The Historically Contingent Origins Of Modern Science

[DocWatts]

Howdy! Thought I might share this write-up I did on the origins of modern science, which delves into how our intuitions about relevance (what is and isn't considered important for a particular problem) inform our problem solving frameworks. This write up is prelude to a deconstruction of scientific realism for the philosophy book I've been writing, 7 Provisional Truths.

Enjoy!

_______________________________________________________________

Horizons Of Relevance

The crux of empiricism's staying power, in both its early and scientific incarnations, stems from its broad applicability to a wide range of practical problems. The key to this versatility? It’s tied to why our problem-solving frameworks are useful to us in the first place. Just as tools empower us to shape raw materials into desired forms, methodologies such as empiricism equip us to steer events towards desired outcomes. Put simply, a methodology is a structured, replicable practice for guiding actions towards an intended purpose. When working as intended, the guidance that these frameworks provide isn’t arrived at by happenstance. It instead follows from successfully pinpointing what’s relevant for a particular problem.

While pinning down what’s pertinent to a given goal may sound straightforward, it can be deceptively complex. Our  lifetime of experience with everyday tasks tends to mask the formidable challenge of discerning relevance in situations where we lack this expertise. The process of determining what's salient - that is, what stands out as important - for a given purpose is known within cognitive science as relevance realization. While it’s yet to become a household term, relevance realization exposes a pivotal aspect of our problem-solving that's easily overlooked in folk-epistemology.

The development of germ theory aptly exemplifies many of these challenges. It shines a spotlight on how our intuitions of salience can be highly misleading, while revealing the ease with which outcome-determinative factors can elude the untrained eye. While it’s become common sense that diseases are transmitted by germs spread through bodily fluids and contaminated material, this wasn’t evident to anyone just a few centuries ago. The existence of microorganisms, not to mention their power to disrupt our bodily processes, isn’t an inference that’s readily drawn from surface-level observation. 

The barrier to connecting these dots can be traced back to the environmental context that our perceptual abilities are adapted to. In essence, our sensory systems are evolutionarily calibrated to an intuitive, human-centric scale. Think of this perceptual baseline as the person-sized ‘factory setting’ to which our experience of both space and time is instinctively attuned. To borrow and extend a term from meteorology, let’s call this anthropocentric frame of reference the mesoscale (from the ancient Greek words for 'middle' and 'size'). So what’s the link between the mesoscale and our intuitions about relevance? The connection is that it’s our perceptual canvas for drawing inferences from our embodied experience.

Though our intuitions of relevance are formed at the mesoscale, this anthropocentric realm is just a tiny slice of Reality. Venturing beyond this familiar domain poses a number of unique challenges, beyond the fact that phenomena become  difficult to observe and manipulate as the scale shifts away from our day-to-day perspective. At extremely small and large scales, everyday phenomena can behave in very counterintuitive ways. Take water, for instance. While its behavior is well accounted for at the mesoscale, from an ant’s point of view water becomes a sticky, globule-like substance with significant surface tension. And from a planetary vantage point, its currents shape the climates of entire continents as it circumnavigates the globe.

Moreover, we often fail to grasp how day-to-day phenomena are intrinsically linked to processes operating at temporal and spatial realms vastly smaller or larger than our habitual frame of reference. Returning to our water example, for most of human history it would have taken a feat of imagination to connect the ocean tides to the invisible pull of the distant moon and sun. That is, until Newton's field guide to universal gravitation upended our cosmic perspective. By the same token, attributing the air that we breathe to the waste products of tiny, invisible creatures in the oceans would have seemed equally far-fetched. Then imagine Leeuwenhoek’s surprise at his chance encounter with microbes from tinkering with glass lenses - and how this discovery would go on to change the world.

The basic takeaway is that our habitual intuitions about relevance are tightly bound to the mesoscale that serves as our stage for daily life. While early empiricism probed the limits of this human-sized backdrop, venturing beyond its comfortable boundaries requires highly specialized techniques. Which brings us to the innovations that the scientific method brought to empiricism - and how its transformation of daily life propelled this methodological toolkit into a bona fide folk-theory of Reality. 

