Mankind's progress in science and technology is often considered one of the most spectacular aspects of human civilization. Many historical ages and technical revolutions are named after the scientific discoveries that marked them: the Bronze Age, the Atomic Age, the Information Age, the Agricultural Revolution and the Industrial Revolution, to name just a few. When we add to our list the inventions that were not given their own eras but were no less crucial — the control of fire, the dawn of writing, and the creation of the wheel, for example — the human capacity for discovery seems remarkable indeed.
The common perception is that mankind's scientific and technological progress is accelerating. Some say that this acceleration is exponential, like Moore's Law of progress in electronic circuits, and that it occurs across many fields of innovation. They even say we might reach a point in the coming decades when machines overtake humans in intelligence. But while computer chips may serve as one of the better metaphors for the complexity of the human brain, using them as a model to predict mankind's future outputs would require a huge leap of faith.
Science and Technology: A Glass Half Full or Half Empty?
Humans are born with a naive understanding of physics that has allowed us to become, with thousands of years of practice, increasingly proficient hunter-gatherers. The banging together of stones to fashion them into specialized instruments, such as arrows, is an early application of the knowledge of mechanics to an understanding of the laws of gravity and motion, not to mention the utility of opposable thumbs. This is why, when you visit a natural history museum, you will see stone tools from prehistoric ages that generally improve with each new period of time. Despite this evolutionary advantage, it remains something of a mystery as to how our species has been able to develop laws of nature about realms that are far beyond our direct experience, such as the world of infinitesimal subatomic particles or the unfathomable universe of deep space. Cognitive scientists explain our understanding of the higher mathematics at the foundation of these laws as an application of the innate human capacity to adopt and adapt basic metaphors from one domain to another, layering one upon another to build up to ever more abstract concepts. In other words, we have taken a natural mental process and extended its use beyond what it was intended for, thus exposing us to gaps in the consistency and integrity of our intellectual reaching.
While the discourse often focuses on the bright side of scientific achievement, I will concentrate on the less well-lit but no less relevant points of the issue. Though most science enthusiasts — a group I consider myself a part of — see the glass as half full, I would argue that our failure to apply scientific logic to the process of pursuing science itself leaves the glass more than half empty. Let me explain.
The scientific method dates back to the 17th century, and Nobel Prizes have been awarded since the start of the 20th century. But it was not until 2007 that the U.S. National Science Foundation began granting research awards (a little more than 30 per year) in the name of advancing evidence-based execution of research — a mere drop in the bucket considering the thousands of awards made to science as a whole each year.
In 2013, two Nobel Prize winners, one in physiology/medicine and another in physics, used their acceptance speeches as an opportunity to lament the poor quality assessment standards of research. One noted that only articles published in the top three scientific journals could cement a scientist's position as a professor or researcher, regardless of the varying quality of articles within those journals or the fact that other journals also published excellent articles. The other winner noted that until he was awarded the Nobel Prize, he was considered an embarrassment to his faculty department for having published so few articles. He offered his suspicions that if he were to have applied for a post-doctorate position as a recent Ph.D. graduate, he would not have been able to get one.
For more than two centuries, science policy has been based in eminence rather than evidence, left to the opinions of prominent professors and key opinion leaders rather than relying on the methodical assessment of quality research. Today this remains largely unchanged, and because scientific research on the process of making research policy itself will take years to yield applicable findings, it is likely to remain unchanged over the next few decades.
For the most part, we do not set advancement goals and manage their execution in a way that is heavily guided by scientific principles. Since its inception with Frederick Winslow Taylor's "Taylorism" theory in the early 1900s, the concept of scientific management has remained in its infancy. Research laboratories at universities, public institutions and private companies are run at a quality maturity level (a measurement of an organization's quality management processes) not much higher than average, despite the great intellectual abilities of its members. Instead, corporations that tend to score the highest in quality maturity are typically found in the petroleum, automobile, telecommunications and industrial machinery industries, and these companies invest an average of only 10 percent of their annual sales revenue in scientific research. It seems odd that the organizations most directly responsible for the advancement of science have not taken the opportunity to be at the forefront of a more scientific approach to management.
A Glass More Than Half Empty
The inefficiency in our current research performance can be difficult to measure, particularly without published figures and future targets of many scientific enterprises. However, let us use an example in the field of biomedical research. A recent report in The Lancet estimated that some 85 percent of global investment in biomedical research, which totaled about $240 billion in 2010, is wasted. The waste did not come from findings that could not be used for anything but expanding our own knowledge; this is the very essence of scientific experimentation. Rather, the waste came solely from unjustifiable and avoidable losses in performance stemming from, among other things, unsound priorities, incorrect design methodologies, inappropriate regulations, incomplete reports and the faulty conduct and analysis of research. Perhaps unsurprisingly, recommendations to correct the situation included conducting a more scientific assessment of the research experiment's yield and potential use.
Redirecting scientific priorities may not be feasible in the near future, particularly when rather prominent human biases are involved in assessing priority in the first place. For example, we have a tendency to favor innovations that are easy to relate to our everyday physical experience or those that have positive cultural images. A scientist has to work much harder to secure funding for research on the archaebacteria in the oceanic volcanic vents than on the microflora used to ferment wine, just as the proctologist has more difficulty finding financial backing than the neurosurgeon. We also tend to favor measures that cure rather than prevent; a cure evokes all the dramatic flare of heroes, whereas by comparison prevention seems rather like watching paint dry. Again, this makes sense from an evolutionary perspective, since we are wired to adapt to the dangers and survival needs of the immediate environment. It was only with the excess harvests of the agricultural revolution and the need to preserve food for future consumption that we developed the social constraints required to control the innate urge to satisfy our immediate needs. Every time this control loosens, we are quickly tempted to revert to the more attractive options rather than the ones most needed.
Because every tycoon is free to invest in any enterprise he fancies and, at the same time, makes decisions based on these very human biases, it is likely that investments will continue to favor more popular areas of scientific progress over others. Whereas some projects, such as cures, inherently appeal to our natural instincts and thus garner our support with ease, others, such as preventive solutions, rely on complicated and fragile scaffolds of metaphorical constructs to make their case. It should have come as no surprise that it took a serious outbreak of Ebola to spur the development of preventive measures and vaccines even though well-founded warnings about the danger of the disease had existed for decades.
One of the first steps in war gaming is to identify the impossible; the same is true in forecasting scientific trends. Though it may be tempting to accept that to err is human, and that we will eventually correct our ways and reduce waste, there is little evidence to suggest that this will happen to any significant extent within our lifetimes. The biases that have slowed our progress thus far are, by their very nature, quite resistant to change or self-reflection. This is not meant to assign fault to human beings, but merely an observation that unless we can somehow trade our current biases for others with fewer adverse effects, we need to be prepared to pay huge overhead costs as we inch our way forward along the path of scientific advancement.
