Posts Tagged ‘philosophy of science’
“The first principle is that you must not fool yourself – and you are the easiest person to fool”*…
We live in a time when a growing number of “authorities” in the U.S. and around the world are actively trading fact for convenient fiction. Science is under attack; there’s (all-too-grounded) concern that we may be headed into a new “Dark Age.”
C. Brandon Ogbunu pushes back, arguing that science– and more particularly, the emerging research field of metascience, a form of scientific self-examination– is essential for navigating our uncertain future…
On May 24, Vice President J.D. Vance authored a post on X that highlighted a “reproducibility crisis” in the sciences. Vance offered this amid a series of other critiques of higher education to justify the withholding of federal science funding to universities over the past several months. His post was timed to accompany a White House executive order that invoked the language of open science to introduce sweeping changes to our federal scientific infrastructure. It came just weeks after the release of plans to cut science funding in the 2026 fiscal year budget.
The playbook is standard: Fuse an aggressive political agenda to a more palatable set of criticisms. In this case, many agree that processes within professional science have, for decades, had significant flaws. In my view, politicians in power are using this as a justification to burn it down. And outside of a few higher-education legal efforts to fight back, the scientific community remains shell-shocked, unable to gather the momentum to resist effectively.
But in addition to resisting the changes, there might be other ways that we can navigate an uncertain future. In recent years, a field called “metascience” (often referred to as “the science of science”) has emerged, charged with understanding the processes of science, how it operates, and identifying themes in what is produced. I argue that this area is going to be essential moving forward in stormy times, as it can dispel the myth that science is an ideological leviathan incapable of self-reflection and can help us rebuild science into a craft that interrogates its fragilities.
As described in a 2018 review, the science of science “is based on a transdisciplinary approach that uses large data sets to study the mechanisms underlying the doing of science—from the choice of a research problem to career trajectories and progress within a field.” It asks questions about aspects of the scientific enterprise, including employment, publishing trends, economic incentives, merit, and other forces that influence science in ways that may escape our intuition…
[Ogbunu explains metascience, and explores examples of work-to-date and questions like: Who is doing science? What are their incentives (and how do they shape behavior)? How innovative is science? He reminds us that “metascientists” are following in the footsteps of humanists and social scientists (Bruno Latour, for example) have examined science practice for many decades…]
… metascience offers a lens that is especially important at this critical moment. Support for science in the face of attacks is critical and necessary. But ironically, one of the best ways to defend the craft might be for scientists to identify the fragilities before the enemy does. We can use data and models, not solely our op-ed voices and social media timelines (though all can be useful). The field is already disabusing us of the notion that science as practiced is based on defensible incentives, neutrality of any kind, or merit, however defined.
Instead, it operates on what looks more like a runaway Matthew Effect, whereby the most established scientists benefit disproportionately from the system of reward — and thus the rich get richer. And the problem isn’t that the flaws exist, but that science’s practitioners aren’t interested in a critical lens towards them.
Metascience won’t fix our problems, but it formalizes ways that we can use to reflect, which may implore us to change science for the better…
Physicians (and other scientists) healing themselves: “Metascience Is More Important Now Than Ever,” from @cbo.bsky.social in @undark.org.
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As we commit to comprehension, we might send insightful birthday greetings to a forefather of metascience, Charles Sanders Peirce; he was born on this date in 1839. A scientist, mathematician, logician, and philosopher, he was (per philosopher Paul Weiss) “the most original and versatile of America’s philosophers and America’s greatest logician”. Bertrand Russell wrote “he was one of the most original minds of the later nineteenth century and certainly the greatest American thinker ever.” He is considered by many to be “the father of pragmatism“; he helped formalize the field of statistics; and his contributions logic were foundational– helping to found semiotics (the study of signs).
For Peirce, logic encompassed much of what is now called epistemology and the philosophy of science. Peirce approached science as a practice, defining the concept of abductive reasoning to explain scientific advance, as well as rigorously formulating mathematical induction and deductive reasoning.
