Science: The God That Failed
Science might as well be the god of the modern age. But science has real limits and it is no replacement for the values of the past.
Science might as well be the god of the modern age. This is not an accident, the accomplishments of science are spectacular: putting a man on the moon, worldwide instantaneous communication, computers and possibility of AI, and plentiful food made possible by factory-produced fertilizer. Science has made the modern world possible and has greatly, materially benefited humanity. However, there are aspects of reality that are so complex that no amount of scientific experimentation will explain. Further, science’s empiricism understands the world as it is, it has no capacity to determine how the world should be, this means it can never be a guide for morality. Finally, science is not free from assumptions, it presupposes a worldview grounded in eternal truth.
Science’s power comes from its ability to test theoretic models of reality with empirical data. The data acts as a filter of ideals, only those theories that are not contradicted by the data are kept. What remains is an understanding of how things work that is consistent with what has been observed. Though science does depend on theoretic models regarding what it is studying, say physics of sociology, science is fundamentally driven by data. It is through observations and experimentation that science determines what is and is not true and which models reflect reality.
However, there is an inescapable problem with using data to understand reality and judge what is true. The weakness of science is illustrated by The Turkey Problem, described by Nassim Taleb in his book, The Black Swan. Imagine being a turkey living on a farm. Each day the farmer brings food for the turkeys and the turkeys enjoy roaming around the farm. Life is good for the turkeys and each day passes with them growing happier and larger. This goes on for a year until the farmer has all the turkeys slaughtered for Thanksgiving. The data, the turkeys experience, points towards life on the farm as good, however that is not the truth. In fact, all the evidence the turkeys’ have points in the opposite direction of the truth. A single day upsets everything they knew and trusted.
This problem has another name, “The problem of induction” which points out that science, and anything else driven by data, can never be sure of its findings. The data can only reject what is believed to be true, never confirm it. In those words, a scientist can never be confident that they have all the data, and what they do not know can completely ruin their theory. In fact, this problem has been known for millennia, it was first described by the philosopher Plato in his allegory of the cave. Imagine people who have spent their whole life chained-up inside of a cave unable even to turn their heads to the left or right. All they know is a show of silhouettes being projected on the wall by their captors. Since they have seen nothing else, these captive people may believe that the show of shadows is all there is to reality, after all, that is all the evidence will tell them. However, if one of the captives is able to break free and leave the cave, all their experiences will be shown to be a lie in a single moment.
Nassim Taleb calls those singular, paradigm breaking events “black swans.” In other words, a black swan is a low-probability, high-impact event. This is exactly the opposite of what science needs: repeatable, consistent data without extreme observations. Scientific study shows how things work on average, but black swans dominate the average making it irrelevant. Black swans do not completely invalidate science but they do show its real limitations of science and even human understanding. Black swans mean that the past fundamentally does not predict the future.
Taleb points out that black swans are not an abstract, theoretic problem, in fact, they dominate human history. For example, World War I was started by a lucky assassin and led to the fall of four empires and a subsequent world war. No one in 1914 had any indication of what was coming since Western society appeared to be progressing in unprecedented ways and at a rapid pace. In many ways it was the peak of civilization and yet around the corner was hell on earth. The discovery of penicillin was an accident that fortunately happened before World War 2 and likely saved millions of lives. More recently was 9/11, the 2008 financial crisis, and COVID-19 all which have had huge historic implications and were unforeseen by scientists, experts, and the general public. Even the creation and impact of the Internet was a surprise, as Nobel-prize winning economist Paul Krugman proclaimed that it would have no greater economic impact than the fax machine. In fact, the most important events in human history were primarily black swans, completely unpredictable at the time with radically significant consequences.
Black swans are a product of what are called complex or chaotic systems. The world is full of complex physical, social, and biological systems, for example, the weather, social networks, markets, ecosystems, the human body and the brain. Though these systems are quite different, the complexity in each arises from three types of sources: feedback, thresholds, and interconnectedness.
