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一般方法論 -- 開欄文
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瀏覽4,801 ｜回應21｜推薦2 |
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子曰：「工欲善其事，必先利其器」(《論語•衛靈公10》)；「器」在這裏指的大概是工具。但是，要把一件事「做對」，「工具」之外，還得講究「方法」。「雖不中不遠矣」 (《大學章句10》)，講的應該是「態度」；這個道理用在「方法」上也可以說得通。這是「科學基礎論」中「科學方法」研究的對象。
大概在初中時，家父給了我一本討論「科學方法」的書；書名已經忘了。這是我第一次接觸到這個主題；自然印象深刻。後來成長過程中，我對它一直非常注意。「邏輯學」之外，我還讀過笛卡爾和波普的書。本部落格過去登過不少這方面的評論。
循此處各《開欄文》之意，另立此欄。
本文於 修改第 4 次
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「愚蠢」的5個基本屬性--Tibi Puiu
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讀了下文，我覺得自己給川普取了「川蠢」這個外號還真不是蓋的(該欄2026/04/11)。川普除了學到東方政客毫無底線的貪婪心態和刮地皮伎倆外(馬可仕、陳阿騙、…)，其特長還包括各種「損人不利己」的奇招。看了下文與本欄2026/07/26和2026/07/12兩篇貼文，就了解何以我為自己神來之筆沾沾自喜。
The 5 Universal Laws of Human Stupidity Explained
Stupidity is not low intelligence. It is damage without gain to anyone involved.
Tibi Puiu, Edited and reviewed by Zoe Gordon, 07/23/26
The most dangerous person in the room may not be the greediest, cruelest, or mentally wackiest. It’s the stupid people you always have to watch out for.
A selfish person usually wants something. Find the incentive, and you can often predict the behavior. The more dangerous person harms others without helping themselves — and may even suffer alongside their victims.
That’s one of the hallmarks of stupidity, as outlined in The Basic Laws of Human Stupidity, a satirical essay written in 1976 by economic historian Carlo M. Cipolla. He did not define stupidity through IQ, low education, or ignorance. He defined it through outcomes: Who gained, who lost, and whether the damage served any purpose.
Cipolla did not present these as literal scientific “laws”. He used satire and a simple payoff model to make a serious point: intelligence, education, and status do not prevent people from making decisions that harm both themselves and others. In his framework, stupidity is not a fixed trait. It is a pattern of behavior defined by its consequences.
Law 1: We Always Underestimate How Much Stupidity Exists
“Always and inevitably everyone underestimates the number of stupid individuals in circulation.” — Carlo M. Cipolla.
No matter how many stupid people you think you will encounter, Cipolla argues, reality keeps exceeding the estimate. People you once considered rational suddenly act in ways that harm everyone involved, while others appear at exactly the wrong moment to waste time, money, or effort for no apparent gain.
The unpredictability of foolish actions makes stupidity seem rarer than it is. We count the obvious cases but overlook how often sensible people behave stupidly under particular circumstances. Cipolla therefore refuses to assign stupidity a precise share of the population: any number, he jokes, would immediately prove too low.
Law 2: Stupidity Ignores Status, Education, and Intelligence
“The probability that a certain person be stupid is independent of any other characteristic of that person.” — Carlo M. Cipolla
Cipolla claimed every social group contains people who act stupidly. And they could be anyone. He offered no evidence for that statistical claim. But his larger point stands: credentials do not guarantee sound judgment.
This resembles the Dunning-Kruger effect. In their 1999 study, Justin Kruger and David Dunning found that poor performers in tasks often overestimated their performance. The more we lack a skill, the harder it is to recognize that deficiency.
Cipolla went further. Even a capable person can act stupidly when anger, fatigue, ideology, distraction, or overconfidence overwhelms judgment.
Law 3: Stupidity Creates Losses Without Producing a Winner
“A stupid person is a person who causes losses to another person or to a group of persons while himself deriving no gain and even possibly incurring losses.” — Carlo M. Cipolla
By creating a graph of Cipolla’s two factors, we obtain four groups of people.
Helpless people contribute to society but are taken advantage of by it;
Intelligent people contribute to society and leverage their contributions into personal benefits;
Stupid people are counterproductive to both their and others’ interests;
Bandits pursue their own self-interest even when this poses a net detriment to societal welfare.
An additional category of ineffectual people either exists in its own right or can be considered to be in the center of the graph. Credit: Wikimedia Commons. 行為結果分類(四象限)座標圖
Cipolla called this the Golden Law of Stupidity. He imagined behavior on a chart measuring gains and losses for the actor and everyone else.
An intelligent action benefits both sides. A bandit benefits while others lose. A helpless person loses while someone else gains. A stupid action leaves both sides worse off.
Someone ruins a partnership to win an argument or damages a service they also use. Nobody emerges better off.
It’s a bit like game theory, only it judges actions after their consequences rather than predicting rational strategies.
Cipolla offers a useful diagnostic question: Who benefited? When the honest answer is “nobody,” the search for a clever hidden motive may be misguided. Things are simple in this case: this is stupidity in action.
Law 4: Reasonable People Underestimate Unreasonable Damage
“Non-stupid people always underestimate the damaging power of stupid individuals.” — Carlo M. Cipolla
A bandit is dangerous but predictable. The bandit seeks money, power, status, or protection. Once you understand the incentive structure, you can negotiate, deter, or defend yourself.
Stupidity removes any logic. You cannot reliably bargain with someone who misreads both gains and losses.
“A stupid creature will harass you for no reason, for no advantage, without any plan or scheme,” Cipolla writes. Because the action produces no clear benefit for its author, it can arrive at an improbable time and take an unpredictable form. Rational people are therefore caught by surprise. Even after they recognize the threat, they struggle to respond because the attack itself has no rational structure.
This is the real point of Cipolla’s fourth law. Intelligent people and bandits often see stupidity as harmless incompetence. They become complacent, assume the person will mainly hurt themselves, or believe they can recruit and manipulate them. Cipolla warns that this usually enlarges the damage. Giving an unpredictable person more access, authority, or room to act also gives their mistakes a wider reach. The would-be manipulator may control them briefly, but cannot anticipate every reaction.
In Cipolla’s framework, stupidity has little to do with IQ (low IQ people are just “dumb”, not “stupid”). What matters when judging stupidity is the result: an action that costs other people time, money, energy, or well-being while bringing its author no corresponding gain.
Law 5: Stupidity Can Be More Dangerous Than Anything
“A stupid person is the most dangerous type of person.” — Carlo M. Cipolla
Cipolla added a corollary: “A stupid person is more dangerous than a bandit.”
Deliberate exploitation or pure evil may seem worse, but Cipolla operates by measuring total damage. A “perfect bandit” takes something from another person and keeps it. One side loses exactly what the other gains. The act may be unjust, but from Cipolla’s cold economic perspective, society’s total stock of wealth or welfare has merely changed hands.
Stupidity produces a worse result. The stupid person causes a loss without securing an equal gain — and may suffer too. Nothing offsets the damage. Each such act leaves the group, institution, or society poorer than before.