But before we part the veil of scientific realism, it will be instructive to touch upon the historical contingencies that gave birth to modern science. Lest we forget, the scientific method wasn’t an inevitability, and its successes were far from guaranteed. Instead, the achievements that would propel the popular image of science from a specialized mode of inquiry into  a de facto ‘theory of everything’ weren’t preordained. Far from mythological depictions of science as a universal cipher to ‘life, the universe, and everything’, it’s important to keep in mind that the science method was invented - not ‘discovered’. In keeping with our theme that our human perspective within Reality is an essential feature of our problem solving frameworks, the story of science can be traced to a specific time and place that was ripe for an epistemological revolution.

The Historical Foundations Of Modern Science

The iterative toolkit that would become modern science found its initial foothold in 16th and 17th century Europe, amidst a convergence of highly contingent social factors. A Pandora’s Box of socially disruptive forces was busy uprooting European civilization from feudalism, which had taken root in the ruins of the Western Roman Empire. 

The prevailing social order, consisting of subsistence farmers bound in hereditary service to a military aristocracy,  had been devastated by the Black Death - a civilizational apocalypse that wiped out a third of Europe’s population. Carried by flea-infested rats who’d made themselves at home amidst the open-sewers and waste-filled streets of European towns and cities, the fetid conditions of daily life were ripe for this plague to spread its tendrils into every corner of society. Sparing neither cities nor countryside, Europe experienced rapid depopulation over just a handful of years, shattering the demographic foundations that had sustained feudalism for centuries. With laborers now worth their weight in gold, centuries of feudal bondage began to crumble, sowing the seeds of a transformative zeitgeist which would go on to change the world.

From feudalism's ashes, a new social order was coalescing around a form of economic activity that historians would later term mercantilism. Driven by commercial interests and secured by maritime power, cosmopolitan exchange was the lifeblood of this new order, flowing into Europe from the New World. Of course, this early form of globalization bore little resemblance to ‘peaceful exchange’ - it was enforced with brutal systematicity through guns, germs, and steel. 

Alongside these developments, the Protestant Reformation had loosened the Catholic Church’s iron grip over European thought, undermining its ability to suppress knowledge perceived as a threat to its authority. This decentralization of knowledge was accelerated by the printing press, which opened the doors to a dissemination of information on an unprecedented scale.

Ancient Greek empiricism, preserved as an incidental byproduct of European monastic transcription and Islamic scholarship, was finding a new audience amongst an emerging stratum of society eager for practical knowledge. An ascendant entrepreneurial class,  unshackled from centuries of feudal constraints, found its interests increasingly served by empirical proofs over appeals to authority. To that end, military competition amongst rival European powers had created a practical need for what we would now call ‘Research and Development’, entailing a far more rigorous approach for how ideas are tested against reality.

In sum: it would be a mistake to think of the development of science as inevitable. Quite the contrary: it was driven by practical problems which emerged due to a convergence of historical contingencies. The impetus behind the invention of science can be traced to limitations of early empiricism, which was proving inadequate as the problems it was applied to became increasingly complex. The crux of these shortcomings is that pre-scientific empiricism was calibrated to search for patterns of relevance within our person-sized mesoscale. In itself, there’s nothing surprising in this limitation, since the mesoscale is the obvious place to begin probing for clues in lieu of additional information that points elsewhere. 

But lest we paint a misleading picture, let’s make sure to give early-empiricism its due before moving forward; for it was able to accomplish quite a lot within this narrow, person-sized slice of reality. Beyond setting the stage for modern science, its success in probing this everyday domain brought us the principles behind many ideas and technologies that we still rely upon today. Agriculture, mathematics, navigation, and wheeled transport are testaments to this legacy. 

These noteworthy achievements notwithstanding, compared to its later scientific variant, the scope of problems that early empiricism was effective for was reaching a perceptual ceiling. The crux of the matter is that there’s nothing inherently special about the mesoscale, beyond the fact that it’s what our perceptual system and intuitions are calibrated for. And as we’ve seen, what affects us on the mesoscale can have explanations that are invisible to us from this perceptual default. 