Feedback is the process of self-reinforcement, sometimes colloquially described as “the rich get richer.” Feedback is found in many different kinds of systems, but one of its most prominent examples in the spreading of viruses. Since disease is spread from person to person, the greater the number of people infected means the greater the number of people who can spread the disease. This means that as the disease spreads, the faster it is able to spread. Feedback creates a compounding effect meaning small differences in the initial spread of the disease massively impacts the final total of people infected. Likewise, “viral” content on social media operates the same way, the more people share something the more people see it and can share it. Similarly, as a book sells, the more people recommend it and sales continue to rise. In this case, the compounding effect is so great that a hundred or so authors make up the majority of total book sales per year despite thousands and thousands of books being published.
Feedback creates the potential for massive, exponential growth. Yet most even the vast majority of diseases, social media posts, or books do not find “viral” success. Small differences in quality, early success, or other properties that are often difficult to quantify create the necessary conditions for runaway growth. The same way you would expect a runner who is 10 percent faster than the next best in the field of competitors to win every race not just 10 percent more. The nature of compounding growth through feedback makes predicting what will be successful difficult at best and impossible at worst.
The second cause of complexity are threshold effects. The turkey problem illustrates a threshold, the changes to the turkeys’ life are not gradual, the change is all at once. Hence the evidence, the data gathered, does not indicate that the situation is bad or even changing. In general, this usually means that data gathered before a threshold is triggered is of limited use. The 2008 financial crisis is a parallel example. Those critics of the growing asset bubble were dismissed as alarmist since the market continued to rise. The system appeared to be stable and many economists made strong arguments for why the market values were sensible. The general public and many experts were ignorant of the dangers up until the collapse. This is reminiscent of the fall of the Soviet Union, the popular consensus was it was going to last for the foreseeable future until it disappeared overnight.
Threshold effects are also found in nature, one example being earthquakes. Pressure on tectonic plates will build over years, decades, centuries or longer until they slip and the pressure is released in a moment causing an earthquake. Determining the magnitude, where, when an earthquake will occur is impossible to predict. Another natural example of threshold effects are neurons. A neuron will receive electro-chemical signals from other neurons but will only fire when the cumulative signal reaches a sufficiently high value. Thresholds are fundamentally a problem for empirical scientific study since they decouple the observation of causes from effects. Even under laboratory controls this can be an issue since a cause can be arbitrarily close to a threshold without triggering an effect.
Finally, the third cause of complex systems is interconnectedness. An interconnected system has a web of relationships between its components and exhibits behaviors that cannot be explained or understood by examining the components on their own. For example, music is interconnected, the individual notes in isolation do little to describe an entire song. In fact, what makes a song a song is the arrangement and relationship between the notes rather than the notes themselves. The difficulty of understanding interconnected systems is that there are potentially a massive number of relationships to be considered. The quantity of all possible relationships between all possible combinations of individual items is roughly exponential. This means as an interconnected system grows, the number of potential relationships grow so fast it is impossible to study them all. Further it is easier to gather data on the individual components rather than the system itself, which gives a bias towards studying the ‘notes’ rather than the ‘song.’ This bias leads to a false impression of understanding the ‘song’ when in fact hardly any progress has been made. Of course some interconnected systems can still be relatively simple, the same way a song can be simplistic and repetitive. They can be fully examined and understood because the number of relationships and patterns are still manageable. However, there are some systems which are metaphorically closer to a creation of Bach, whose music is still being unraveled and analyzed for new patterns and themes hundreds of years after their composition. Highly interconnected systems are common in nature and human society such as the ecosystem of a jungle, geopolitics, or a free market economy. Again, the individual actors are relatively easy to empirically examine, however the relationships and behaviors that emerge from the systems are quite complex.
Another example are computer programs which consist of simple, unambiguous operations such as addition or comparison of two numbers for equality. Yet what emerges from these sequences of simple steps, programs, is highly complex behavior. Sometimes, in fact more often than admitted, the emergent behavior is too complex even for the programmer to understand. The result is unintended consequences i.e. bugs in the programs. This is why development of reliable software is so difficult and expensive.