Cipolla then scales the model up from individuals to entire countries. He claims that rising and declining societies contain the same underlying proportion of stupid people. The difference lies in how everyone else behaves.
In a society moving upward, enough intelligent people create gains for themselves and others. They limit the reach of destructive actors and produce enough value to outweigh their losses. In a society moving downward, stupid people gain more freedom and power to act.
At the same time, Cipolla sees more “bandits with overtones of stupidity” among those in charge — people whose personal gains are small compared with the damage they inflict — and more helpless people among everyone else.
An ordinary person may waste a colleague’s afternoon. A general, bureaucrat, executive, or head of state can apply the same irrational pattern to millions of lives.
Cipolla’s fifth law is therefore not simply an insult directed at foolish individuals. It is a warning about systems that give destructive, unpredictable behavior greater reach while weakening the people capable of counteracting it. Don’t vote for stupid people.
Nobody is permanently intelligent, helpless, selfish, or stupid. We move between Cipolla’s quadrants from one decision to the next. In other words, we’re all stupid sometimes, and that’s sort of fine as long as you offset things by acting reasonably most of the time.
The first defense against stupidity is not identifying the idiots around us. It is asking before we act: What happens if I am wrong, and who else will pay for it?
Tibi Puiu is a science journalist and co-founder of ZME Science. He writes mainly about emerging tech, physics, climate, and space. In his spare time, Tibi likes to make weird music on his computer and groom felines. He has a B.Sc in mechanical engineering and an M.Sc in renewable energy systems.
We recommend
* On Stupidity, de Beistegui, Miguel, University of Chicago Press, 2022
* On the Politics and Sociology of Stupidity in Our Society, Lewis Anthony Dexter, Social Problems, 1962
* Stupidity, Madness, and Malevolence: Schelling, Deleuze, Flaubert, and Musil and the Problem of Violence, Jason M. Wirth, Oxford Academic Books, 2015
* Foolishness, Stupidity, and Cognitive Values, Kevin Mulligan, The Monist, 2014
* Self-Knowledge and Self-Regulation: An Economic Approach, Roland Benabou, MIT Press, 2003
* Brutal Thoughts: Laruelle and Deleuze on Human Animal Stupidity, Ó Maoilearca, Edinburgh University Press, 2017
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「愚蠢」的性質和成分--Ross Pomeroy
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** 本文原於2025/10/08在「自我提升篇」一欄刊出；現移至本欄。造成不便，請見諒。另請參看本欄2026/07/12貼文。
自以為是 = 愚蠢
不合邏輯 = 愚蠢 X 2
自以為是 + 不合邏輯 = 愚蠢 X 愚蠢
你有這兩種「成分」嗎？如果有，多嚴重？
What Is Stupidity?
Ross Pomeroy, 03/02/19
What is stupidity? Surprisingly enough, it's a question few scientists have grappled with, perhaps out of a desire not to wade into a subject that could so easily offend. After all, the field of intelligence studies is rife with controversy. Still, some have tendered their thoughts.
Evolutionary biologist David Krakauer, President of the Santa Fe Institute, told Nautilus, “Stupidity is using a rule where adding more data doesn’t improve your chances of getting [a problem] right. In fact, it makes it more likely you’ll get it wrong.”
Carlo M. Cipolla, a professor of economic history at the University of California - Berkeley, argued that stupidity is characterized by causing losses to another person or group whilst deriving no gain and even possibly incurring losses yourself.
In one of the few direct empirical studies on stupidity, researchers Balazs Aczel, Bence Palfi, and Zoltan Kekecs distilled a few traits that drive stupidity: overconfidence, ignorance, absentmindedness, impracticality, and an inability to control one's own actions.
Notice that none of these descriptions of stupidity simply refers to it as an absence of knowledge. Lacking information about a topic does not make one stupid, as one can always educate oneself. Rather, stupidity is more of a choice. If someone chooses to act without taking full measure of the available evidence, that is stupidity.
Since humans take countless actions that scythe across disciplines and scenarios, anyone – educated or not, wealthy or poor, politician or voter – can be stupid at one time or another. Although, it must be said, some tend to be stupid more often than others.
One area of research where we perhaps can see stupidity on paper is the Dunning-Kruger effect. As many studies have revealed, it seems surprisingly (and unfortunately) universal that people who lack correct information about a certain issue tend to think they are actually informed about it. Often, they even overestimate their knowledge by such a degree that they are more confident than people who actually know the correct information. These people, the ones who know little but profess to know a lot, can be said to be truly stupid.
Can stupidity be avoided or is it hard-wired? Perhaps writing tongue-in-cheek, Cipolla expressed the opinion that stupidity is genetically predetermined, an "indiscriminate privilege of all human groups... uniformly distributed according to a constant proportion."
I'll take the opposite stance. I believe that education can root out stupidity like a garden weed. The answer is not to merely teach facts, as is still all too common, but to teach people how to attain facts and how to discern a good source of information from a bad one. One must also learn to nurture a healthy degree of self-doubt. Essentially, the antidote to stupidity is a scientific way of thinking.
Related Topics:
intelligence, social science, psychology, stupidity
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愚蠢、愚蠢人、和愚蠢行為 -- David C. Krakauer
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下文放在「人工智慧縱橫談」一欄也適合。放在這裏是因為：用了「錯誤」的「方法」，或把「方法」用在「錯誤」的場合，都會造成「愚蠢」的結果。
What Makes Humans Stupid
It takes intelligence to get things spectacularly wrong. An essay on our undoing.
David C. Krakauer, 07/08/26
I have never heard a rock described as stupid. And the same would be true of a river, a hurricane, and even a thermostat. Stupidity seems to be a sophisticated form of behavior despite its ignominious associations.
Human beings can land autonomous rovers on Mars, sequence a genome in hours, and engineer nanometer circuits. And yet conspiracy theories, anti-scientific political movements, and institutional hatred proliferate on a scale that might embarrass the meager success record of a medieval alchemist.
One might say that stupidity implies a capacity for getting things right before it can get them spectacularly wrong. Stupidity is not the opposite of intelligence but its evil twin, the dissimulating Cain to a cerebral Abel.
And perhaps surprisingly, the degree of stupidity available to any system scales directly with the intelligence that system possesses—more intelligence begets greater feats of stupidity. It would be a stretch to call a bacterium stupid, and we know that cats and dogs achieve modest feats of it. But human beings, equipped with language, abstraction, technology, institutions, and ideology, can be stupid on a truly civilizational scale. This is not a joke; it is close to a law of nature. A law that might very well be our undoing.
We have thousands of research programs on intelligence, and not all of them are intelligent, including studies of IQ, AI, animal cognition, and collective problem-solving. These take place in celebrated departments and are published in prestigious journals devoted to understanding how minds make hard problems easy.
These days we cannot take a step without crashing into another article on intelligence and AI. Researchers from all fields without any knowledge of intelligence research and its history have become self-declared “thought leaders” in natural and artificial intelligence.
Yet stupidity gets almost no attention at all. It is treated as a mere absence, as if once we subtract intelligence what remains is stupidity. It is likened to a form of psychological darkness experienced after you have switched off the lights of deliberation. But this is a mistaken belief.