And with that, we wrap this lightning tour of the historically contingent origins of modern science. As we’ve seen, empiricism was a notable expansion in our problem-solving repertoire, applicable to a host of day-to-day domains. But it would pale in comparison to the profound shift that occurred as the scientific method emerged. As we’ll see, its unprecedented operational success in transforming virtually every aspect of daily life would inadvertently birth a strange metamorphosis. What began as a more rigorous iteration of empiricism would be gobbled up, bit by bit, by tacit Transcendental assumptions that are outside of what science itself can provide evidence for. In our next section, we'll pull back this veil of scientific realism to reveal the more nuanced relationship between our models and the Reality they approximate.

 

Edited October 30 by DocWatts

[Oeaohoo]

This was very well-written and informative, though I feel that one can get overly wrapped up in explaining everything in terms of historical factors.

In the process, one can also end up reducing history to something mechanical. I love Thomas Carlyle’s essay about this, The Signs of the Times:

Quote

It is no very good symptom either of nations or individuals, that they deal much in vaticination. Happy men are full of the present, for its bounty suffices them; and wise men also, for its duties engage them. Our grand business undoubtedly is, not to see what lies dimly at a distance, but to do what lies clearly at hand.

[…]

Were we required to characterise this age of ours by any single epithet, we should be tempted to call it, not an Heroical, Devotional, Philosophical, or Moral Age, but, above all others, the Mechanical Age. It is the Age of Machinery, in every outward and inward sense of that word; the age which, with its whole undivided might, forwards, teaches and practises the great art of adapting means to ends.

[…]

Speak to any small man of a high, majestic Reformation, of a high majestic Luther; and forthwith he sets about "accounting" for it; how the "circumstances of the time" called for such a character, created, fashioned and floated him quietly along into the result; how, in short, this small man, had he been there, could have per formed the like himself! For it is the "force of circumstances" that does everything; the force of one man can do nothing.

We figure Society as a "Machine”…

Such a mechanical explanation avoids the mysterious and spiritual origin of human creativity, which is the true fountain from which world-historical processes spring. Ironically, it has this in common with the scientific worldview whose development you have explained in such terms…

Edited December 4 by Oeaohoo

[DocWatts]

On 12/4/2024 at 8:21 AM, Oeaohoo said:

This was very well-written and informative, though I feel that one can get overly wrapped up in explaining everything in terms of historical factors.

Thank you!

 

On 12/4/2024 at 8:21 AM, Oeaohoo said:

Such a mechanical explanation avoids the mysterious and spiritual origin of human creativity, which is the true fountain from which world-historical processes spring. Ironically, it has this in common with the scientific worldview whose development you have explained in such terms…

I argue in other parts of my book that human rationality isn't a mechanical or computational process, but instead relies significantly on nonconceptual knowledge (or know-how), metaphor, and imagination.

[DocWatts]

If anyone is curious, here's a follow up section that discusses the methodology of science - particularly how it helps us better discern casual relevance above and beyond ordinarily observational reasoning (as a prelude to an in-depth exploration the limitations of these methods, later in the book).
 

The Pattern Recognition Trap

If early empiricism handed us a box of rough-hewn tools for tackling everyday tasks, science would offer us precision instruments for unlocking realms far beyond our natural reach. When skillfully wielded, straightforward observational reasoning can provide serviceable diagnostics for zeroing in on desired outcomes - provided that problem-relevant features are in plain sight. Which brings us to the key caveat of this approach: its operating domain is constrained to what we can observe and manipulate from our person-sized mesoscale. As we’ve seen, what affects us on the mesoscale need not have its origins there - hardly surprising when we recall that this familiar perceptual domain is but a tiny slice of reality. 

This widening gap between straightforward observational reasoning and concrete, material demands drove sharpening tension between theorists and practitioners. Practical applicability is where the rubber meets the road for explanatory theories, and in this regard empiricism was straining against its methodological limitations. The ‘gotcha’ of this approach? Empiricism was becoming a victim of its own success, increasingly thrown at problems whose causal chains lay far beyond its operational constraints. 