Perhaps the ultimate example of a massively interconnected system is the human brain. Further the brain is full of feedback and threshold effects. The result is a system so complex it is likely incomprehensible. Even the simplest attempts at mimicking the brain, artificial neural networks are considered “black boxes” that are beyond understanding or explanation or understanding. These networks have a tiny fraction of the trillions of synaptic connections found in the human brain and yet fully explaining their behavior is considered impossible.
Complex systems, those that have feedback, threshold, or interconnectedness fundamentally are problematic for empirical science. Since taking measurements and collecting data is always messy and noisy to some degree, science must be robust to relatively small amounts of error in the data. However, complex systems behavior can vary wildly depending on small changes in conditions. In other words, there is no way to control for small amounts of error and that error can completely change the outcome of an experiment. The result is that scientific empiricism is not in general robust for complex systems.
At some point, large complex, especially interconnected systems defy any explanation. Humans seek explanations that have a handful perhaps a dozen at most causes involved, but what if some phenomena has a thousand or more causes? This is the problem with attempting to understand large markets, ecosystems, brains, or neural networks where each relationship is a potential contributing cause for some phenomena. Even if the individual causes were fully understood, the resulting phenomena would still be a mystery. In fact, the existence of complex systems defy the human desire for manageable, workable descriptions of cause and effect. People have a bias towards what they can comprehend hence scientific study has been focused on what is readily explainable. The initial success of science was fueled by the discovery and master of relatively simple phenomena with a handful of relevant causes and factors. This success led to a confidence in the belief that science’s triumph over the natural world was inevitable, that one day humanity would have a complete understanding of the universe as a result of empirical science. In recent history this confidence has transformed into a matter of faith. It is a faith in the inevitable progress, in the unlimited capacity of human reasoning, and a hope that one day the worst aspects of the human condition can be solved. However this faith is misplaced, the problem of complexity i.e. feedback, thresholds, and interconnectedness mean that data and evidence can be fundamentally unreliable or misleading. This complexity will always mean that single high-impact, low probability events, the black swans, can completely undermine scientific theories previously believed to be trustworthy. Science is a useful tool but an imperfect guide to what is true.
Consider two different theories of consciousness. The first theory is that consciousness is purely a "mechanical" function of the brain and by understanding how the brain operates, consciousness will be explained. If given a set of inputs, the brain will produce a particular output. Though, a human brain is a chaotic network of neurons, it gives rise to consciousness as simply emergent behavior of the network. The second theory is that consciousness is completely unexplainable. Further, let's say the possibility of consciousness residing outside the body is ruled out and it is not possible for the brain to function as an "antenna." The second theory is falsifiable, if evidence was found that identified the mechanism of consciousness, the theory could safely be thrown out. However, it is not clear that the first theory is falsifiable.
Now if the data in the turkey problem is misleading due to a single threshold effect, observations of the complexity of the human brain will be extremely difficult to decipher. The data is likely to be inconclusive and remain inconclusive despite researchers' efforts. When should researchers give up and accept the second theory? Given the complexity of the human brain, it is always justifiable to gather and analyze more data. If the theory of unintelligibility is true, the researchers will never find data to justify the first theory. They will keep gathering more and more data, running more and more experiments. Believing science is the best guide to understanding reality infuses a bias towards the possibility of explanation. If the second theory is true, scientists will be forever searching to justify the first theory. There is effectively no way to differentiate between "can not know" and "do not know yet." This is analogous to what computer sciences call an undecidable problem.
Modern people ask for evidence to support any claim of truth, but this is simply unreasonable. There is truth without proof and mysteries humanity will never unravel due to the sheer complexity of reality. Science will never be able to completely explain the natural world and it is even less equipped to answer metaphysical questions such as where the universe came from or if there is a God. Materialist atheists demand evidence for God’s existence but there are parts of reality that no amount of experiments will explain. They say that historically, religious people worshiped a “god of gaps” because god was the explanation for what they did not know, the stop-gap for their limited understanding of the universe. Thanks to the advancement of science and human progress, that gap is now supposedly shrinking, eliminating any space for God to operate. The problem is that the gap is permanent and contains some of the most fundamental questions about the natural world and the human experience. Some aspects of the universe are so complex that there is no way to determine if what we experience is the effect of tiny perturbations in a chaotic, incomprehensible system or the working of God directly. It is absurd to rule out God as a cause when it is impossible to determine what all the causes are. This positivist view, that every phenomena has an explanation that can be determined by reason or experimentation, is the peak of human arrogance and wishful thinking.
