Science does not solve our problems. People do.

when we look only to scientists for solutions, we shoot ourselves in the foot

Recent opining over a loss of trust in science and scientific institutions ignores some critical context. The Center for Disease Control (CDC), the National Institute of Health (NIH), and the World Health Organization (WHO) have made real mistakes in the official interpretations of pandemic data. Each institution has also provided contradictory guidance and engaged in fear-mongering. It is fair to point out that our scientific institutions erred in applying even some of the most fundamental epidemiology theories and ethical principles to the design of our public health response.

In many ways, the Limits of Inference is my plea to fix these problems to benefit future generations. The mistakes are not new, but symptoms of a cultural problem pervasive in scientific institutions. To fix this, we first have to admit there is a problem.

A disclaimer seems necessary, given the political climate. I am not saying WHO or CDC messed up because I do not ‘believe in science.’ I am a scientist. This familiarity allows me to assert with confidence that the pursuit of knowledge remains a political, human, and often flawed process. It is crucial to judge the institutions by the quality of the output, not only the scientific method’s theoretical potential.

The CDC and WHO both erred in the stories they told to explain COVID-19 data. Like everyone else, scientists write stories to make sense of data. If you are not familiar with my ‘stories we tell ourselves’ framework (SWTO), check out an intro here. As a brief reminder, data tells us that something happened, not why. The why is a story that a human — whether it is a scientist, an analyst, a politician, etc. — writes to make sense of it. There is never only one story that can explain a dataset. Instead, the person doing the analysis chooses what they consider the best story and presents that as an argument to inform a specific decision or achieve a particular outcome. Scientists have no secret method to better storytelling, just the luxury of a lifelong focus on a narrow problem space.

I am frustrated with our scientific institutions today due to the repeated insistence that ‘science says lockdowns are necessary.’ My frustration is not only because I disagree with the conclusion (I think we have better options), but because that statement is unscientific on its face. Science cannot say what we should do about any problem—pandemic or otherwise— because we make decisions based on goals, values, and ethics. Science has no system of ethics, nor any knowledge of our goals or the available solutions. Hence science has no opinion on any policy in response to the pandemic or any problem in society. Individual scientists can have opinions, however, and that is the point. Science does not say lockdowns are necessary; some scientists do. Our leading scientific institutions and their leadership do not know the difference between science and their own opinions. This blindspot is a red flag that government leaders and decision-makers should not overlook.

Thomas Malthus has the infamous position of being synonymous with the type of hubris that plagues these modern scientists. In 1798, Malthus analyzed historical trends and observed that human population growth is exponential, while food and resource growth is linear. As technology improved, the food supply increased, but the population also grew as a result. As the population grew faster than the food supply (see the graph), humanity kept finding itself in a trap. Therefore, Malthus concluded there was an upper limit on the earth’s population. If surpassed, a catastrophe such as famine and war must occur. 

Then, the industrial revolution began. Food supply outpaced population growth. Malthus’ prediction was made to look ridiculous within only a few years. 

Notice the form of the error he makes in his doomsday predictions for the human race: Malthus could not imagine a technology that would increase food production meaningfully, so he concluded that no solution would ever exist. He told this story using both data and mathematical models — It has not happened in the past. I cannot imagine how this would happen; therefore, it cannot occur. Intentional or not, Malthus told a story that assumes all human ingenuity resided in his mind and his mind alone.

One thing to realize about Malthus in the context of the stories framework — Malthus was correct on the science itself.  The fundamental relationships Malthus observed about food production and population growth and their respective growth curves were accurate in 1798 and are still fair approximations of the dynamics today. The mistake was that science could not (and still cannot) be used to know the solutions to problems. Science cannot inform us about what is possible, just what is currently happening. 

Science says catastrophe will inevitably occur. Science says global lockdowns are the only way out. The parallels between Malthus and some of our current scientific leaders are hard to ignore. Even when presented directly with the idea that they could be wrong, our scientific leadership insists — by the authority of The Science, The Data, and The Math— that there are no other viable alternative solutions. Clearly, the sarcastic emphasis and capitalization are mine.

This hubris stems from a pervasive cultural problem within scientific professions that incentivizes scientists to see their own perspective as objectively correct, instead of a story they have constructed to explain some limited data. We have trained an entire generation of professional scientists, and now another new field of data scientists, to believe that data and math matter more to problem-solving than reason, creativity, and strategic thinking.

Scientists, by-and-large, are not inventors, not engineers, not entrepreneurs. Solutions are not a laboratory scientist’s wheelhouse. Many people can contribute to solving our world’s biggest problems. We need more than just a tiny-group of narrow-minded scientists thinking about how. Scientists should be asking for help, not claiming to know everything themselves.

Solutions to our problems do not come from analyzing data—creativity and vision matter. Even scientific research is a creative endeavor when done well. Data is only a tool. Let’s work to understand why this is true more intuitively, preferably before the next global pandemic. More to come.