True, the typical argument for the great silence implying a late filter is weak, because an early filter is not all that a priori implausible.
However, the OP (Katja Grace) specifically mentioned "anthropic reasoning".
As she previously pointed out, an early filter makes our present existence much less probable than a late filter. So, given our current experience , we should weight the probability of a late filter much higher than the prior would be without anthropic considerations.
Individuals may be bad at foresight, but if there's predictably going to be a good price for 100000 coats in a few months, someone's likely to supply them, unless of course there's some anti "price gouging" legislation.
If you didn’t account for selection effects, you may have correctly avoided boosting DEX because you thought it was actively harmful instead of merely useless.
I immediately considered a selection effect, but then I tricked myself into believing it did matter by a method that corrected for the selection effect but was vulnerable to randomness/falsely seeing patterns. Oops. Specifically I found the average dex for successful and failed adventurers for each total non-dex stat value, but had them listed in an inconvenient big column with lots of gaps. I looked at some differences and it seemed that for middle values of non-dex stats, successful adventurers consistently had lower average dex than failed ones, while that reversed for extreme values. When I (now - I didn't at the time) make a bar chart out of the data it's a lot more clear that there's no good evidence for any effect of dex on success:
If you didn’t look for interactions, you may have dodged the WIS<INT penalty just because WIS seemed like a better place to put points than INT.
Yep. Thing is, I *did* look for interactions - with DEX. I had the idea that DEX might be bad due to such interactions, and when I didn't find anything more or less stopped looking for such interactions.
And I’m pretty sure even the three people who submitted optimal answers on the last post (good job simon, seed, and Ericf) didn’t find them by using the right link function
For sure in my case. I calculated the success/fail ratios for each value of each stat individually (no smoothing), and found the reachable stat combo that maximized the product of those ratios. This method found the importance of reaching 8. I was never confident that this wasn't random, though.
When I did later start simming guesses what I simmed would have given smoothed results: a bunch of stat checks with a D20, success if total number of passed stat checks greater than a threshold. The actual test would have been pretty far down in the list of things I would have checked given infinite time.
>! in reply to:
Graduate stats likely come from 2d10 drop anyone under 60 total
I think you're right. The character stats data seems consistent with starting with 10000 candidates, each with 6 stats independently chosen by 2d10, and tossing out everything with a total below 60.
One possible concern with this is the top score being the round number of 100, but I tested it and got only one score above 100 (it was 103), so this seems consistent with the 100 top score being coincidence.
You do indeed miss out on some gains from a jump - WIS gets you a decline in success at +1 but a big gain at +3. (Edit: actually my method uses odds ratio (successes divided by failures) not probabilities (successes divided by total). So, may not be equivalent to detecting jump gains for your method. Also my method tries to maximize multiplicative gain, while your words "greatest positive" suggest you maximize additive gain.)
STR - 8 (increased by 2)
CON - 15 (increased by 1)
DEX - 13 (no change)
INT - 13 (no change)
WIS - 15 (increased by 3)
CHA - 8 (increased by 4)
calculation method: spreadsheet adhockery resulting in tables for each stat of:
per point gain = ((success odds ratio for current stat)/(success odds ratio for current stat + n))^(1/n), find n and table resulting in highest per point gain, generate new table for that stat for new stat start point and repeat.
str +2 points to 8, con +1 point to 15, cha +4 points to 8, wis +3 points to 15, based on assuming that a) different stats have multiplicative effect (no other stat interactions) and b) that the effect of any stat is accurately represented by looking at the overall data in terms of just that stat and that c) the true distribution is exactly the data distribution with no random variation. I have not done anything to verify that these assumptions make sense.
dex looks like it actually has a harmful effect. I don't know whether the apparent effect is or is not too large to be explained by it helping bad candidates meet the college's apparent 60-point cutoff.
I would worry in a lot of these cases that there's some risk that your model isn't taking account of, so you could be "picking up pennies in front of a steamroller". Not in all cases though - 70-200% isn't pennies.
But things like supposedly equivalent assets that used to be closely priced now diverging seems highly suspicious.
You need to have a private key to sign, otherwise it would be useless as a "signature".
For signing (in the non-ring case), you encrypt with your private key and they decrypt with your public key, whereas in normal encryption (again, non-ring) you encrypt with their public key and they decrypt with their private key.
It's not necessarily structural inefficiency at PredictIt specifically that is causing most of this, but to a large extent bettors pricing in the odds of Trump still winning the election. Apparently Betfair's odd of Trump winning are still around 10% - link I found from searching for articles on betting odds from the last day, but I wasn't able to find the odds at Betfair itself.
Yes, if you consider one branch of the wavefunction, you have less than full information than the full state you branched from. But, the analogous situation would apply to a merger of different branches - you would have less than full information in one of the initial branches regarding the full resulting merged state.