Bankers aim to maximise profits. Scientists aim to understand reality. But Mike Peacey of the University of Bristol suggests, based on a new model he has just published in Nature, that both professionals are equally likely to conform to whatever views are prevalent, whether they are right or wrong.
In the past decade scientists have raised serious doubts about whether science is as self-correcting as is commonly assumed. Many published findings, including those in the most prestigious journals, have been found to be wrong. One of the reasons is that, once a hypothesis becomes widely accepted, it becomes very difficult to refute it, which makes it, as Jeremy Freese of Northwestern University recently put it, “vampirical more than empirical – unable to be killed by mere evidence”.
There are three possibilities to explain why scientists converge on mistaken conclusions. First, as humans, scientists try to be rational but remain stuck on certain views in the face of contrary evidence. Second, some scientists make up data to further their careers, as happened in a high profile case last year. Third, the “publish or perish” culture forces scientists to consciously or unconsciously gravitate towards results that support their conclusions.
At the heart of science’s attempt to be self-correcting is the peer review system. The hope is that scientists' aim to understand the world will guide them in evaluating the research, and that multiple independent reviews will get rid of some of the biases that usually affect the authors and the reviewers.
Sadly the peer review system does not always live up to its high aims. Some have called to abandon the system, while others insist that, like democracy, it is the least worst system on offer. “Peer review isn’t as bad as many think,” Peacey said. He and his colleagues decided to investigate what some of its faults are and how they could be fixed. They built a computer model to understand how scientists may behave based on some simplified parameters.
Assume a group of scientists is deciding between Hypothesis A and Hypothesis B. Each scientist will have some probability of leaning towards one hypothesis or the other. The computer model begins when a scientist submits a manuscript based on one of these views to a journal. To keep things simple, editors will always pass this manuscript on for peer review. Now the reviewers need to decide whether the manuscript should be published. After which they will also need to decide which hypothesis should they lean towards in their own future submission.
(In reality one of the hypothesis may be correct, if herding occurred on the correct one it won’t be harmful. But that wasn’t the point of the experiment and thus the researchers gave no value judgement to a hypothesis.)
They ran the model in three different conditions. In M1 scientists were allowed to use their own subjective and unpublished results to evaluate the manuscript. In M2 scientists were forced to remain as objective as possible. In M3 all manuscripts were published without peer review.