“Brains exist because the distribution of resources necessary for survival and the hazards that threaten survival vary in space and time”*…
And, it seems, they not only evolve, but in ways and with a frequency we’ve only just begun to appreciate. It’s long been noted that evolution seems to have a thing for “carcinization”– crabs have evolved separately at least five times. (Oh, and apparently also for anteaters…) Recent findings hint that evolution might have the same sort of jones for the brain. Amy Maxmen reports…
Our brains, perched atop a network of nerve cells that ascend the length of our bodies, are thought to have arisen once in an animal hundreds of millions of years ago and then evolved over time. However, new findings suggest instead that brains and nervous systems originated multiple times from scratch.
The findings, published today in Nature, highlight an ancient and gelatinous marine predator called a comb jelly [pictured at top]. Unlike pulsating jellyfish, comb jellies swim by “rowing” their many hair-like cilia, which are arranged in rows called combs. They possess rudimentary brains and sophisticated nervous systems replete with elongated cells that communicate through synapses much like our own. Some comb jellies show mirror-like bilateral symmetry, as do we. And like most animals, their muscles derive from a middle tissue layer, which does not exist in jellyfish or sponges, another ancient type of aquatic creature.
So it’s little wonder that biologists have long placed the comb jelly group close to worms, flies, and humans on the evolutionary tree of life; sponges emerge at the base, meaning that this group appeared first. In this traditional view, complex body parts like the brain and muscles arose gradually, and only once, since those parts look similar across related animals, and the chances of that same evolutionary process being repeated seems slim.
But this scenario was shaken by a report in Science last year, which suggested that the comb jelly group emerged before jellyfish and even the brainless, muscle-less sponges, more than 550 million years ago.
Some biologists doubted the rearrangement because it implied two equally uncomfortable possibilities: that the ancestor of all living animals had true muscles and a rudimentary brain, and then sponges and jellyfish lost those parts without a trace; or that the great animal ancestor was simple, and comb jellies evolved separately from all the other animals, yet ended up with rather similar nervous systems, muscles, and bilateral symmetry. When paleontologist Graham Budd heard the news last year, he said, “It is effectively saying animals evolved twice. Frankly, I’m not ready to believe it.”
Without a time machine, it’s impossible to know what our great ancestor looked like. However, today’s report adds more support to the notion that she was simple and comb jellies independently evolved their complex body parts. Leonid Moroz, a neurobiologist at the University of Florida’s Whitney Laboratory for Marine Bioscience, and his colleagues confirm comb jellies’ position below sponges at the base of the evolutionary tree with an analysis of genetic sequences from 11 comb jelly species…
… In an essay for Nautilus called “Evolution, You’re Drunk,” I described how hypotheses entrenched in the notion that evolution leads toward increasing complexity have recently begun to teeter. Now Moroz’s study adds another shove. It seconds the finding that simple sponges, long placed at the base of the evolutionary tree, actually evolved after the sophisticated comb jelly group arose. The story of how complexity evolves is more complex than scientists realized.
Furthermore, the brain—the epitome of complexity—seems to have sprouted up at least twice over evolutionary time. This clashes with the traditional notion that complex, multifaceted features come about in a very specific way, and each emerges just one time. “What everyone has said about complexity is wrong,” Moroz says. “It can happen more than once.”
Finding that comb jellies independently arrived at similar ends as other animals might also have surprised the late paleontologist Stephen Jay Gould, who famously doubted that animals would look the same today if the world were to begin again—if we could replay “the tape of life.”
Is such convergence in design a coincidence? Probably not, guesses Andreas Hejnol, an evolutionary developmental biologist at the Sars International Centre for Marine Molecular Biology in Norway. “If you need a fast communication system, it helps to have extended cells that communicate through chemicals,” he says. In other words, the structure of the nervous system reflects its function. So if intelligent life exists elsewhere in the universe, it’s not too far a stretch to think it could possess a brain comprised of trillions of neurons. Hejnol asks, “How else could it be?”…
The mysterious mechanism of evolution: “Evolution May Be Drunk, But It’s Serious About Making Brains,” from @amymaxmen.bsky.social in @nautil.us.
