The study is on procrastination. My gut says Dan was scrambling for data at the last minute and just threw some numbers together based on his priors.
As the other fraudulent paper in this comment section linked to Dan is about dishonesty, it makes me wonder if there’s more fraud in science papers about bad and wrong choices, or just Dan’s.
I began my interest in economics from the behavioral angle. I continue to be relieved that my alma mater did not offer a behavioral economics specialization.
The whole field is left with a deep credibility hole to dig itself out of.
I think this is a good case study in bottlenecks in AI application. Given the data, detecting this kind of fraud is almost trivial; AI has been more than smart enough to do it, cheaply and quickly, for almost two years now. But the data is hard to get, people don't want to rock the boat, and there's always the risk of lawsuits.
The asymmetry cuts the wrong way, though. The datasets that are easy to pull are the ones somebody chose to deposit, and depositing is exactly the step a fabricator skips. A cheap automated screen therefore ends up pointed at the honest half of the literature, while the papers you most want checked never enter the pipe at all.
Which makes this less of a detection problem than it looks. Better tooling raises the yield on data that is already open; it does nothing about the selection in front of it. The lever is making deposit a condition of the claim rather than a courtesy, and that is a journal policy question.
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Comments Section
The study is on procrastination. My gut says Dan was scrambling for data at the last minute and just threw some numbers together based on his priors.
As the other fraudulent paper in this comment section linked to Dan is about dishonesty, it makes me wonder if there’s more fraud in science papers about bad and wrong choices, or just Dan’s.
It's fine, he'll just throw his post-docs under the bus again.
Dan Ariely seems to be into this fraud thing. https://datacolada.org/109
I began my interest in economics from the behavioral angle. I continue to be relieved that my alma mater did not offer a behavioral economics specialization.
The whole field is left with a deep credibility hole to dig itself out of.
I think this is a good case study in bottlenecks in AI application. Given the data, detecting this kind of fraud is almost trivial; AI has been more than smart enough to do it, cheaply and quickly, for almost two years now. But the data is hard to get, people don't want to rock the boat, and there's always the risk of lawsuits.
The asymmetry cuts the wrong way, though. The datasets that are easy to pull are the ones somebody chose to deposit, and depositing is exactly the step a fabricator skips. A cheap automated screen therefore ends up pointed at the honest half of the literature, while the papers you most want checked never enter the pipe at all.
Which makes this less of a detection problem than it looks. Better tooling raises the yield on data that is already open; it does nothing about the selection in front of it. The lever is making deposit a condition of the claim rather than a courtesy, and that is a journal policy question.
Damn