The GMVH seems extremely plausible to me, but many people in my social circle disagree. I think they're letting their politics interfere with their reason; but, out of fear it is in fact I who am embarrassingly wrong, I'm going to test the GMVH by writing a simple evolution-simulation. Roughly:

• simulate a population over time, where some genes can affect fitness variably depending on sex

• after many generations, make some sort of graph from which I can eyeball each sex's fitness-variance

• eyeball it, see if the variances are clearly different in the expected direction

Bias minimization

I want to minimize the ability of my preconceptions to taint my results. And there's the classic trap where you debug a program only until it produces the output you want/expect. So I'm going to try really hard to make this program produce a valid experimental result on the first try:

• I'm going to litter the code with assertions to make sure it's simulating a reasonable, realistic scenario (e.g. "the individuals in each generation should not all share a parent"), so that I don't have to look at the results to notice something's fishy.

• I'm going to put lots of printfs in my code, to print various things that might indicate something weird going on. I'll be sure to not print anything that I can see how it would correlate with whether the simulation supports the GMVH or not.

I'm probably going to fail, because nobody has ever written a nontrivial program that works correctly on the first try. So, after I run the program and get nonsense results, and fix it and re-run it and get nonsense results, etc., and then (of course) finally get the results I expected, I commit to spending at least 3x as long carefully debugging the program as the longest time it took me to resolve any previous issue. (If I give up at any point after the first not-obviously-nonsense run, this market will resolve NO.)

Program sketch

My program will probably look very-but-not-exactly like this one:

• There is a population of 1000 organisms.

  • Each organism has:

    • 1 "sex-gene," which is "A" or "B"

    • many "fitness-genes," each consisting of two numbers: "contribution to fitness-variance if I'm type A" and "... if I'm type B."

    • A "fitness," sampled from Normal(mean=0, variance=sum(gene.fitnessVarianceContribution[o.type] for gene in o.fitnessGenes).

• Each generation consists of 1000 samples from the following distribution:

  • Choose the parents: randomly choose an A-type from the previous generation, where each A's chance of being chosen is proportional to 2^fitness. Same for B, except proportional to (2.2)^fitness.

  • Each of the offspring's genes is selected from one of the parents at 50-50-random.

    • ...and then mutagenesis happens: 10 random fitness-variance-contributions are increased or decreased by a factor sampled from LogNormal(0,0.1).

• After 10,000 generations, make a histogram of all genes' fitness-variance-if-A. Make another histogram for fitness-variance-if-B. See if the histograms clearly represent different distributions.

I'm not sure I've chosen a good function from [each organism's genome] to [its expected number of offspring]; I fear that the exact program described above will lead to unrealistic scenarios where pretty much all the offspring come from only the "alpha" of each type, causing all genes to race for maximal variance. I might change it if I think of a more realistic-seeming model.

(I also might tweak the {population size, genome size, number of generations to run for} after further thought. And, though I could be argued out of this, I reserve the right to increase {genome size, number of generations} even after running the program, to increase resemblance to reality, where genomes and evolutionary-history are both very long.)

Correct my model!

If you create a market of the form "If the GMVH sim is re-run with $SOME_CHANGE, will the difference-in-variability be [larger/smaller]?", and I think your proposed change makes my model importantly more realistic --

(for example,

If the GMVH sim is re-run with a gene controlling offspring-sex-selection, will the difference-in-variability be lower?

)

-- then I will submit a M$200 NO limit-order at 50%, and, after resolving this market, I will implement your change and re-run the sim and tell you the results.

This means that, if you see an important error in my model, and you can predict which direction it will push my results, you can take my money.

(Offer only open while supplies last, "supplies" mostly being my willingness to implement various tweaks to my code.)

Market resolution

At some point before 2023-11-09, I'll run my sim several times, do the final "3x as long" debugging step, and then resolve this market:

• YES if I eyeball the graph(s) and they clearly, consistently-across-the-several-runs, show type B having greater variance

• NO if I think that, in ~half-or-more of the runs, a reasonable person could argue the graphed distributions are the same

• N/A if I never write a sim that runs successfully without failing any of its copious assertions