* John M. Allman, Evolving Brains
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As we contemplate the changing comprehension of cerebra, we might send thoughtful birthday greetings to Sir Karl Raimund Popper; he was born on this date in 1902. One of the greatest philosophers of science of the 20th century, Popper is best known for his rejection of the classical inductivist views on the scientific method, in favor of empirical falsification: a theory in the empirical sciences can never be proven, but it can be falsified, meaning that it can and should be scrutinized by decisive experiments. (Or more simply put, whereas classical inductive approaches considered hypotheses false until proven true, Popper reversed the logic: conclusions drawn from an empirical finding are true until proven false.)
Popper was also a powerful critic of historicism in political thought, and (in books like The Open Society and Its Enemies and The Poverty of Historicism) an enemy of authoritarianism and totalitarianism (in which role he was a mentor to George Soros).
“The advance of genetic engineering makes it quite conceivable that we will begin to design our own evolutionary progress”*…
The obligations of a multi-day meeting (and the travel involved) mean that, from this issue, (R)D will be on pause until February 12 or 13 (depending on how connections play out…)
… and indeed the evolutionary progress of others species. But, Deputy Co-chair of the Nuffield Council on Bioethics Melanie Challenger asks, have we been sufficiently thoughful about the implications of this power?…
In 2016, Klaus Schwab announced that we had entered the Fourth Industrial Revolution. This is the era of the industrialization of biology, the leveraging of technologies to modify biological materials to meet human goals. While the first two Industrial Revolutions exploited energy and materials and the Third exploited digital information, the current revolution is a direct manipulation of life-forms and life’s substances.
The signature invention of this new era is CRISPR, dubbed “genetic scissors.” CRISPR is a ground-breaking method of making precise changes to DNA for a wide range of possible uses from disease reduction and elimination to the eradication of “pest” species and increases in the productivity of farmed animals. CRISPRs (the best-known system being CRISPR-Cas9) originate in RNA-based bacterial defense systems. Naturally occurring in species of bacteria, the Cas9 enzyme cuts the genomes of bacteriophages (viruses that will attack a bacterium), saving a record for defense against future infections. Scientists realized that this immunological strategy could be coopted to innovate a general tool for cutting DNA.
The optimism among those that seek to utilize these tools has been palpable for some time. As noted by the researchers at The Roslin Institute, creators of Dolly the Sheep, the world’s first cloned mammal: “Until recently, we have only been able to dream of…the ability to induce precise insertions or deletions easily and efficiently in the germline of livestock. With the advent of genome editors this is now possible.”
But the technologies of this new industrial era present ethical dilemmas and unknown consequences. What will it take to ensure that this revolution avoids worsening the enormous challenges we already face, especially from biodiversity loss and climate change? How can we get the balance right between the benefits and risks of human inventiveness?
In the 1980s, tech theorist David Collingridge presented his eponymous dilemma for those seeking to control potentially disruptive technologies. First, there is an “information problem” in which significant impacts are often invisible until the technology is already in use. Second, there is a “power problem” in which the technology becomes difficult to shape, regulate or scale back once it has become integrated in our lives. If we are going to navigate the Fourth Industrial Revolution successfully, we need to examine our use of CRISPR through the Collingridge dilemma.
The investors and engineers of the first industrial revolutions in the nineteenth century provide a vivid example of the information problem. They hoped that innovations like the combustion engine would unlock efficiency across multiple human sectors, from transportation to logistics to tourism. Such optimism was not unwarranted. Yet, as Collingridge’s dilemma suggests, it is easier to picture gains than to predict trouble. Building road systems and infrastructure carved capital movements into the landscape, symbolising freedom and the flow of wealth and creativity. Yet the striking visual parallels with our circulatory system did not stimulate anyone to forecast the ninety per cent of people today who are exposed to unsafe pollution levels from traffic or the associated health burdens from heart and lung disease to asthma. Nobody then foresaw the yearly deaths of two billion or so non-human vertebrates on our roads today, or that high traffic areas would cause localised declines in insect abundance of at least a quarter and, in some studies, as much as eighty per cent.