Darkness does not do anything pernicious in the way that stupidity does. Stupidity takes an easy problem and, with great effort and misdirected ingenuity, makes it hard. That effort is the key to grasping stupidity, in that you need sophisticated machinery to be genuinely, consequentially stupid.
Here is how I like to think about this from a scientific perspective. If intelligence means making a problem of difficulty X easier, by deploying tools, using mathematics, and adopting strategies that reduce its cost, then stupidity means making a problem of difficulty X harder.
And contrary to expectations, the most reliable way to make an easy problem hard is to bring to bear an impressive apparatus of complicated theories, elaborate beliefs, and sophisticated algorithms that sound tremendously convincing but perform worse than doing nothing.
A person who does not know the answer to a question is merely ignorant. A person who constructs an ingenious hundred-page argument for the wrong answer is stupid. As great writers, artists, and philosophers throughout time have understood, such constructions require intelligence of a high order.
Consider this analogy. When a meteor collides with the Earth, there is no sense in which it is doing anything wrong. It is obeying classical mechanics, and that is all there is to say about it. But when a bird flies into a glass window, something different has happened, something we can with justification call an error.
Life, uniquely among physical systems, has the capacity to be wrong. Stupidity is a very special species of failure and not all errors qualify. People err from ignorance (insufficient data), from noise (a signal is distorted), or from the honest misapplication of a rule to a novel situation and changing context. These are the misfortunes of our lives about which we should be forgiving.
Stupidity begins where error is elaborated, defended, refined, institutionalized, and made the foundation for further action. Stupidity makes everything progressively worse. And it is this elaboration of erroneous belief and behavior that demands, paradoxically, the prior existence of intelligence.
Some of the most impressive feats of stupidity consist in applying a perfectly good theory to the wrong problem.
Some would argue that quantum mechanics is one of the most “intelligent” inventions of physics, one that provides a framework of extraordinary precision for describing the behavior of subatomic particles, despite its truly counter-intuitive requirements.
But there is a small cottage industry of thinkers who have tried to apply quantum mechanics to human consciousness, decision-making, and psychology to explain why people are indecisive and why they eventually make up their minds. There is nothing wrong with the mathematics and the physics is sound. But the application is peculiar.
What was an elegant and parsimonious description of photons and electrons becomes, when imported into psychology, an absurdly over-complicated way of saying that people sometimes change their minds. A phenomenon that a novelist could illuminate in a paragraph has been buried under a formalism designed for a completely different scale of reality.
The theory did not become less intelligent, it was asked to solve a problem it was never designed for, and in the process, it made that problem harder to understand, not easier. This flies in the face of the purpose of science, which is, as the physicist and philosopher Ernst Mach described it, “the completest possible presentment of facts with the least possible expenditure of thought.”
The early cybernetics movement offers a similar cautionary tale. Norbert Wiener and his colleagues at MIT developed a powerful framework for understanding feedback, control, and communication in machines and organisms. The mathematics was original, the engineering applications impressive, and the cybernetic enterprise even provided the groundwork for the development of complexity science.
Then the management consultant Stafford Beer and others tried to apply cybernetic control theory to the management of entire national economies, most famously in Salvador Allende’s Chile, where Project Cybersyn attempted to run the Chilean economy through a network of telex machines. The idea was to model the economy as a dynamical system with inputs and outputs, and to use real-time feedback to optimize production and distribution.
Unfortunately, an economy is not a servomechanism. The feedback loops in a national economy involve millions of adaptive agents with private information, conflicting goals, and a tendency to evolve their preferences. The seduction of applying cybernetic methods to complex systems is described beautifully by my Santa Fe Institute colleague, the writer Francis Spufford, in his novel Red Plenty:
“The world was lifting itself up out of darkness and beginning to shine, and mathematics was how he could help. It was his contribution. It was what he could give, according to his abilities. He was lucky enough to live in the only country on the planet where human beings had seized the power to shape events according to reason.”
A simple behavioral intervention, such as adjusting a price, changing a regulation, or God forbid, simply asking people what they need, could often have achieved in an afternoon what the cybernetic apparatus was struggling to model in weeks.
The intelligence of the cybernetics as a framework for control of simple systems is unquestionable, but its application to the economy inflated the difficulty of the problem it was meant to solve.
The Austrian novelist and essayist, Robert Musil, in a 1937 lecture, “On Stupidity,” described what may be the most important distinction in the whole literature of stupidity.
He separated “honorable stupidity,” which is a simple cognitive limitation, or the inability to grasp a difficult argument, from “intelligent stupidity,” which he considered far more dangerous.
This insight is the central concern of my favorite novel of Musil’s, The Man Without Qualities. Intelligent stupidity marshals all the resources of the intellect in the service of an error. It is not the failure to think; it is thinking flamboyantly and systematically in the wrong direction.
A student who cannot follow a mathematical proof is not stupid in Musil’s sense. A math professor who builds an elegant theoretical edifice to defend a proposition a child could see is false is intelligently stupid.
Musil was not alone in taking stupidity seriously. Novelists have long understood the intelligence-stupidity relationship with a clarity that most scientists choose to ignore. Perhaps because fiction can show the process by which an intelligence can devour itself, a rather unpalatable spectacle to a rationalist.
Jonathan Swift’s Gulliver’s Travels describes the Academy of Lagado, whose researchers deploy a variety of elaborate experimental methodologies. One is extracting sunbeams from cucumbers while another softens marble into pillows. A third colleague breeds naked sheep. Swift’s satire is obviously targeted at a variety of forms of misdirected systematic inquiry.
These are forms of intelligence trapped in frameworks so rigid that they produce outcomes that are worse than doing nothing.
In the sky city of Laputa, brilliant mathematicians and accomplished musicians apply projective geometry in order to build with no right angles and chefs prepare meals based on geometric figures. These are abstractions of exquisite subtlety and power in mathematics that become helpless when directed at the wrong practical problems.
William Gaddis in his The Recognitions presents a society of forgery, misattribution, and counterfeiting in which enormous ingenuity is expended in the service of inauthenticity. The protagonist, Wyatt Gwyon, produces forged Flemish paintings that require more skill and knowledge than original compositions. His forgeries are technically masterful, art-historically impeccable, and completely fraudulent. Each of his many characters talk past one another in dialogues of escalating misrecognition, deploying considerable verbal intelligence to deepen general confusion.
Thomas Pynchon’s Gravity’s Rainbow describes a vast bureaucratic and military apparatus of World War II that functions as a machine for converting rational planning into catastrophe. Pynchon’s cartels and rocket engineers are not unintelligent, quite the opposite, and their competence is the V-2 rocket, that threatens to annihilate the war-ravaged cities of the West. Pynchon describes a world in which institutional intelligence becomes a form of collective stupidity.
A number of philosophers have sought to provide a framework for understanding this perverse phenomenon. Erasmus, in 1511, in The Praise of Folly, suggests that folly is the engine of human accomplishment. Without self-delusion and overconfidence, nothing would ever get attempted. The challenge for Erasmus was not whether a civilization produces stupidity, but whether it will produce the kind that can be survived.
