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​Is science synonymous with 'truth'? Game theory says, 'not always.'

Good science is sometimes trumped by the craving for a "big splash."

Kevin Zollman: Game theory can be applied to scientific understanding in a lot of different ways. One of the interesting things about contemporary science is that it's done by these large groups of people who are interacting with one another, so science isn't just the lone scientist in his lab removed from everyone else, but rather it's teams working together, sometimes in competition with other teams who are trying to beat them out to make a big discovery, so it's become much more like a kind of economic interaction. These scientists are striving for credit from their peers, for grants from federal agencies, and so a lot of the decisions that they make are strategic in nature, they're trying to decide what things will get funded, what strategies are most likely to lead to a scientific advance. How can they do things so as to get a leg up on their competition and also get the acclaim their peers?


Game theory helps us to understand how the incentives that scientists face in trying to get credit, in trying to get grants and trying to get acclaim might affect the decisions that they make. And sometimes there are cases were scientists striving to get acclaim can actually make science worse because a scientist might commit fraud if he thinks he can get away with it or a scientist might rush a result out of the door even though she's not completely sure that it's correct in order to beat the competition. So those of us who use game theory in order to try and understand science apply it in order to understand how those incentives that scientists face might eventually impact their ability to produce truths and useful information that we as a society can go on, or how those incentives might encourage them to do things that are harmful to the progress of science by either publishing things that are wrong or fraudulent or even withholding information that would be valuable.


This is one of the big problems that a lot of people have identified with the way that scientific incentives work right now. Scientists get credit for publication, and they're encouraged to publish exciting new findings that demonstrate some new phenomenon that we have never seen before. But when a scientist fails to find something, that's informative too—the fact that I was unable to reproduce a result of another scientist shows that maybe that was an error. But the way that the system is set up right now I wouldn't get credit for publishing what's called a null result, a finding where I didn't discover something that somebody else had claimed to discover. So as a result when we look at the scientific results that show up in the journals that have been published it turns out that it's skewed towards positive findings and against null results. A lot of different people have suggested that we need to change the way that scientists are incentivized by rewarding scientists more for both publishing null results and for trying to replicate the results of others. In particular in the fields like psychology and medicine, places where there's a lot of findings and there's lots of things to look at, people really think that we might want to change the incentives a little bit in order to encourage more duplication of effort in order to make sure that a kind of exciting but probably wrong result doesn't end up going unchallenged in the literature.


Traditionally, until very recently, scientists were mostly looking for the acclaim of their peers. You succeeded in science when you got the acclaim of another scientist in your field or maybe some scientists outside your field. But now as the area of science journalism is increasing the public is starting to get interested in science, and so scientists are starting to be rewarded for doing things that the public is interested in. This has a good side and a bad side. The good side is it means that scientists are driven to do research that has public impact that people are going to find it useful and interesting. And that helps to encourage scientists not just to pursue some esoteric question that maybe is completely irrelevant to people's everyday life. The bad side is that now scientists are encouraged to do things that will make a big splash, get an article in the New York Times or make them go viral on the Internet, but not necessarily things that are good science. And so one of the things that people are concerned with is that this new incentive, being popular with the public, can make scientists shift towards research that's likely to be exciting but not necessarily true. Because the public doesn't discover when ten years later we decide that the research was wrong, they just remember the research that they read about in the New York Times ten years before. And so the danger is that lots of misinformation can get spread when scientists are rewarded for making a big splash.

One of the things that we've really discovered through this use of game theory in studying science is that it's really very complicated. That scientists striving to get credit from their peers might initially seem counterproductive, you think, "Well, scientists should care about truth and nothing more." But instead what's actually going on is that sometimes the desire for credit can actually do things that are really good for science. Scientists are encouraged to publish their results quickly rather than sort of keeping them secret until they're absolutely positively sure, and that can actually be beneficial because then other scientists can build on it, can use it in their discoveries. All of that is the good side. There's also a bad side, which is that striving for credit can sometimes encourage scientists to skew towards one project or another and not distribute their labor as a community of scientists over a bunch of different projects. One thing that's really important in science is that scientists work on different projects, because we don't know which project is going to end up working out. There are many different ways to say, detect gravitational waves, or many different ways to try and discover a subatomic particle, and what we want is scientists to distribute themselves so that we have groups working on each different project. The desire for credit can sometimes encourage them to be too homogenous, to all jump onboard with what looks like the best project and not distribute themselves in a good or useful way across all the different projects.

Nobody really has a good idea yet of how to balance these, because there's a good side and a bad side. We don't want to scientists to be completely divorced from the world, but on the other hand the danger is if we put too much emphasis on public reward and the acclaim of the public then we worry that we have scientists just generating false but exciting results over and over and over again in an attempt to get popular acclaim.

  • Scientists strive to earn credit from their peers, for grants from federal agencies, and so a lot of the decisions that they make are strategic in nature. They're encouraged to publish exciting new findings that demonstrate some new phenomenon that we have never seen before.
  • This professional pressure can affect their decision-making — to get acclaim they may actually make science worse. That is, a scientist might commit fraud if he thinks he can get away with it or a scientist might rush a result out of the door even though it hasn't been completely verified in order to beat the competition.
  • On top of the acclaim of their peers, scientists — with the increasing popularity of science journalism — are starting to be rewarded for doing things that the public is interested in. The good side of this is that the research is more likely to have a public impact, rather than be esoteric. The bad side? To make a "big splash" a scientist may push a study or article that doesn't exemplify good science.

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A new hydrogel might be strong enough for knee replacements

Duke University researchers might have solved a half-century old problem.

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Predicting PTSD symptoms becomes possible with a new test

An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.

Image source: camillo jimenez/Unsplash
Technology & Innovation
  • 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
  • Early treatment is available but there's been no way to tell who needs it.
  • Using clinical data already being collected, machine learning can identify who's at risk.

The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.

In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.

That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.

70 data points and machine learning

nurse wrapping patient's arm

Image source: Creators Collective/Unsplash

Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:

"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."

The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.

Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."

Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.

Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.

On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.

Going forward

person leaning their head on another's shoulder

Image source: Külli Kittus/Unsplash

Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."

"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.

The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.

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