In an era where discerning nature’s hidden patterns was rapidly translating into tangible material spoils, empiricism was due for an update if it was to meet escalating practical demands. The core issue confronting its practitioners was an inferential bottleneck, stemming from a tricky framing problem. As the situations that empiricism was thrown at became more complex, it was struggling to suss out reliable links between cause and effect.

At first glance, establishing cause and effect seems straightforward enough. Drop a glass, and it shatters. Heat up water, and it boils. As problems increase in complexity, however, it can become deceptively hard. It’s one thing to notice a disconcerting rattling from your car’s engine compartment when you press down on the accelerator. It’s quite another to figure out that the rattling isn’t coming from the engine at all, but from a worn-out joint in your vehicle’s drivetrain that only shows symptoms when accelerating from a stop.

The key to this deceptive complexity lies in how causal patterns can be invisible to us without the proper investigative tools. Where our built-in pattern recognition comes pre-calibrated for everyday problem solving, scientific methods are more like precision instruments that demand training and expertise. Case in point, our default observational reasoning naturally gravitates to the readily obvious - the engine must be the problem since that's where the sound is coming from. Yet this reflex can lead us astray when visible symptoms stem from causes that aren't immediately apparent. In such cases, we may end up grasping at patterns that are intuitive but misleading.

While human psychology is hard-wired for pattern recognition, most of the patterns we spot are mere coincidental associations rather than genuine causal relationships. Untangling causal threads from this expansive web of spurious associations can be a daunting task, even for experienced investigators. So how do we slice through this tangle of false leads? By a ruthless pursuit of relevance. Without being able to identify what’s relevant for a particular problem, we’re left pulling at loose threads that don’t weave into a coherent tapestry. Our technique for separating these strands rests on a fundamental principle: correlation does not imply causation - meaning that you can’t assume that one event causes another just because they occur together. 

An oft cited example is how ice cream sales and shark attacks both increase during the summer - yet it would be foolish to conclude that soft serve in a waffle cone sends sharks into a feeding frenzy. Beyond this deliberately facetious example, the real world is rife with cases where the inability to separate correlation from causation has led to deadly consequences. Prior to the scientific principles that gave rise to germ theory, doctors were more akin to respected quacks whose remedies could be worse than the ailments they were attempting to treat. A common idea from pre-scientific medicine was that diseases were caused by bad blood, leading to ‘remedies’ like bloodletting - literally draining a sick person of their blood. While with the benefit of hindsight this seems like an insane practice, from the purview of its practitioners it made a certain kind of sense. Patients would often recover in spite of their prescribed ‘treatment’, creating a powerful illusion that they got better because of it. 

In our own era, we can note how racists cite crime statistics to draw sweeping conclusions about out-groups, while ignoring how systemic poverty and forced inequality are the root causes of crime. Or how vaccine skeptics point to rising autism diagnoses alongside vaccination rates, while ignoring how improved awareness and diagnostic criteria explain why this correlation is illusory.

The examples above highlight the ease with which our habitual  pattern recognition instincts can lead us astray. The lesson to be drawn is that short of solid diagnostic tools for identifying relevance, it’s all too easy to conflate cause and coincidence. What the scientific method brings to the table is a hard-won field manual for addressing this perennial blind spot. It employs methodologically rigorous techniques to develop iterative, falsifiable models of our Reality - or those aspects of it that we can measure and test. These models are how science distinguishes true cause-and-effect relationships from coincidental patterns. 

To that end, scientific practice begins with transforming hunches into precise, measurable predictions known as hypotheses. While hypothesis formation draws upon a rich blend of observation, expertise, imagination, and informed speculation, these intuitions must be translated into concrete, testable claims. While we might start from an informed intuition that 'phones are bad for young children’, if we wanted to verify that scientifically, we might propose a study that measures changes to sustained attention span as a result of prolonged screen use. But measurable predictions alone aren't enough - we need rigorous methods to test them. 