Dietrich Bonhoeffer, writing from a Nazi prison in 1943, suggests that stupidity is not a cognitive defect but an imposed sociological structure. People under the spell of power tend to surrender their capacity for independent judgment and become “stupid” instruments.
And Carlo Cipolla, an Italian economic historian, in the Basic Laws of Human Stupidity published in 1976, defined a stupid person as someone who causes losses to others while deriving no gain, or even harm, for themselves.
Cipolla was daring enough to propose that the proportion of stupid people is constant across all populations, from professors, plumbers, generals, and janitors. I would suggest, in line with the speculations of Swift and Musil, that it only gets worse with complication.
If stupidity is costly to its practitioners why does natural or perhaps even cultural selection not eliminate it from populations of organisms? One obvious possibility is a change in the environment that renders a previous behavior obsolete.
For millions of years, maintaining a fixed angle to a distant celestial light source, the moon and the stars, proved to be a vital navigational heuristic across the animal world. It is efficient, reliable, and requires minimal neural hardware.
Once humans invented artificial illumination, a strategy that had worked for eons became suicidal. A moth spiraling into a candle flame is not failing to navigate, it is using a time-tested rule-of-thumb rendered catastrophically wrong by a change in context. Intelligence and stupidity turn out to be chronometrical. A brilliant heuristic and a fatal one can be the same heuristic, separated by an unexpected shift in the world.
Sea turtles have followed moonlight to the ocean for 100 million years and now they orient toward the artificial illumination of mushrooming beachfront condos. Albatross have evolved to scoop fish from the ocean surface and now unknowingly collect floating plastic and feed it to their chicks.
The fungus Ophiocordyceps infects carpenter ants and coopts their behavioral circuitry. An infected ant climbs to a precise height on a plant stem, clamps its mandibles onto the underside of a leaf, and dies, ideally positioned for the fungus to disperse its spores. The ant’s behavioral program has been hacked. The ant is performing a sophisticated sequence of actions, including climbing, orienting, and gripping in the service of another organism. This is an ant perfectly captured by Bonhoeffer’s idea of competence co-opted by a malicious agent to solve the wrong problem.
Perhaps specialization produces local intelligence that when overextended can generate global stupidity. This would be a change in scale and domain that corresponds to the moth’s change of environment.
The physicist Lord Kelvin marshaled all his physics knowledge to prove that heavier than air flying machines are impossible less than a decade before the Wright brothers flew a heavier than air airplane.
Nikola Tesla rejected quantum mechanics and the theory of relativity and argued that future human civilization would run on the energy of the Earth through “earth resonance.” And Percival Lowell used his telescopes to map “canals” on the surface of Mars that he claimed were alien irrigation ditches.
These are all examples of the overcommitment, and overdevelopment of an idea, far from the territory in which an idea once grew and flourished or from which it was unceremoniously banished.
Artificial intelligence is by design the most powerful cognitive artifact ever created. It is engineered to minimize user effort by performing tasks that humans would otherwise find time consuming or impossible.
The problem of course is that the better the tool gets the less the user needs to think for themselves. And in a vicious spiral, the less the user thinks the more dependent they become on their tools. That is until the tool disappears and the whole system collapses.
If intelligence is a necessary precondition for stupidity, and intelligence and stupidity scale together such that it takes real intelligence to be spectacularly stupid, then super-intelligence will be the opening act to an era of super-stupidity.
AI hallucination might be the first evidence of this dynamic. Large language models produce fluent, confident, detailed text that is, with some regularity, factually wrong. And this is not a simple bug but a structural feature of systems that optimize for appeal and plausibility rather than truth.
And the danger is not that the AI will be wrong, after all, humans are wrong all the time, but knowing this, humans have invented means to detect and correct errors. We call this the scientific method.
The danger is that an AI will be wrong in ways humans can no longer detect because the very capacities that would catch the error have been outsourced to the machine or exceed the capacities of human minds.
We face the prospect of a stupidity so sophisticated that it becomes indistinguishable, to its beneficiaries, from intelligence. This is the parable of Douglas Adams’ The Hitchhiker’s Guide to the Galaxy, where the answer to the ultimate question, the meaning of life, the universe, and everything, is 42.
I would like to make a modest proposal and suggest that we need a science of stupidity as rigorous as our emerging sciences of intelligence.
This will not require billions of dollars of investment.
It would involve inquiries into the mechanisms by which intelligent systems produce stupid outcomes.
It would include studying the evolutionary dynamics that maintain stupidity despite its selective costs.
It would promote the development of design principles that distinguish tools which enhance cognition from tools which replace it.
And it would include surveying the institutional conditions under which collective intelligence degrades into collective stupidity.
Stupidity is not what remains when intelligence is subtracted, it is an active mechanism with its own logic, its own dynamics, and a capacity for unbounded growth parasitic on ingenuity. In a world obsessed with ever more powerful cognitive technologies, understanding stupidity is not merely an academic exercise, it might prove to be the most intelligent thing we do.
Related Reading
Adams, D. The Hitchhiker’s Guide to the Galaxy Pan Books, London (1979).
Bonhoeffer, D. After ten years. In Behtge, E. (Ed.) Letters and Papers from Prison SCM Press, London (1953).
Cipolla, C.M. The Basic Laws of Human Stupidity Il Mulino, Bologna, Italy (1976).
Erasmus, D. The Praise of Folly (1511); translated by Miller. C.H. Yale University Press, New Haven, CT (1979).
Gaddis, W. The Recognitions Harcourt, Brace & Company, New York, NY (1955).
Musil, R. On stupidity. Lecture delivered in Vienna (1937). In Pike, B. & Luft, D.S. (Eds.) Precision and Soul: Essays and Addresses University of Chicago Press, Chicago (1990).
Musil, R. The Man Without Qualities (1930–1943); translated by Wilkins, S. & Pike, B. Alfred A. Knopf, New York, NY (1995).
Pynchon, T. Gravity’s Rainbow Viking Press, New York, NY (1973).
Spufford, F. Red Plenty Faber and Faber, London (2010).
Swift, J. Gulliver’s Travels Benjamin Motte, London (1726).
Lead image: jdoms / Adobe Stock
David C. Krakauer is the president and William H Miller Professor of Complex Systems at the Santa Fe Institute.
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統計架構：古典式、頻率式、和貝斯式 -- Leandre Sabourin
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統計學雖然算是一門「專業領域」，但在人人都對「人工智能」朗朗上口的當下，多點常識總有用得上的時刻。對數學、數字、或公式敏感的朋友，不妨跟上一篇「笑話」同時看來調劑、調劑。
Differencing the Three Statistical Framework : Classical, Frequentist and Bayesian
Leandre Sabourin, 01/02/26
Bayesian statistics is an interesting branch of statistics, that is less used, but is gaining in popularity in many different aspects. Although less common because of its mathematical complexity, it is now gaining a lot of momentum accross many different fields, such as data science, medicine, artificial intelligence, etc.
Some software (like JASP) offer the possibility to perform traditional statistical test like the ANOVA into the frequentist or bayesian approach. In this article, we are going to set the basis on what is distinguishing this approach, compared to traditional statistical framework. This will set the tone for future articles where I will take a closer look at Bayes’ theorem.