And, of course, most calamitous of all, there is climate change. Traffic emissions account for a fifth of all contributions to global warming. Yet the idea that a profitable and efficient machine like the combustion engine might precede devastating shifts in temperature and weather patterns was scarcely conceivable at the time. Now, it is a near ubiquitous feature of our understanding of the world.
When it comes to the engineering of biology, a similar information problem abounds. Not only is our understanding of biological life incomplete, but we know little about what the industrial processes that we are advancing inside the cells of organisms will do. The changes are both physically and ethically occluded. The ramifications of this and other related biotechnologies are not only rendered uncertain by the inherently complex nature of biological systems but are largely inaccessible to our imaginations.
We must struggle with the radical character of the industrialization of biology. Gene drives (a tool to increase the likelihood of passing on a gene) can weaponize the bodies and reproductive strategies of organisms to bias evolution in a directed way. Artificial chimeric organisms (those composed of cells from more than one species) mix and match biological traits and functions to bring about beings that wouldn’t occur otherwise, transforming autonomous organisms into useful parts for plug and play. But while evolutionary processes will sift those forms and strategies that most benefit future organisms, our acts of creation primarily benefit us alone. Survival of the fittest gives way to the contrivance of the functional.
Yet, despite the disruptive nature of these technologies, CRISPR is already entrenched in our research and economic landscape: here is the power problem of our new technology. The efficiency of modern versions of CRISPR has allowed the technology to pick up users fast. It is now a commonplace tool in labs around the world – with uses amplified during the pandemic – and continues to be utilized in ethically provocative trials, including the cloning of mammal species. CRISPR has been normalised by stealth.
This largely uncontested rollout has been enabled by biases in the evaluation of who is at risk. Put bluntly, humans worry about humans, and take risks to non-humans less seriously. As such, there are vastly different acceptance thresholds for certain kinds of uses and these can be exploited by those that seek to deregulate or profit from the technologies…
… This discrepancy is evident in the anxieties of Jennifer Doudna, one of the Nobel-winning scientists who made the CRISPR breakthrough. In her book, A Crack in Creation, she writes of a dream in which Hitler appears to her with the face of a pig and questions her excitedly about the power she has unleashed. Doudna’s anxieties relate not to the pigs of her dream (who are subject to a wide range of CRISPR applications) but to the potential of eugenics re-emerging in human societies. Her dream reflects not only the inevitability that any technology such as this will be equal parts destruction to rewards, but also that we must confront uncomfortable ideas about what it is to be a creature as much as a creator. Recognizing that these technologies work in the bodies of all biological beings, including humans, is a continual assault on the reasoning behind a hard moral border between us and them.
At present, the lives of non-human animals are the experimental landscape for our technologies. Their powerlessness to protest the uses of their bodies, wombs, physical materials, or futures leaves them vulnerable to being the test sites for a wide range of possible human applications. As a direct consequence of the serviceability of the bodies of organisms, CRISPR has been integrated into our world with little fanfare, directly facilitating the power problem that will, eventually, impact us too. Given Collingridge’s dilemma, what concepts and strategies could help us reduce the risks from CRISPR?
The first thing we need is a new definition of pollution. When it comes to combustion engines and other technologies of the first industrial revolutions, pollution is by far the most consequential harm. Direct impacts include the release of particulate matter or chemical compounds like nitrogen oxides or carbon dioxide into the atmosphere. Pollution from traffic has an immediate impact, especially fifty to one hundred metres from the roadside, with effects that we can measure, such as reduced growth rates or leaf damage in plants, or changes to soil chemistry and nutrient availability. On the other hand, long term effects of emissions, such as global warming, or the sustained impacts of waste on organisms and ecosystems, have proven tricky to anticipate and even harder to hold in mind…
…What is curious about the Fourth Industrial Revolution is that while several branches of science are arming us with the evidence that justifies an expansion of the moral circle to encompass a larger range of organisms, other branches are cranking up the objectification and exploitation of life-forms. As a result, there’s an obvious gap. Without addressing this, most concepts of pollution will remain anthropocentric. This may prove a critical misstep…
A provocative argument that “Gene Editing is Pollution,” from @TheIdeasLetter. Eminently worth reading in full.
