumes implicitly that we have no idea who would win in the event of an electoral vote tie.) [Footnote: We ignored the splitting of Nebraska’s and Maine’s electoral votes, which retrospectively turned out to be a mistake in 2008, when Obama won an electoral vote from one of Nebraska’s districts.]

2. We estimate the probability that your vote is decisive, if your state’s electoral votes are necessary, by working with the subset of the 10,000 simulations for which the electoral vote margin based on all the other states is less than or equal to E.  We compute the mean M and standard deviation S of the vote margin among that subset of simulations and then compute the probability of an exact tie as the density at 0 of the Student-t distribution with 4 degrees of freedom (df), mean M, and scale S.

The product of two probabilities above gives the probability of a decisive vote in the state.

This gives the following results for the 2008 presidential election, where they estimate that you had less than one chance in a hundred billion of deciding the election in DC,  but better than a one in ten million chance in New Mexico. (For reference, 131 million people voted in the election.)

Is this basically correct?

(I guess you also have to adjust for your confidence that you are voting for the better candidate. Maybe if you think you're outside the top ~20% in "voting skill"—ability to pick the best candidate—you should abstain. See also.)

FiveThirtyEight released their prediction today that Biden currently has a 53% of winning the election | Tweet

The other day I asked:

Should we anticipate easy profit on Polymarket election markets this year? Its markets seem to think that

• Biden will die or otherwise withdraw from the race with 23% likelihood
• Biden will fail to be the Democratic nominee for whatever reason at 13% likelihood
• either Biden or Trump will fail to win nomination at their respective conventions with 14% likelihood
• Biden will win the election with only 34% likelihood

Even if gas fees take a few percentage points off we should expect to make money trading on some of this stuff, right (the money is only locked up for 5 months)? And maybe there are cheap ways to transfer into and out of Polymarket?

Probably worthwhile to think about this further, including ways to make leveraged bets.

Should we anticipate easy profit on Polymarket election markets this year? Its markets seem to think that

• Biden will die or otherwise withdraw from the race with 23% likelihood
• Biden will fail to be the Democratic nominee for whatever reason at 13% likelihood
• either Biden or Trump will fail to win nomination at their respective conventions with 14% likelihood
• Biden will win the election with only 34% likelihood

Even if gas fees take a few percentage points off we should expect to make money trading on some of this stuff, right (the money is only locked up for 5 months)? And maybe there are cheap ways to transfer into and out of Polymarket?

I like "Could you repeat that in the same words?" so that people don't try to rephrase their point for no reason.

In addition to daydreaming, sometimes you're just thinking about the first of a series of points that your interlocutor made one after the other (a lot of rationalists talk too fast).

By "subscriber growth" in OP I meant both paid and free subscribers.

My thinking was that people subscribe after seeing posts they like, so if they get to see the body of a good post they're more likely to subscribe than if they only see the title and the paywall. But I guess if this effect mostly affects would-be free subscribers then the effect mostly matters insofar as free subscribers lead to (other) paid subscriptions.

(I say mostly since I think high view/subscriber counts are nice to have even without pay.)

Paid-only Substack posts get you money from people who are willing to pay for the posts, but reduce both (a) views on the paid posts themselves and (b) related subscriber growth (which could in theory drive longer-term profit).

So if two strategies are

1. entice users with free posts but keep the best posts behind a paywall
2. make the best posts free but put the worst posts behind the paywall

then regarding (b) above. the second strategy has less risk of prematurely stunting subscriber growth, since the best posts are still free. Regarding (a), it's much less bad to lose view counts on your worst posts.

[Book Review] The 8 Mansion Murders by Takemaru Abiko

As a kid I read a lot of the Sherlock Holmes and Hercule Poirot canon. Recently I learned that there's a Japanese genre of honkaku ("orthodox") mystery novels whose gimmick is a fastidious devotion to the "fair play" principles of Golden Age detective fiction, where the author is expected to provide everything that the attentive reader needs to come up with the solution himself. It looks like a lot of these honkaku mysteries include diagrams of relevant locations, genre-savvy characters, and a puzzle-like aesthetic. A bunch have been translated by Locked Room International.

The title of The 8 Mansion Murders doesn't refer to the number of murders, but to murders committed in the "8 Mansion," a mansion designed in the shape of an 8 by the eccentric industrialist who lives there with his family (diagrams show the reader the layout). The book is pleasant and quick—it didn't feel like much over 50,000 words. Some elements feel very Japanese, like the detective's comic-relief sidekick who suffers increasingly serious physical-comedy injuries. The conclusion definitely fits the fair-play genre in that it makes sense, could be inferred from the clues, is generally ridiculous, and doesn't offer much in the way of motive.

If you like mystery novels, I would recommend reading one of these honkaku mysteries for the novelty. Maybe not this one, since there are more famous ones (this one was on libgen).

Ask LLMs for feedback on "the" rather than "my" essay/response/code, to get more critical feedback.

Seems true anecdotally, and prompting GPT-4 to give a score between 1 and 5 for ~100 poems/stories/descriptions resulted in an average score of 4.26 when prompted with "Score my ..." versus an average score of 4.0 when prompted with "Score the ..." (code).