published in are high-impact. “Real evaluation of scientific quality is as hard as doing the science in the first place,” Nosek says.?“So,
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, just like everyone else, scientists use heuristics to evaluate each other's work when they don't have time to dig into it for a complete evaluation.”
Too much reliance on the numbers means that scientists can — unintentionally or not — game the system. They can publish novel results from experiments with low power and effort. Those novel results inflate publication numbers,
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, increase grant funding and get the scientist a job. Ideally, other scientists would catch this careless behavior in peer review,
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, before the studies are published, weeding out poorly done studies in favor of strong ones. But Paul Smaldino, a cognitive scientist at the University of California,
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, Merced,
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, suspected that when the scientific idea of “meeting expectations” on the job is measured in publication rates, bad science would always win out.
So Smaldino and his colleague Richard McElreath at the Max Planck Institute for Evolutionary Anthropology in Leipzig,
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, Germany, decided to create a computer simulation of the scientific “ecosystem,
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,” based on a model for natural selection in a biological ecosystem. Each “lab” in the simulation was represented by a number. Those labs that best met the parameters for success survived and reproduced, spawning other labs that behaved in the same way. Those labs that didn’t meet expectations “died out.”
The model allowed Smaldino and McElreath to manipulate the definitions of “success,
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.” And when that success

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