See also: “The Ethics and Security Challenge of Gene Editing” and “The great gene editing debate: can it be safe and ethical?“
* Isaac Asimov
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As we ponder permuted progeny, we might send microbiological birthday greetings to Jacques Lucien Monod; he was born on this date in 1910. A biochemist, he shared (with with François Jacob and André Lwoff) the Nobel Prize in Physiology or Medicine in 1965, “for their discoveries concerning genetic control of enzyme and virus synthesis.”
But Monod, who became the director of the Pasteur Institute, also made significant contributions to the philosophy of science– in particular via his 1971 book (based on a series of his lectures) Chance and Necessity, in which he examined the philosophical implications of modern biology. The importance of Monod’s work as a bridge between the chance and necessity of evolution and biochemistry on the one hand, and the human realm of choice and ethics on the other, can be seen in his influence on philosophers, biologists, and computer scientists including Daniel Dennett, Douglas Hofstadter, Marvin Minsky, and Richard Dawkins… and as a context setter for the deliberations suggested above…
“The importance of experimental proof, on the other hand, does not mean that without new experimental data we cannot make advances”*…
Adam Becker explains why demanding that a theory is falsifiable or observable, without any subtlety, will hold science back…
The Viennese physicist Wolfgang Pauli suffered from a guilty conscience. He’d solved one of the knottiest puzzles in nuclear physics, but at a cost. ‘I have done a terrible thing,’ he admitted to a friend in the winter of 1930. ‘I have postulated a particle [the neutrino] that cannot be detected.’
Despite his pantomime of despair, Pauli’s letters reveal that he didn’t really think his new sub-atomic particle would stay unseen. He trusted that experimental equipment would eventually be up to the task of proving him right or wrong, one way or another. Still, he worried he’d strayed too close to transgression. Things that were genuinely unobservable, Pauli believed, were anathema to physics and to science as a whole.
Pauli’s views persist among many scientists today. It’s a basic principle of scientific practice that a new theory shouldn’t invoke the undetectable. Rather, a good explanation should be falsifiable – which means it ought to rely on some hypothetical data that could, in principle, prove the theory wrong. These interlocking standards of falsifiability and observability have proud pedigrees: falsifiability goes back to the mid-20th-century philosopher of science Karl Popper, and observability goes further back than that. Today they’re patrolled by self-appointed guardians, who relish dismissing some of the more fanciful notions in physics, cosmology and quantum mechanics as just so many castles in the sky. The cost of allowing such ideas into science, say the gatekeepers, would be to clear the path for all manner of manifestly unscientific nonsense.
But for a theoretical physicist, designing sky-castles is just part of the job. Spinning new ideas about how the world could be – or in some cases, how the world definitely isn’t – is central to their work. Some structures might be built up with great care over many years and end up with peculiar names such as inflationary multiverse or superstring theory. Others are fabricated and dismissed casually over the course of a single afternoon, found and lost again by a lone adventurer in the troposphere of thought.
That doesn’t mean it’s just freestyle sky-castle architecture out there at the frontier. The goal of scientific theory-building is to understand the nature of the world with increasing accuracy over time. All that creative energy has to hook back onto reality at some point. But turning ingenuity into fact is much more nuanced than simply announcing that all ideas must meet the inflexible standards of falsifiability and observability. These are not measures of the quality of a scientific theory. They might be neat guidelines or heuristics, but as is usually the case with simple answers, they’re also wrong, or at least only half-right.
alsifiability doesn’t work as a blanket restriction in science for the simple reason that there are no genuinely falsifiable scientific theories. I can come up with a theory that makes a prediction that looks falsifiable, but when the data tell me it’s wrong, I can conjure some fresh ideas to plug the hole and save the theory.
