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Science in survival mode

Scientific research is unreliable. It comes with uncertainty. Whether launching a rocket or measuring racial prejudice, there is uncertainty. We use this uncertainty to make decisions. If the rocket has a 40% chance of exploding, best not to stick astronauts in it. If skin-tone bias of soccer referees is somewhere from none to a lot, it is irresponsible to conclude they are prejudiced.

Investigating uncertainty, is science. Truth and uncertainty are two sides of the same coin, they co-define each other. A problem for humans measuring uncertainty, is that humans are unreliable. Human scientists themselves add uncertainty to the measurement of uncertainty.

Recent studies suggest that somewhere between 25 and 60% of published statistical results cannot be recreated using the materials provided – these measures of uncertainty come with their own uncertainty. It turns out that scientific researchers are doing some really peculiar things to generate uncertainty. Some surveys suggest as many as 9% of scientists faked data at least once in their careers, and that more than half selectively reported findings – a behavior that makes the things they study to appear less uncertain than they actually are.

I believe the answer lies in their humanness. Like all animals, they are genetically programmed for survival. They are capable of both rational, reflective decision-making and split second reaction without any thought. Given time to reflect, a human would generally conclude murder is unethical, but simultaneously would not hesitate to kill if it prevented their child from being killed. Murder remains wrong, but to not kill and let a child die is also wrong. It would be irresponsible parenting failing to ensure survival.

Murder is a profound act when a human perceives themselves to be in a situation of life or death. It is a symptom of subconsciously activated defense mechanisms in survival mode. But there are many other symptoms. In order to avoid death, humans, like other primates can engage in deception, disassociation, aggression, manipulation, submission, scapegoating, theft and hoarding. If someone held me at gun point and told me to prove the earth was flat, I would have no problem doing it. The math would work, I would just need to fake a little data

Most scientists have highly valued knowledge and competencies and thus live in situations where their lives are not under threat. At least not as a result of their scientific practice. For the sake of this thought experiment, lets just rule out regularly occurring life-threatening danger as a cause of scientists exhibiting survival mode behaviors. This leaves the perception that they are under threat, as a possible explanation.

It takes only a few stimuli to induce survival mode behavioral changes in animals. Like hearing a frightful noise when seeing an animal. This animal and anything that looks like it become automatic sources of anxiety and fear even without the sound. Imagine being told over and over and over that you have to have an exciting study with powerful results in order to get an academic job after graduate school, otherwise you wasted 3-8 years of your life and probably a large chunk of capital on getting a PhD. Could this alone, without introducing any actual shocks or physical pain, induce Pavlovian fear? I encourage you to go ask any graduate student to answer this that does not yet have such a study.

Now imagine that during graduate school a student invests all their time and resources into an experiment. After it is complete, they hypothesized result, the one sure to be exciting and publishable, is not there. Imagine the horror, the shame, the feeling of failure, the panic. Remember the poor soul who leaped to his death because he misunderstood futures trading and thought he owed three-quarters of a million dollars? That was a triggered survival response. Because dying felt like the only way to ‘survive’ the horror of facing that debt. Imagine if he could have just changed the futures market by adding his own numbers to the market. Would he have done it?

We should not be surprised at all then, when scientists acting out of fear-based survival strategies, fake data. Diederik Stapel faked an entire career of data before being caught. His behavior was self-described as an “addiction”. A common reaction of individuals placed under fear stimuli that are emotionally damaging if not traumatic. The intense pressures and expectations of the academic environment created a context in which he felt compelled to engage in unethical practices to maintain his status and success. It does not make murder or data-faking right, but to not take this as grounds for indicting the scientific rewards system is certainly wrong.

The incentive structures have to change before we can honestly expect the fear-driven pressure to fake, cheat, lie or steal – in order to avoid the experience of loss associated with the common null results that occur when conducting high quality scientific research on radically complex human brains and societies that are frustratingly difficult to measure things in – to go away.

Love is in the error term

A major segment of social science uses formal models and quantitative methods to explain social phenomena. That means they use mathematical symbols to define how they think the world works, and then find data and test it. For example, researchers want to explain social outcomes, like committing a crime, changing jobs, moving or having children. Why do some people do these things and not others? Researchers then speculate that other things cause these outcomes like getting married, having a job or losing a job, how much money a person makes, how old they are and all kinds of other stuff.

Next, researchers change their theoretical ideas into a formal model. This means an equation. Before you stop reading, maybe pause to consider that an equation is just a theory expressed with symbols instead of standard language. For example,

Y = X

and this might represent moving homes (“Y“) is caused by getting married (“X“). Another way of saying this is, Y depends on X or moving depends on getting married.

But seriously, getting married does not always cause moving. Sometimes it does. The correct claim is that moving is a function of getting married. Function means that marriage probably increases the likelihood of moving by some amount. Therefore, researchers add a modifier to X, like the letter b, so if b was 0.3 getting married would increase the likelihood of moving by 30%. People also move when they don’t get married, so researchers add a constant a to account for the likelihood of a person moving for any other reason. So if a was 0.3 then the average person would have a 30% chance of moving at any point.

Y = a + bX

Now if a and bX could be used to perfectly predict whether a person moves or not. The researcher would have made a monumental achievement here. Instead what happens is the researcher goes and observes moving and marriage behaviors. Tries to do this with a random sample of the population of a society. Then applies the above equation to the sample data. Does the equation fit perfectly to the data? No. Never. Researchers must admit that their theories come with uncertainty. Maybe moving depends on the type of home the person always lives in, maybe it depends on whether a couple can afford to move or if it is even a couple or a single parent. There are nearly unlimited things that could cause moving. Also, getting a perfectly random sample, mistakes when observing or coding data, etc. leads to uncertainty in the results. This means the formal equation has to have e, an error term.

Y = a + bX + e

Researchers spend their time trying to reduce what is in the error term. So they are basically tasked with the job of reducing uncertainty. If scientists wants to fly a spaceship to mars they have to reduce all possible forms of uncertainty in the take off, flight path, landing, electronics and so forth. But a social scientist who wants to predict how many people will move, where they will move or which people will move, cannot reduce uncertainty that much. Sure, they can explain moving with job changes, family members getting sick, weather, hobbies, wars, and so many observable things. But they will never fully explain moving. What I’m really saying here is that social scientists will never be able to perfectly explain human behaviors. Its like this:

An Enlightened Physicist describes Sociology

Why is it so hard? If we could just measure all the things that people are doing and thinking we could predict precisely what humans will do next, right? Maybe so, maybe not; but irrelevant because we cannot measure everything about humans. Even if we could measure the precise actions of the network of neurons in one human brain numbering greater than all the stars in the universe, we probably couldn’t measure love. But something that we might refer to as love seems to be a major force in guiding human actions. Both the effect of love:

“Love affects more than our thinking and our behavior toward those we love. It transforms our entire life”

– Thomas Merton

And the lack of love:

“Intense spiritual and emotional lack in our lives is the perfect breeding ground for material greed and overconsumption.”

– bell hooks

(both quotes from hooks 2000, pp. 187 & 105)

These suggest love is a prime mover of human decisions and behaviors. But we don’t know what it is exactly and we have made no attempts, that I am aware of, in any large population surveys to measure it. So,

When we add love as an X variable to our formal model we cannot test anything, because we have not developed instruments to measure it. So it remains in the void of all that variance in Y that we simply cannot explain. Maybe we should start focus some of our effort on that.

hooks, bell. 2000. All About Love: New Visions. William Morrow and Company, New York, NY. ISBN 0-688-16844-2.