Positivism is the unnamed religion of the modern era. It is practically in the air we breathe, since it is the underpinning for the modern search for truth. Stating that a fact is scientific is usually the trump card in any modern debate, which shows the authority of science in the popular mind. The authority of science and the implicit acceptance of positivism has its roots in the Enlightenment. More recently, its prominence is the result of one influential man’s work, John Dewey. Dewey was an atheist, the father of the American progressive movement, and the founder of the public school system in the United States. He was raised as a Christian and though he lost faith in God, he kept the evangelical fervor for the vision of a better society. In a speech to a Christian organization in the beginning of the 20th century, he laid out his view that the state and science was now able to fulfill Christ’s vision and bring the kingdom of heaven on Earth. This is the view that has persisted until today though never explicitly stated, that humanity’s progress is steady and inevitable, driven by science, and will one day achieve a kind of utopia, the kingdom of heaven but without God. It is the hope and even the faith of the secular humanist, the progressive, the socialist, and even the Marxist. But it is a faith in a god that cannot deliver on its promises.
Science is limited and cannot answer every question about the natural world let alone human society. Data, empiricism, and experimentation all fall victim to a complex world where the past does not necessarily predict the future and single events undo mountains of evidence. However, science has a far more fundamental limitation when it comes to being a guide for individuals and society. The problem is the notion of the good, that is, how things should be. Science is grounded in the present, in what can be observed and measured, it has no capacity to examine ideals and morality which are at least partly subjective. The dichotomy between “what is” and “what should be” is illustrated in the life of Fritz Haber. Haber was a chemist in the early 20th century who won the Nobel prize for inventing a process of extracting nitrogen from the atmosphere to create fertilizer. Most of the food grown today depends on the fertilizer from Haber’s process. It is possible that billions of people would not be alive without it. Haber’s work is a great legacy that shows the positive impact science can have on society. However Haber has a darker legacy as well. He is known as the “father of chemical warfare” for pioneering the use of chlorine gas as a weapon of mass destruction. Further, chemists trained by Haber was went on to develop Cyclone-B, the gas used in the Holocaust to kill millions. Haber’s life and work demonstrate that science itself is amoral, without any inherent ability to judge good from evil. What is good is a philosophical or religious question and cannot be determined by experiment.
Science’s inability to make moral judgments is why it cannot nor should replace human tradition or religion as the primary guide for individuals and society. However this is not science’s most significant limitation. The premise of science is that data and experimentation can be used to determine what is true. Further, science depends on repeatable, consistent observations. This means that not only does science presuppose the existence of truth, it is implied that there is truth that does not change. Whatever science determines to be true must have a reason for existence, and that reason in turn must have its own reason and so on. The conclusion of this chain of causes and explanations must be a cause that is itself without a cause. As Aristotle and Leibniz realized, this cause without cause is the very definition of God. If that chain of causes does not terminate then whatever truth science claims to discover is simply an illusion. Whatever discovery science claims to make, since it exists ultimately for no reason then it can disappear for no reason. That is, if God is not real then science is a lie. Science itself rests upon a classical Western and Christian worldview that presupposes eternal truth outside of time and space. Science cannot explain away God because with God science has no ability to explain.
The complexity of reality means that present observations are not necessarily a reliable guide for the future. This fundamentally limits science’s ability to determine what is true. These limitations undermine the popular faith in the inevitable progress of science and the material advance of society. Regardless, science’s reliance on “what is” renders it incapable of determining “what should be,” that is, science can never be a moral compass. Finally, science is a search for truth and without a reason for everything, God, there is no truth to be discovered. Science does not and cannot have all the answers, and is no substitute for the faith, values, and beliefs handed down by tradition.