Dan Ariely’s talk was a standard
Ted talk in that on the surface of the thing it was quite clever, but does not
hold up to much scrutiny. I find his view that people will cheat a little, and
are mainly influenced by their desire to see themselves as good and appropriate
members of self-identified groups, to be reductionist and flawed. My main two
issues with his conclusions are related to one another. The first is the fact
that in using a highly un-random sample of people, i.e. college students, you
are taking a group that is far more likely to be coming from a financially
comfortable position. This feeds into the second, that it seems a harsh
economic model that does not view people as rational actors but as manipulable entities
seems more appropriate. In the majority of cases, a person does not desire to
steal, but the incentives to do so may literally outweigh the costs. If a
person is hungry, it hardly seems a crime for them to steal bread. It comes
down to what the balance of pressures on you to cheat, as we can see evidenced
by businesses doing things they know are illegal, but have been calculated to
be more profitable than the fine for getting caught, taking into account the
chance of being caught at all.
I feel
that this incentive model is more appropriate to life in general as well as the
sciences specifically. It begins from the level of scientific journals, which
are motivated not only by ideals of publishing good science, but also by
getting eyes on what they publish, and most importantly people paying for the privilege.
At the end of the day, Nature, as part of NatureSpringer publishing is a for-profit
operation, and wants highly viewed and cited papers more than it cares about
the quality of the science. Science, despite being held by a non-profit, still largely
follows the same model, seeking “groundbreaking” if less than true science to
chase citations and impact. This trickles down to the scientists seeking
publication to further their own careers. Plos one in my field is considered
the place papers go to die, where null hypothesis are true. We are thankful for
the papers, but dread publishing there ourselves. In the face of career
pressures, publishing negative results is a salvage effort, trying to minimize
losses rather than maximize returns.
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