## LESSWRONGLW

Oscar_Cunningham

Are index funds still a good investment?

Passive investors own the same proportion of each stock (to a first approximation).  Therefore the remaining stocks, which are held by active investors, also consist of the same proportion of every stock. So if stocks go down then this will reduce the value of the stocks held by the average active investor by the same amount as those of passive investors.

If you think stocks will go down across the market then the only way to avoid your investments going down is to not own stocks.

Scoring 2020 U.S. Presidential Election Predictions

I just did the calculations. Using the interactive forecast from 538 gives them a score of -9.027; using the electoral_college_simulations.csv data from The Economist gives them a score of -7.841. So The Economist still wins!

Scoring 2020 U.S. Presidential Election Predictions

Does it make sense to calculate the score like this for events that aren't independent? You no longer have the cool property that it doesn't matter how you chop up your observations.

I think the correct thing to do would be to score the single probability that each model gave to this exact outcome. Equivalently you could add the scores for each state, but for each use the probability conditional on the states you've already scored. For 538 these probabilities are available via their interactive forecast.

Otherwise you're counting the correlated part of the outcomes multiple times. So it's not surprising that The Economist does best overall, because they had the highest probability for a Biden win and that did in fact occur.

My suggested method has the nice property that if you score two perfectly correlated events then the second one always gives exactly 0 points.

Did anybody calculate the Briers score for per-state election forecasts?

Does it make sense to calculate the score like this for events that aren't independent? You no longer have the cool property that it doesn't matter how you chop up your observations.

I think the correct thing to do would be to score the single probability that each model gave to this exact outcome. Equivalently you could add the scores for each state, but for each use the probabilities conditional on the states you've already scored. For 538 these probabilities are available via their interactive forecast.

Otherwise you're counting the correlated part of the outcomes multiple times. So it's not surprising that The Economist does best overall, because they had the highest probability for a Biden win and that did in fact occur.

EDIT: My suggested method has the nice property that if you score two perfectly correlated events then the second one always gives exactly 0 points.

PredictIt: Presidential Market is Increasingly Wrong

Maybe there's just some new information in Trump's favour that you don't know about yet?

Classifying games like the Prisoner's Dilemma

I've been wanting to do something like this for a while, so it's good to see it properly worked out here.

If you wanted to expand this you could look at games which weren't symmetrical in the players. So you'd have eight variables, W, X, Y and Z, and w, x, y and z. But you'd only have to look at the possible orderings within each set of four, since it's not necessarily valid to compare utilities between people. You'd also be able to reduce the number of games by using the swap-the-players symmetry.

Wolf's Dice

Right. But also we would want to use a prior that favoured biases which were near fair, since we know that Wolf at least thought they were a normal pair of dice.

Suppose I'm trying to infer probabilities about some set of events by looking at betting markets. My idea was to visualise the possible probability assignments as a high-dimensional space, and then for each bet being offered remove the part of that space for which the bet has positive expected value. The region remaining after doing this for all bets on offer should contain the probability assignment representing the "market's beliefs".

My question is about the situation where there is no remaining region. In this situation for every probability assignment there's some bet with a positive expectation. Is it a theorem that there is always an arbitrage in this case? In other words, can one switch the quantifiers from "for all probability assignments there exists a positive expectation bet" to "there exists a bet such that for all probability assignments the bet has positive expectation"?

The Kelly Criterion

I believe you missed one of the rules of Gurkenglas' game, which was that there are at most 100 rounds. (Although it's possible I misunderstood what they were trying to say.)

If you assume that play continues until one of the players is bankrupt then in fact there are lots of winning strategies. In particular betting any constant proportion less than 38.9%. The Kelly criterion isn't unique among them.

My program doesn't assume anything about the strategy. It just works backwards from the last round and calculates the optimal bet and expected value for each possible amount of money you could have, on the basis of the expected values in the next round which it has already calculated. (Assuming each bet is a whole number of cents.)