The scientific method employs several key strategies:

• Controlled Experimentation where we change just one thing at a time to see what actually makes a difference for a given outcome. If we’re trying to figure out what affects a cake’s texture, we might adjust only the amount of sugar while keeping all other ingredients exactly the same.
   
• Mechanistic Investigation which maps out how exactly A leads to B. Beyond just observing that too much alcohol makes us sick, we want to trace out what happens as it moves from our gut to our bloodstream to our brain.
   
• Falsification Testing where a community of scientists works to prove each other’s hunches wrong, whether by design or accident. If the prevailing scientific theory is that light needs a special medium to move through (the so-called ‘luminiferous ether’), we might inadvertently falsify it while trying to measure whether the Earth's motion through this invisible field affects the speed of light.
   
• Natural Experiments where we study real-world situations that act like controlled experiments, when direct manipulation isn’t possible or ethical. Like comparing identical twins raised in different environments to understand how genes shape our personality.
   
• Statistical Analysis where we measure how strongly two phenomena track together, to separate spurious coincidences from patterns that are worth investigating further. If we notice that crime is reported at a higher rate during full moons, we'd want to track this over thousands of cases to see if the pattern holds up.
   
• Replicability where different people try performing the same experiment, to see if the results are consistent or a one-off. If a single study claims to show how quickly ordinary people become cruel when given authority, we’d want other researchers to verify this under different circumstances.

These methodological tools have proven remarkably successful in helping us zero in on relevance with a degree of precision undreamt of by our ancestors. The extension of our observational reach beyond our person-sized mesoscale has proven indispensable for sifting genuine causes from a vast ocean of spurious correlations. This investigative power has been enhanced by generations of painstaking methodological refinements, allowing science to peer into realms far beyond our ordinary observational reasoning. World changing discoveries from electromagnetism to germ theory to genetics would have been impossible through pre-scientific observational reasoning alone. And once these forces were unleashed, there would be no going back.

Edited December 7 by DocWatts

[DocWatts]

Follow up section on the disruptive power of the scientific method, which touches upon the ease with which its successes tempt us into mistaking the map for the territory, while hinting at how science can play a constructive role in our search for meaning.

Enjoy!
 

No Going Back - Science Unleashed

Like opening Pandora’s Box, scientific understanding unleashed a tidal wave of disruptive forces that would go on to reshape our material reality. While propelling us to new heights of prosperity and potential, these advancements left a cascade of mounting externalities in their wake. The stakes of these mounting costs? Catastrophe, if humanity continues to kick the can down the road until it's too late to change course.

If scientific advancement has courted unforeseen existential risks, then why pursue it? Because its gifts have been an existential boon for mankind. The cumulative scope of scientific progress goes far beyond the laboratory - of the 8 billion people in the world today, as much as 75% wouldn't be alive without the agricultural and medical advances that modern science has unlocked. In four centuries the average global life expectancy has more than doubled - ballooning from an estimated 30-35 to 73 - owing to sharp decreases in infant mortality and drastic improvements in disease prevention and treatment. A host of deadly, debilitating illnesses that have been the scourge of mankind since the dawn of civilization - from smallpox to polio to malaria - have been eradicated or significantly controlled thanks to scientific medicine. The global literacy rate skyrocketed from a paltry 10% to a remarkable 86% in the last four centuries, thanks to a confluence of scientific advancements in printing technology, public health, and education. 

In sum: the cumulative impact of these developments extends far beyond any single breakthrough or invention. Contrary to its bastardized image as a technological Santa Claus, peddled by commercial interests who would reduce it to mere gadget-making, science has been the catalyst for an unprecedented transformation of daily life on a global scale. As the Grinch discovered, perhaps science means just a little bit more.

With such profound achievements, it's a legacy that advocates for science proudly defend - and rightly so. This is especially important at a time when science is under sustained assault by bad actors with a vested interest in undermining its hard-won credibility. At the same time, the mere existence of bad-faith criticism shouldn't prevent nuanced examinations of science's strengths and limitations. Allowing these politically motivated attacks to create a chilling effect where all critical examination of science is viewed with suspicion is to cede the narrative to those who don’t deserve a seat at the table.

