A recent Nature article got lots of press: We present an evolutionary model showing that, counterintuitively, overconfidence maximizes individual fitness and populations tend to become overconfident, as long as benefits from contested resources are sufficiently large compared with the cost of competition. In contrast, unbiased strategies are only stable under limited conditions. The fact that overconfident populations are evolutionarily stable in a wide range of environments may help to explain why overconfidence remains prevalent today.
Evolutionary or any other simulations are not data, they are theory! A computer simulation is logically equivalent to a theory, i.e. it is deductive and produces the output you want. A simulation does not provide any new information, all it tells you is that your theory can be modelled using a simulation; but of course e.g. in neurology you can always create a simulation that functions as your theory predicts.
I looked through your paper and it seems you come to a different conclusion:
"Second, there is a strategic effect, whereby confidence variations can let agents commit to strength in a conflict. This strategic effect induces over-confidence in high ability agents, and under-confidence in low ability agents..."
Don't they find only overconfidence? I don't see the benefit here to under-confidence.
I think the contrast between what is published in the hard sciences and in social sciences derives from a disrespect for the field. In psychology, only two kinds of papers are published --amazing babies with amazing cognitive systems showing humans are just one down from god OR comparisons with nonhuman primates showing we are one up from the animals. I think this is how physicists or molecular biologists like to think about human thought. But many of the interpretations in these studies are in direct contradiction to a long history of empirical work. alas.
There is a qualitative difference between "we have a mathematical model that gives us X result" and "we have run an experiment that gave us X result". There are things to be said in favor of each of them, ideally they would go together. Perhaps you could have taken your few lines of game theory math and used them to make a testable prediction, which you would then test by means of an evolutionary simulation, and that would have been a better paper than either.
In general there seems to be a big push for (natural) scientists to come in and "clean up " the social sciences in general and economics in particular. Witness the current faddish brouhaha over "econophysics." While there have been a few good papers published, 95% of econophysics papers are garbage.
This scourge has been especially prevalent in my domain of expertise, market microstructure. It seems like every other day there's a new paper on Arxiv by some physicist or computer scientist, with absolutely zero knowledge of economics or finance, thinking that some model based on deeply flawed assumptions, is going to overturn 50 years of established research.
You might try submitting your paper for publication before you use its rejection as an example of bias.
I'm not in a position to evaluate whether your paper should be accepted or rejected either on its own merits or due to Nature's mission, but I do know your conclusion isn't supported by the hypothetical rejection.
I don't think it's a conspiracy against econ; mediocre papers in every subject get in to Nature. It might be particularly bad in econ (I don't have the perspective to judge) but that really only points to a poor deputy editor.
Am I wrong or does this theory provide a decent explanation for the paradoxal Dunning-Kruger effect ? Another example of the "power of stupidity" maybe.
Congrats for thinking about the underlying game theory model years ago, too bad you don't get some credit for that. Looks like adding a dose of "evolution" in anything these days is key to get published.
I don't Understand how you reached your conclusion.
I would read this just as "nature liked the idea of publishing a paper with lots of evolutionary simulations, even if it wasn't clear they added anything to basic game theory."
When you say your own paper was "too simple, that seems to get something right. Journals may well have a bias in favor of complicated papers.
I just don't see where the headline is coming from.
Evolutionary or any other simulations are not data, they are theory! A computer simulation is logically equivalent to a theory, i.e. it is deductive and produces the output you want. A simulation does not provide any new information, all it tells you is that your theory can be modelled using a simulation; but of course e.g. in neurology you can always create a simulation that functions as your theory predicts.
Have you sent a link to your paper to the authors of the Nature article? They might be interested in seeing it.
I looked through your paper and it seems you come to a different conclusion:
"Second, there is a strategic effect, whereby confidence variations can let agents commit to strength in a conflict. This strategic effect induces over-confidence in high ability agents, and under-confidence in low ability agents..."
Don't they find only overconfidence? I don't see the benefit here to under-confidence.
I think the contrast between what is published in the hard sciences and in social sciences derives from a disrespect for the field. In psychology, only two kinds of papers are published --amazing babies with amazing cognitive systems showing humans are just one down from god OR comparisons with nonhuman primates showing we are one up from the animals. I think this is how physicists or molecular biologists like to think about human thought. But many of the interpretations in these studies are in direct contradiction to a long history of empirical work. alas.
All that would do is test the mathematical model, whose results are already known!
There is a qualitative difference between "we have a mathematical model that gives us X result" and "we have run an experiment that gave us X result". There are things to be said in favor of each of them, ideally they would go together. Perhaps you could have taken your few lines of game theory math and used them to make a testable prediction, which you would then test by means of an evolutionary simulation, and that would have been a better paper than either.
In general there seems to be a big push for (natural) scientists to come in and "clean up " the social sciences in general and economics in particular. Witness the current faddish brouhaha over "econophysics." While there have been a few good papers published, 95% of econophysics papers are garbage.
This scourge has been especially prevalent in my domain of expertise, market microstructure. It seems like every other day there's a new paper on Arxiv by some physicist or computer scientist, with absolutely zero knowledge of economics or finance, thinking that some model based on deeply flawed assumptions, is going to overturn 50 years of established research.
You might try submitting your paper for publication before you use its rejection as an example of bias.
I'm not in a position to evaluate whether your paper should be accepted or rejected either on its own merits or due to Nature's mission, but I do know your conclusion isn't supported by the hypothetical rejection.
I don't think it's a conspiracy against econ; mediocre papers in every subject get in to Nature. It might be particularly bad in econ (I don't have the perspective to judge) but that really only points to a poor deputy editor.
Am I wrong or does this theory provide a decent explanation for the paradoxal Dunning-Kruger effect ? Another example of the "power of stupidity" maybe.
Congrats for thinking about the underlying game theory model years ago, too bad you don't get some credit for that. Looks like adding a dose of "evolution" in anything these days is key to get published.
I don't Understand how you reached your conclusion.
I would read this just as "nature liked the idea of publishing a paper with lots of evolutionary simulations, even if it wasn't clear they added anything to basic game theory."
When you say your own paper was "too simple, that seems to get something right. Journals may well have a bias in favor of complicated papers.
I just don't see where the headline is coming from.