0. Statistical Framework
In statistics, there are three main framework used to define the probability of an event to happen. Probability is a numerical measure of how likely an event is to happen. There are three principal framework by which you can do statistics. Those framework are (1) the classical approach, (2) the frequentist approach and (3) the Bayesian approach.
1. The Classical Approach
In the classical framework, outcomes that are equally likely have equal probabilities. For example, if you roll a six-sided dice, your probability or rolling a four is 1 in 6. Similarly, your probability of rolling a 2, 3 or 5 is the same.
The classical approach expresses probability as the ratio between the number of favorable outcomes and the number of equally possible outcomes in the sample space. The formula for this framework can be seen as :
P(A) = Number of favorable outcomes/Total number of possible outcomes
Classical approach
2. The Frequentist Approach
The frequentist approach define the probability of an event to happen based of the frequency of this outcome in a sequence of event defined a priori. The frequentist approach is the one mostly used in research nowaday because most of the statistical test rely on this approach.
In essence, the role of researchers and statistician is to recruit a sample of participant that is big enough that they will be able to (1) find a significant effect and (2) infer the results from this sample to the rest of this population. The formula for this framework can be seen as :
P(A) = Number of times event A occurs/Total number of trials
Frequentist approach
There are two subjective components to this approach, the first one being the choice of the statistical test and the it’s components (i.e : post-hoc tests, parameters, etc). To ensure a good analysis, researchers have to justify their choice and have a methodological approach to their experiment. Otherwise, they could attempt to ‘hack’ their test by optimizing the parameters that will allows them to find an effect, without there being an actual one.
The second one is about the number of participants. There isn’t a rule for the amount of participant you have to recruit in your study. Although researchers can do a power analysis to see at what number of participant they are most likely to find a significant effect (more detail about this here), this analysis is still based on a subjective number of participant defined by earlier research in the scientific literature.
3. The Bayesian Approach
The third framework and the one we will be most interested today is the Bayesian approach. In this framework, we are incorporating a prior knowledge and beliefs about an event to happen. Instead of having to rely of a sample size like the frequentist approach, the subjective components of this framework relies on our belief of an event A to happen given an event B. This means that different people can have different probabilities for the same event based on their knowledge and experiences.
One of the strengths of the Bayesian approach is its ability to update beliefs over time. With new evidences coming over time, the initial belief (prior) we had can be revised into a new belief (posterior) For example, if we are rolling a six-sided dice, we would hold the assumption that we have 1/6 chance of getting any of the numbers from the dice. However, if we do 100 attempts, we realize that we are getting the number three 90 times out of these attempts. This new evidence will dramatically change our belief about the fairness of this dice.
Conditional probability
The Bayesian approaches uses principles from conditional probability to predict that an event A will happen given an event B. It updates our knowledge about one variable when partial information about another is known
P(A〡B) = P(A and B)/P(B)
P(A ∣ B): probability of A given B
* P(A and B): probability that A and B happen together
* P(B) : probability of B happening
*Note : the sign ‘∩’ (原文所用符號；此處以 “and” 代替) “means ‘ intersection (“and”), where ∪ represents union (“or”)’. So in that case, P(A ∩ B) refers to the probability of A and B to happen together (i.e : the probability that someone is a math major and a male in a sample).
The main formula used in the Bayesian framework to express that conditional probability is through the Bayes’ theorem, which allows a way to update our beliefs about the probability of an event based on new evidence. In simple terms, it helps us calculate the likelihood of an event happening after we have some additional information.
P(A〡B) = P(B〡A)P(A)/P(B)
This formula expresses how the prior probability P(A) is transformed into a posterior probability P(A∣B) once the evidence B has been taken into account.
Summary
In this article, I presented the three statistical framework used nowadays as well as how probability is being represented. This can be summarized in the following table :
三種統計架構比較表 (請至原網頁查閱)
As we can see, they all have a radically different way to see the probability of an event to happen. understanding the difference between the three will set the tone for futur articles on the Bayesian Statistics. Consider subscribing if you learned something new today, and want to receive notification when I publish a new article!
Written by Leandre Sabourin
Psychology M.Sc. student writing about statistics and psychedelics science
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有關「統計學」的笑話10則 -- Md Johirul Islam
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下文雖然以「笑話」命題，其主旨則在於：
* 釐清「統計」和「統計數字」的概念及意義；
* 說明應用「統計」的正確方式與場合；
* 幫助一般人了解「用數字說謊」的欺騙行為。
故置於此欄。
10 Statistics Jokes That Are Funny… Until You Understand Them
Md Johirul Islam, 06/16/26
Statistics is one of the few subjects where a sentence can sound harmless, a chart can look convincing, and an average can quietly ruin your life.
That's also what makes statistics jokes so good.
They're not just random punchlines — they play with uncertainty, data, averages, probability, false conclusions, and the weird ways humans misunderstand numbers.
If you've ever laughed at the phrase "correlation doesn't imply causation", this post is for you.
Let's break down 10 statistics jokes that are funny… until you realize they're also uncomfortably true.
1. "A statistician drowned crossing a river that was, on average, 3 feet deep."
Why it's funny
This is one of the most famous statistics jokes because it exposes a classic mistake:
An average does not tell the whole story.
Even if the average depth of the river is 3 feet, some parts could be:
* 1 foot deep
* 2 feet deep
* 10 feet deep
And if the person steps into the deep section, the average suddenly becomes useless.
The joke is funny because it shows how relying on averages blindly can be dangerous.
2. "Correlation does not imply causation… but it sure gets published a lot."
Why it's funny
In statistics, correlation means two things move together.
For example:
* ice cream sales go up
* drowning incidents also go up
That doesn't mean ice cream causes drowning.
A third factor — like summer weather — may explain both.
The joke is funny because people, media, and sometimes even researchers love seeing a correlation and jumping straight to a cause.
It's basically a joke about how humans misuse statistics with great confidence.
3. "My data scientist friend says I'm below average. I told him that's mean."
Why it's funny
This joke works because of the double meaning of mean.
In statistics:
* mean = average
In normal language:
* mean = unkind
So if someone says you're below average, calling that "mean" works in both senses.
Simple, clean, nerdy wordplay — exactly the kind statisticians love.
4. "If your head is in the oven and your feet are in the freezer, then on average you're comfortable."
Why it's funny
This is another joke about the danger of averages.
If you average two extreme conditions:
* very hot
* very cold
you might get something moderate.
But no real person experiences an average in that situation — they experience both extremes at once.
The joke is funny because it shows how an average can hide reality instead of revealing it.
5. "The plural of anecdote is not data."
Why it's funny
This line is funny because it sounds like a grammar correction, but it's really a statistics lesson.
An anecdote is a single story:
* "My uncle smoked every day and lived to 95."
* "My friend never studied and still got an A."
But isolated examples are not enough to draw reliable conclusions.
Statistics depends on:
* larger samples
* patterns
* evidence
* uncertainty
The joke is really a warning: one dramatic example is not the same as data.
6. "Never trust statistics you didn't fake yourself."