The history of science is full of examples of this ex post facto intellectual engineering…
[Becker recount’s The Story of Herschel’s discovery on Uranus, the challenge it posed to Newtonian gravity, and Einstein’s ultimately saving theory; then returns to Pauli and to Bohr’s attempts to use it to refute the principle of conservation of energy; and finally explores the disagreement among, Boltzmann, Maxwell and Clauisus (on the one hand) and Mach (on the other) over atomic theory. He then considers competing theories for similar outcomes (that’s to say, theories that are observationally identical)…]
… the choices we make between observationally identical theories have a big impact upon the practice of science. The American physicist Richard Feynman pointed out that two wildly different theories that have identical observational consequences can still give you different perspectives on problems, and lead you to different answers and different experiments to conduct in order to discover the next theory. So it’s not just the observable content of our scientific theories that matters. We use all of it, the observable and the unobservable, when we do science. Certainly, we are more wary about our belief in the existence of invisible entities, but we don’t deny that the unobservable things exist, or at least that their existence is plausible.
Some of the most interesting scientific work gets done when scientists develop bizarre theories in the face of something new or unexplained. Madcap ideas must find a way of relating to the world – but demanding falsifiability or observability, without any sort of subtlety, will hold science back. It’s impossible to develop successful new theories under such rigid restrictions. As Pauli said when he first came up with the neutrino, despite his own misgivings: ‘Only those who wager can win.’…
We need madcap ideas: “What is good science?,” from @FreelanceAstro in @aeonmag.
Apposite: Charles Sanders Peirce on “abduction”
* Carlo Rivelli, Reality Is Not What It Seems
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As we ponder proof, we might spare a thought for Donald William Kerst; he died on this date in 1993. A physicist, he helped develop the experimental approach (and apparatus) that let Enrico Fermi confirm the existence of Pauli’s neutrino (among many other discoveries).
Kerst specialized in plasma physics, and worked on advanced particle accelerator concepts (accelerator physics). He developed the Betatron (1940), the first device to accelerate electrons (“beta particles”) to speeds high enough to have sufficient momentum to produce nuclear transformations in atoms. It influenced all subsequent particle accelerators.
“The pursuit of science is a grand adventure, driven by curiosity, fueled by passion, and guided by reason”*…
Adam Mastroianni on how science advances (and how it’s held back), with a provocative set of suggestions for how it might be accelerated…
There are two kinds of problems in the world: strong-link problems and weak-link problems.
Weak-link problems are problems where the overall quality depends on how good the worst stuff is. You fix weak-link problems by making the weakest links stronger, or by eliminating them entirely.
Food safety, for example, is a weak-link problem. You don’t want to eat anything that will kill you. That’s why it makes sense for the Food and Drug Administration to inspect processing plants, to set standards, and to ban dangerous foods…
…
Weak-link problems are everywhere. A car engine is a weak-link problem: it doesn’t matter how great your spark plugs are if your transmission is busted. Nuclear proliferation is a weak-link problem: it would be great if, say, France locked up their nukes even tighter, but the real danger is some rogue nation blowing up the world. Putting on too-tight pants is a weak-link problem: they’re gonna split at the seams.
It’s easy to assume that all problems are like this, but they’re not. Some problems are strong-link problems: overall quality depends on how good the best stuff is, and the bad stuff barely matters. Like music, for instance. You listen to the stuff you like the most and ignore the rest. When your favorite band releases a new album, you go “yippee!” When a band you’ve never heard of and wouldn’t like anyway releases a new album, you go…nothing at all, you don’t even know it’s happened. At worst, bad music makes it a little harder for you to find good music, or it annoys you by being played on the radio in the grocery store while you’re trying to buy your beetle-free asparagus…
Strong-link problems are everywhere; they’re just harder to spot. Winning the Olympics is a strong-link problem: all that matters is how good your country’s best athletes are. Friendships are a strong-link problem: you wouldn’t trade your ride-or-dies for better acquaintances. Venture capital is a strong-link problem: it’s fine to invest in a bunch of startups that go bust as long as one of them goes to a billion…
In the long run, the best stuff is basically all that matters, and the bad stuff doesn’t matter at all. The history of science is littered with the skulls of dead theories. No more phlogiston nor phlegm, no more luminiferous ether, no more geocentrism, no more measuring someone’s character by the bumps on their head, no more barnacles magically turning into geese, no more invisible rays shooting out of people’s eyes, no more plum pudding…
Our current scientific beliefs are not a random mix of the dumbest and smartest ideas from all of human history, and that’s because the smarter ideas stuck around while the dumber ones kind of went nowhere, on average—the hallmark of a strong-link problem. That doesn’t mean better ideas win immediately. Worse ideas can soak up resources and waste our time, and frauds can mislead us temporarily. It can take longer than a human lifetime to figure out which ideas are better, and sometimes progress only happens when old scientists die. But when a theory does a better job of explaining the world, it tends to stick around.
