The analytic result (that only 2 of 736 strategically unique ordinal 2×2 games are PDs) is interesting, the numerical simulation less so; the paper doesn’t motivate the specific choice of a uniform distribution from which to randomly sample payoffs. (I can imagine the results changing quite a lot if the payoffs are taken from e.g. lognormal or normal distributions instead.)
Wanna bet? I made a few PredictionBookentries. The author was nice enough to give me his source code; I modified it and ran it. I will edit this comment as soon as I finish cranking out the results. I’ll rot13 them, or hide them in a spoiler window (is that possible?), for those who’d like to try a Prediction Book probability estimate.
I neglected to make Prediction Book entries for the lognormal distribution, but they would have been similar. Herewith, the results in rot13:
Normal distribution: guerr cbvag sbhe creprag
Lognormal: guerr cbvag svir creprag
Additional edit: based on a few runs, the error of those estimates from the true mean is of the order of 0.1%.
I’d put, say, 45% on continuous normal distributions giving a <3.4% PD proportion, and 55% on them giving a ≤5.1% distribution. (I don’t have a PredictionBook account so I’ll just note those probabilities here before I look at your answers.)
Edit: I’ll rot13 everything after this sentence in case anyone else is making guesses. V jnf jebat! Gur cerpvfr qvfgevohgvba nccrnef gb znxr ab qvssrerapr, cerfhznoyl orpnhfr gur CQ’f na beqvany curabzraba, abg n pneqvany bar. (Cerfhznoyl bar pbhyq cebir guvf zngurzngvpnyyl gb obyfgre Ehfpu’f cbvag.)
The analytic result (that only 2 of 736 strategically unique ordinal 2×2 games are PDs) is interesting, the numerical simulation less so; the paper doesn’t motivate the specific choice of a uniform distribution from which to randomly sample payoffs. (I can imagine the results changing quite a lot if the payoffs are taken from e.g. lognormal or normal distributions instead.)
Wanna bet? I made a few Prediction Book entries. The author was nice enough to give me his source code; I modified it and ran it. I will edit this comment as soon as I finish cranking out the results. I’ll rot13 them, or hide them in a spoiler window (is that possible?), for those who’d like to try a Prediction Book probability estimate.
I neglected to make Prediction Book entries for the lognormal distribution, but they would have been similar. Herewith, the results in rot13:
Normal distribution: guerr cbvag sbhe creprag
Lognormal: guerr cbvag svir creprag
Additional edit: based on a few runs, the error of those estimates from the true mean is of the order of 0.1%.
I’d put, say, 45% on continuous normal distributions giving a <3.4% PD proportion, and 55% on them giving a ≤5.1% distribution. (I don’t have a PredictionBook account so I’ll just note those probabilities here before I look at your answers.)
Edit: I’ll rot13 everything after this sentence in case anyone else is making guesses. V jnf jebat! Gur cerpvfr qvfgevohgvba nccrnef gb znxr ab qvssrerapr, cerfhznoyl orpnhfr gur CQ’f na beqvany curabzraba, abg n pneqvany bar. (Cerfhznoyl bar pbhyq cebir guvf zngurzngvpnyyl gb obyfgre Ehfpu’f cbvag.)