Why it's funny
This is a twist on the famous cynical line:
“There are lies, damned lies, and statistics.”
It jokes about how statistics can be manipulated by:
* cherry-picking data
* choosing misleading graphs
* using biased samples
* reporting only favorable results
Of course, the joke is intentionally absurd — you obviously shouldn’t trust fake statistics either.
It's funny because it exaggerates a real fear: numbers can look objective even when the story behind them isn't.
7. "Statistically speaking, the average person has one breast and one testicle." (男人數 + 女人數)/2
Why it's funny
This joke is shocking, but mathematically it makes sense if you average anatomy across a whole population.
If you combine everyone together and compute certain averages, you can end up with statements that are technically true in aggregate but ridiculous when applied to an individual person.
* That's the core joke:
* the statistic may be numerically valid
* but it becomes absurd when interpreted literally
It shows that population averages are not personal descriptions.
8. "I'm great at statistics. I can make numbers say anything I want."
Why it's funny
This joke points to a real concern: statistics can be abused.
You can manipulate interpretation by:
* truncating the y-axis on a chart
* selecting convenient time ranges
* ignoring outliers
* choosing a bad sample
* reporting percentages without context
The numbers themselves don't lie — but people can absolutely use them to mislead.
The joke lands because everyone who's seen a suspicious graph knows it's not entirely a joke.
9. "There's a 50% chance this joke is funny."
Why it's funny
This joke plays with the way probabilities are casually thrown around.
People often say "50% chance" when they really mean:
* maybe yes
* maybe no
* I have no idea
But in statistics and probability, 50% has a precise meaning.
So the joke is funny because it pretends to give a mathematically rigorous estimate of humor — which is obviously not how jokes work.
It's funny because it applies statistical precision to something completely subjective.
10. "Our model has 99% accuracy."
"Great. What's the class distribution?" (class distribution：「類別分布」)
"…99% of the data is the same class."
Why it's funny
This is a classic machine learning/statistics joke.
Imagine a dataset where:
* 99% of emails are not spam
* 1% are spam
A lazy model could simply predict “not spam” for everything and still get 99% accuracy.
That sounds impressive, but the model is actually useless.
The joke highlights a huge lesson in statistics and ML:
A metric can look great while hiding a terrible model.
The humor comes from knowing that accuracy without context can be meaningless.
Written by Md Johirul Islam
Software Engineer at Amazon
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「意識」：我的了解-Siegbert
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「我的了解」一詞，出於我對下文作者的基本尊重。也許，稱為「天馬行空之冥想」，或「東拉西扯之胡謅」，比較貼切。置於此欄，以其為「思考方法」之負面教材也。
What is consciousness?
Siegbert, 06/05/26
Religious and spiritual people often think of consciousness as the soul.
Scientists and critical thinkers usually assume that consciousness is a property of the brain, something physical, biological, and not yet fully understood.
I want to argue for a third possibility:
Consciousness may be a property of mathematical principles and quantum mechanics.
At the most basic level, information can be distinguished into 0s and 1s. But between 0 and 1, there is not only a hard binary choice. There is also a space of potential states, uncertainties, probabilities, and transitions.
In this perspective, consciousness does not emerge from fixed information alone. It emerges from the space between fixed informational states.
A bit may be described as either 0 or 1, but the process of becoming 0 or 1 may contain all possible intermediate information states. This uncertain field of infinite potential choices is what we experience as free will: the openness before a decision becomes inevitable.
Before we choose, infinite possibilities seem available. After we choose, reality collapses into one path.
This is why I describe free will as a kind of informational superposition: the ambiguity of decision-making before circumstance forces a final selection.
The second feature of consciousness is its lack of perfect replicability.
We are not able to reproduce our thoughts in exactly the same way five minutes later. Even if we repeat the same opinion to someone, we do not use the exact same words, rhythm, emotion, tone, or sequence. Something always changes. This is important.
A machine can copy a file perfectly. But a conscious being does not simply repeat. A conscious being reinterprets.
This second feature resembles the uncertainty principle. Every thought process contains an element of uncertainty. This uncertainty makes our thinking flexible, diverse, surreal, redundant, inefficient, and creative.
In mathematical language, we are not simply computations of 0s and 1s. We are not just binary machines. We also contain an error parameter:
e
This e is not merely a mistake. It is the creative instability inside consciousness.
So the mathematical description of conscious information might look like this:
[0+e] [0−1+e] [1+e]
These are not just numbers. They are informational states disturbed by uncertainty.
Now, what happens when many e’s accumulate, repeat, interfere, get stored, and become activated again?
The answer is simple: We forget.
Memory is not a perfect archive. It is an unstable reconstruction.
How much do you remember about what you ate last Monday morning? What was the name of the neighbor who moved out of the residual building five years ago? What kind of present did someone give you on your last birthday?
You may remember some of these things. But most likely, many details are already gone, blurred, or rewritten.
Forgetfulness is not a failure of consciousness. It may be one of its central properties to focus on what is really important.
A perfectly deterministic system would store and retrieve everything exactly. But consciousness does not work like that. It compresses, distorts, selects, forgets, and recreates. Maybe this evolution made our brains the fittest for survival.
Another almost mythical property of consciousness is awareness itself.
We are not only processing information. We are aware that we are processing information.
We experience our thoughts, our bodies, our environment, and other people. We do not merely act; we observe ourselves acting.
In mathematical language, this is known as self-reference.
Self-reference can be expressed through recursive functions, iterations, and loops. A system becomes self-referential when it can refer back to its own informational states.
I describe this self-reference as an index function:
i
applied to conscious information states:
[0+e]i(0+e) [0−1+e]i(0−1+e) [1+e]i(1+e)
This means that the system does not only contain information. It also indexes, observes, and references its own information.
In simpler words:
A conscious system is not only a calculator. It is a calculator that watches itself calculating.
This self-reference may be one of the deepest roots of awareness. The brain does not merely compute external reality. It constantly checks, updates, and interprets its own internal states.
That may be why consciousness feels so strange from the inside.
We are, in a sense, inefficient and slow-witted quantum computers. We burn enormous amounts of energy not only to process the world, but to self-reference our own actions, memories, emotions, and decisions at every moment in time.
The principles of superposition, uncertainty, and self-reference may together give the brain its most mysterious abilities:
the ability to experience free will,
the ability to forget,
and the ability to remain aware of itself.
Consciousness is not a simple substance, not merely a soul, and not merely brain matter.
* It is a dynamic mathematical process.
* It is information disturbed by uncertainty.
* It is memory shaped by error.
* It is computation observing itself.
In a certain sense, we may be immortal algorithms. But we are also so unique in our experience, so unstable in our inner states, and so dependent on our exact path through reality, that we can never be perfectly replicated.
Perhaps this is what makes consciousness so difficult to define.
It is not just what we are. It is the process by which we keep becoming ourselves.
What do you think consciousness is?
Leave a comment below.
See you!