(Science being a strong-link problem doesn’t mean that science is currently strong. I think we’re still living in the Dark Ages, just less dark than before.)
Here’s the crazy thing: most people treat science like it’s a weak-link problem.
Peer reviewing publications and grant proposals, for example, is a massive weak-link intervention. We spend ~15,000 collective years of effort every year trying to prevent bad research from being published. We force scientists to spend huge chunks of time filling out grant applications—most of which will be unsuccessful—because we want to make sure we aren’t wasting our money…
…
I think there are two reasons why scientists act like science is a weak-link problem.
The first reason is fear. Competition for academic jobs, grants, and space in prestigious journals is more cutthroat than ever. When a single member of a grant panel, hiring committee, or editorial board can tank your career, you better stick to low-risk ideas. That’s fine when we’re trying to keep beetles out of asparagus, but it’s not fine when we’re trying to discover fundamental truths about the world…
The second reason is status. I’ve talked to a lot of folks since I published The rise and fall of peer review and got a lot of comments, and I’ve realized that when scientists tell me, “We need to prevent bad research from being published!” they often mean, “We need to prevent people from gaining academic status that they don’t deserve!” That is, to them, the problem with bad research isn’t really that it distorts the scientific record. The problem with bad research is that it’s cheating.
I get that. It’s maddening to watch someone get ahead using shady tactics, and it might seem like the solution is to tighten the rules so we catch more of the cheaters. But that’s weak-link thinking. The real solution is to care less about the hierarchy…
Here’s our reward for a generation of weak-link thinking.
The US government spends ~10x more on science today than it did in 1956, adjusted for inflation. We’ve got loads more scientists, and they publish way more papers. And yet science is less disruptive than ever, scientific productivity has been falling for decades, and scientists rate the discoveries of decades ago as worthier than the discoveries of today. (Reminder, if you want to blame this on ideas getting harder to find, I will fight you.)…
…
Whether we realize it or not, we’re always making calls like this. Whenever we demand certificates, credentials, inspections, professionalism, standards, and regulations, we are saying: “this is a weak-link problem; we must prevent the bad!”
Whenever we demand laissez-faire, the cutting of red tape, the letting of a thousand flowers bloom, we are saying: “this is a strong-link problem; we must promote the good!”
When we get this right, we fill the world with good things and rid the world of bad things. When we don’t, we end up stunting science for a generation. Or we end up eating a lot of asparagus beetles…
“Science is a strong-link problem,” from @a_m_mastroianni in @science_seeds.
* James Clerk Maxwell
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As we ponder the process of progress, we might spare a thought for Sir Christopher Wren; he died on this date in 1723. A mathematician and astronomer (who co-founded and later served as president of the Royal Society), he is better remembered as one of the most highly acclaimed English architects in history; he was given responsibility for rebuilding 52 churches in the City of London after the Great Fire in 1666, including what is regarded as his masterpiece, St. Paul’s Cathedral, on Ludgate Hill.
Wren, whose scientific work ranged broadly– e.g., he invented a “weather clock” similar to a modern barometer, new engraving methods, and helped develop a blood transfusion technique– was admired by Isaac Newton, as Newton noted in the Principia.
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