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.