Written by Siegbert
spiritualist and philosopher
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真相/真理：三種不同的觀念 -- Ronald Bailey
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不同的人對「真相」或「真理」有不同的「定義」。至於把「真相」或「真理」拿來當工具騙選票、混飯吃、或混砲打的人，可能就在你身邊。
索引：
confirmation bias：偏聽偏信；依照自己的成見、偏見、利益、或立場等因素，「選擇性」的處理相關資訊或證據。
correspondence Theory of Truth：「真理對應論」；在傳統「認識論」中，另外兩個關於「真理」的理論為：「真理相容論」和「真理貓鼠論」；在近代「認識論」中，其它關於「真理」的理論有：「真理建構論」、「真理共識論」、「真理誤指論」、「真理自道論」、「真理言行論」、「真理多相論」等等；另請參考下文作者提供此術語的「超連結」。此處的中文譯名與通行者有出入。
Theories of truth：《關於「真理」的各種理論》；此為《維基百科》條目，對以上各種關於「真理」的理論有簡單說明。
The Surprising Divide Over What Counts as True
A new study finds that what people think about facts, authenticity, or coherent beliefs explains why they disagree about what is true.
Ronald Bailey, 05/15/26
Maria and Peter are students and meet up for a late dinner. Peter asks Maria whether Tom is at the party that they intend to go to after dinner. Maria answers that Tom is at the party. After all, Tom had told her that he would be at the party. When they arrive at the party, it turns out that Tom had changed his plans, and is not at the party.
This was the scenario posed to research participants in a new study by a team of European researchers. They were then asked: Was Maria's answer true or false?
It's pretty clear that Maria's answer is false, at least from my point of view. In other words, I am fully embracing the correspondence theory of truth. However, the study, published in Cognition, shockingly found that only just over 50 percent of participants would agree with me. Apparently, many other people tend to identify truth with how well a statement fits within a person's coherent set of beliefs or whether a person's beliefs are authentic, that is, they are sincere and honest.
To probe how ordinary people think about what is true, the researchers first created conceptual maps of 200 participants asking how similar they think truth is to other related concepts. For example, correspondence related to "reality" and "fact"; coherence to "justification" and "reason"; and authenticity to "honesty" and "transparency." While many participants endorsed notions relating to all three conceptions of truth, in a "winner-takes-all" summary of the judgements, 55 percent aligned most strongly with correspondence.
三種不同的真相/真理觀念統計圖：
54.69% 的人接受真相/真理的「對應性」觀念(correspondence)
10.42% 的人接受真相/真理的「一致性」觀念(coherence)
34.90% 的人接受真相/真理的「真誠性」觀念(authenticity)
In other words, just a bare majority believes that truth is defined by factual reality.
The researchers then wanted to see if these concepts of the truth remained stable in individuals over time. So three months later, they managed to contact 128 of the original participants and ask them to consider what is the truth in the above Maria vignette. In this case, the choice was binary: Was Maria's statement true or false? As the researchers explained, "A 'true' response reflects an authenticity- or coherence-based understanding, as it emphasizes Maria's sincerity or justification at the time of speaking, while a 'false' response reflects a correspondence view, judging truth based on factual alignment with reality." I have no trouble accepting that Maria could try to justify her sincere and honest belief that Tom was at the party, but the plain truth is that he wasn't there.
In the later survey, it turns out that an individual's concept of truth does modestly predict how he or she evaluates the truth of Maria's statement. The researchers report, "Overall 68 (53.13%) participants responded that Maria's answer was false (agreeing with correspondence theory) and 60 (46.89%) that her answer was true (agreeing with an authenticity or coherence notion of truth)." Again, a bare majority endorsed factual reality as the standard for determining what is true.
In an article describing their findings over at Psyche, the researchers outline how different conceptions of the truth can cause conflict:
Imagine someone makes a statement about climate change. The discussion unfolds predictably: one side posts links to data (correspondence), the other side cares less about data and replies with accusations of bad faith (authenticity), or they argue that the statement is untrue because it doesn't fit everything else they already believe to be true (coherence). In such disagreements, giving more of the evidence that convinces you could risk making the conflict worse, not better.
We have all been there, haven't we? Even for those who endorse the correspondence theory of truth must still grapple with the pervasive problem of confirmation bias.
As I reported a while back, research by the Yale law professor Dan Kahan finds that as scientific literacy goes up, so too does partisan polarization on the issue of climate change. In other words, the more science people know, the more they are able to seek out and find information justifying their beliefs.
Nevertheless, the European researchers suggest hopefully that understanding the differences in the conceptions of what is true may help us more fruitfully navigate political and policy disagreements.
Start your day with Reason. Get a daily brief of the most important stories and trends every weekday morning when you subscribe to Reason Roundup. (請至原網頁訂閱)
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「『非』現實論」 -- Jack Preston King
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下文的標題是個「邏輯謬誤」中的「『類型』錯誤」；作者論述「非」的是：「人們對現實的『認知』」；並不是「現實『本身』」。這個「邏輯謬誤」的歷史悠久，可以上溯到釋迦牟尼；如「色即是空」一類說法。
The Case Against Reality
Jack Preston King, 04/17/26
We do not perceive the world as-it-is
Donald Hoffman is a Professor of Cognitive Sciences at the University of California, Irvine, and an award-winning researcher into perception, evolution, and consciousness. Know this going in. Hoffman is a professional scientist, and The Case Against Reality: Why Evolution Hid the Truth from Our Eyes is in every way a serious science book.
It also turns everything we think we know about reality on its head.
The basic premise is simple. At the heart of the “evolutionary algorithm” is fitness. Biological evolution does not work by accurately revealing “objective reality.” It works by identifying and emphasizing environmental fitness payoffs and hazards. The senses of every creature provide critical environmental information regarding what is likely to help them live long enough to reproduce VS what may kill, injure or sicken them. And fitness payoffs/hazards vary according to the creature; they are not intrinsic to the thing encountered. The same evolution that shaped flies to experience excrement as a tasty food source shaped humans to turn away in disgust.
We do not perceive the world as-it-is. We perceive the world as a cognitive interface drawing our attention to fitness payoffs and warning us away from hazards (still a payoff, since we avoided death today). Aspects of reality that serve neither function get edited out of awareness by natural selection, and so are unknown to and potentially unknowable by us. Even with advanced scientific thinking and instrumentation what we can know about reality is limited by our biologically-evolved senses, following evolution’s blind survival algorithm.
We have no idea what’s really “out there” in the world. We can only know how things in our environment relate to human survival. Things that impact our survival, we see twisted in the light of our need (we see their fitness payoff, not their intrinsic nature). Things that don’t impact our survival, we don’t see at all.
In Hoffman’s view — backed by science! — the chance human perception accurately describes objective reality is statistically zero.
From The Case Against Reality:
Darwin’s idea of natural selection entails the FBT [Fitness Beats Truth] Theorum, which in turn entail that the lexicon of our perceptions — including space, time, shape, hue, saturation, brightness, texture, taste, smell and motion — cannot describe reality as it is when no one looks. It’s not simply that this or that perception is wrong. It’s that none of our perceptions, being couched in this language, could possibly be right.