The takeaway? Critical examination - when mindful and informed - serves a vital purpose. Far from undermining science, this honest self-reflection is necessary if it’s to play a constructive role in humanity’s search for meaning. While it might be objected that science can't tell us what to value - as Hume observed, we can't derive an 'ought' from an 'is' - its ability to ground our ideas in verifiable realities makes it an essential voice in these discussions. This grounding role points to how we should understand science - not as a mere accumulation of facts and theories that mirror a fixed, perspectiveless Reality, but as an activity that must be interpreted and integrated with other forms of human understanding. At its core, the story of science is the story of humanity.

This human story, however, isn't a simple tale of progress. From the American Revolution to works of fiction such as Star Wars, the folk-image of revolutions often leans towards romantic oversimplification. In the real world however, revolutions tend to be messy, complicated affairs with mixed outcomes - and scientific revolutions are no exception to this. Case in point: the same principles that electrified our cities can also be used to burn them to the ground. The advancements which brought us our global, interconnected society have also given autocrats the ability to consolidate power and resources on a scale that would have been envied by ancient God-emperors. The very technologies that turbocharged our productive capacity also enable pervasive surveillance, hyper-targeted propaganda that exploits precise psychological vulnerabilities, and an industrial-scale dehumanization of labor - creating new levers of power far beyond what was possible in previous eras. In other words, science has given institutions more effective tools to monitor, influence, and control - not just variables in an experiment, but living, thinking human beings as well.

An entrenched elite, whose short-term interests are at odds with our civilization’s long-term survival, have channeled considerable resources into stonewalling efforts to address mounting technological externalities.  Centuries of fossil fuel dependence have created a crushing ecological debt that will be paid one way or another - either through selective changes to how we organize our society, or through a cataclysmic change that’s forced on us. Meanwhile, the advent of artificial intelligence threatens to amplify these power imbalances even further - giving those with a vested interest in resisting change more powerful tools to maintain their grip on our collective future.

So how do we respond to these challenges? While it might be tempting to retreat to the comforting myth of a romanticized past, the stark reality is that science has become too deeply woven into the fabric of modern life to simply abandon. In spite of protracted hostility from vested interests who are working to undermine its credibility, science remains the voice of authority in our culture - evidenced by how even propaganda that denies climate change comes cloaked in faux-scientific arguments. Whether we're grappling with the Meaning Crisis or confronting existential threats, we must do so in dialogue with science's authority and credibility.  Since the genie can’t be put back in the bottle, the only path forward lies in understanding both the power and limitations of scientific thinking.

Given its unparalleled success in demystifying problems that have bedeviled our best and brightest for thousands of years, it can be quite tempting to see science as a literal mirror of Reality. As if the scientific method is the key to a universal cipher with ready-made answers for all aspects of our existence. This presupposes that Reality itself is a problem to be solved - which leaves aside the possibility that existence is also a mystery to be experienced. But this isn't a lament that science is ‘unweaving the rainbow’ by demystifying natural phenomena. Quite the contrary - we’d be fools to throw away the explanatory power of science out of nostalgic longing for a simpler world. With this in mind, there’s little doubt that science has a central role to play in our search for meaning - its importance in keeping our ideas grounded in verifiable realities is beyond doubt. Instead, the more interesting question is whether there are important aspects of ourselves that can’t be fully captured by its models. If so, how do we synthesize scientific models with other methodologies that deal more directly with our lived perspective within Reality - and what might that dialogue look like?

As we approach the finish line for this chapter, our target becomes clear: the perceived significance of our scientific models. Even as these models allow us to develop increasingly precise approximations of Reality, we should be extraordinarily careful about confusing the model for the manifestation. Without epistemological rigor it becomes all too easy to confuse our abstractions about Reality for Reality itself. In our conclusion, we’ll see how the extraordinary success of science has perpetuated a type of Scientific Realism, which makes sweeping metaphysical claims that go well beyond what these methods can actually justify.