… That revolutionized view leaves in its wake an evolutionary biology that is itself transformed. Still recognizable… are the landmarks of universal Darwinism: variation, selection, and heredity. But gone from objective reality are physical objects in spacetime…
… The FBT Theorem asserts that if reality outside the observer has any structure beyond probability, then natural selection will shape perception to ignore it.
… The key insight of the theorem is simple: the probability that fitness payoffs reflect any structure in the world plummets to zero as the complexity of the world and perception soars… the laws of probability dictate that Truth has less chance than your lottery ticket.
I can’t wait to try this one out on Twitter/X.
THEM: There is no evidence for the existence of God!
ME: There is no evidence for the existence of anything.
THEM: But… But Science! Science describes verified objective reality!
ME: So you deny evolution, then?
LOL.
I listened to the Brilliance Audio audiobook of The Case Against Reality: Why Evolution Hid the Truth from Our Eyes, narrated by Timothy Andrés Pabon. Pabon is terrific, with a clear voice, solid pacing, and lots of inflection. He always sounds interested in the text, which kept me always interested in the text. Well done.
Quotes in this essay are used under fair use copyright guidelines.
This post contains affiliate links. I may earn a small commission if you make a purchase through these links, at no extra cost to you. Rest assured, I only link to books that I personally recommend.
Thank you for reading!
Written by Jack Preston King
I write for people who intuit that there is more to reality than meets the eye. JackPrestonKing.com Follow me on Bluesky @jackprestonking.bsky.social
Not a member? Read this story free on jackprestonking.com!
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社會科學實驗難以重複問題 -- David Randall
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We Know Social Science Is Shoddy. It's Time to Actually Fix It
David Randall, 04/15/26
“Half of social-science studies fail replication test in years-long project”: that’s the headline from reports on the latest mass replication project sponsored by the Center for Open Science (COS). COS has been sponsoring several mass replication projects in the last decade, each of which organizes dozens or hundreds of researchers to replicate recent research in different disciplines. As famed reproducibility researcher John Ioannidis notes, “the results are ‘not surprising’, because they are in line with those from smaller, earlier studies.
A combination of flawed statistical procedures, slipshod research techniques, skewed publication incentives, and politicized and disciplinary groupthink has led to the mass production of irreproducible research. The key takeaway is that you cannot trust recent research to produce a true result. Entire bodies of science may be untrustworthy, as they are built on mountains of individually unreliable results.
Most commenters emphasize how this latest project points out the unreliability of social science research. Skeptic that I am of quantitative social science—my presumption is that much of it is piffle piled on piffle—I actually think that the report should make one optimistic about the capacity of the social sciences. As much as half of their results reproduce! Social sciences are roughly as capable of producing reproducible results as basic life sciences. This actually is a great vote of confidence in the social sciences, which has aimed to provide a quantitative, scientific basis to the study of human behavior.
The social sciences can achieve true results, but not nearly as easily as its practitioners have hoped, and too casually assumed. Any social science results (as any scientific result) should be achieved multiple times, by different researchers, before it receives even preliminary recognition as a plausibly true result. The rate of social science research, if it is to be at all reliable, must be much slower. The social sciences can produce truths, but not if they continue business as usual.
The question is how to change business as usual. At this point, there may be diminishing returns to these massive replication projects sponsored by COS. They provide useful publicity to remind scholars and the public about the existence and the seriousness of the irreproducibility crisis, but at this point most scholars in the affected fields are aware of the problem. They may choose to resist reform, but that is no longer from ignorance. What we need now are ways to build upon these mass-replication projects. We must figure out ways to change the standard operating procedures of the social sciences (and sciences) to create standardly reproducible and reproduced research.
The National Association of Scholars (NAS) has made a series of suggestions for how precisely to reform scientific procedures, and in particular the government’s financial incentives for scientific and social scientific research. But broadly, we should set social science research on an even keel by the following reproducibility reforms:
* Require pre-registered research hypotheses and establish born-open data for all research data.
* Separate data collection from data analysis and establish standard research procedures assigning the two functions to different, independent researchers.
* Establish standard research procedures for different, independent researchers to conduct analysis for every research hypothesis.
* Set high definitions of required statistical power for any quantitative social science research, and make clear that all low-power studies—“qualitative,” “evidence-based,” and other euphemisms for abandoning statistical rigor—have no social-scientific value, and no professional value in any discipline worthy of public support.
Of course these changes would require massive changes in academic culture. Social science researchers will need to accept massive numbers of negative results as normal, and to distribute publications, tenure, and other professional rewards for researchers who get negative result after negative result. Social science departments will need to determine how to distribute job offers and promotion for individual members of massive research teams, where it is more difficult to determine which individuals deserve the most credit. Maintaining researcher independence among cooperating teams will be a practical problem. Independent peer review will become much more difficult if most or all researchers in a particular subdiscipline necessarily will be taking part in a particular cooperative research project.
Perhaps most importantly, reformed social science research will become much more expensive. It costs far more to assemble data with the number of research subjects necessary to produce data with high statistical power. Social science will become far more difficult to fund—and government and private foundations well may determine that it isn’t worthwhile to invest money in social science that costs several times as much for each research result.
All these are great challenges. But if we do nothing, social science will continue to produce results where their veracity is essentially a coin flip. The Center for Open Science has made clear the terrible state of the status quo in the social sciences. Social science scholars and public representatives now should move to make the comprehensive changes in the procedures, cultures, and funding that shape American social science research. Social science research can determine truth. We should start at once to make sure that it does.
David Randall is the Director of Research at the National Association of Scholars.
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關於「受理論制約」的說法 – Paul Austin Murphy
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請參考：
Feyerabend, Paul
Hacking, Ian
Wittgenstein, Ludwig
下文相當鬆散；與其說是一篇「文章」，不如說是作者把他的「讀書心得」或「讀書札記」拼湊在一起。不過，「受理論制約」這個議題值得討論；故轉載於此。
Observations of Feyerabend’s Theory-Laden Underpants
The idea of theory-ladenness became popular in the 1960s, and has remained popular in various sections of academia ever since. It’s often said to have begun with the work of Kuhn, Hanson and Feyerabend in the late 1950s. This idea impacts on science, and is said — by some — to lead to “anti-science”, “relativism” and “the attack on objectivity”. Whatever the case is, scientific theory is obviously distinguished from observation. So are all observations theory-laden? Just some? Or is it more a question of degree? The following essay relies on the work of the philosopher of science Ian Hacking, whose position is nuanced. Hacking sees the truth in much of what Kuhn, Hanson and Feyerabend argued. Yet at the same time he stated that “[t]here have been important observations in the history of science, which have included no theoretical assumptions at all”. Hacking also argued that the overuse of the term “theory-loaded” effectively made it “trifling”.
Paul Austin Murphy, 03/25/26
Philosophical positions on the distinction between observation and theory range from the old “naïve” view (i.e., that scientific observations must be pure) to rejecting the distinction altogether. Many philosophers take a position somewhere down the middle, but even that middle has various grey areas.
The philosopher Ian Hacking took a nuanced position that’s partially against some distinctions between theory and observation, but one which questions the complete rejection of the distinction too.
What is original to Hacking is his stress on experiment, which he believed had been largely ignored by philosophers. More relevantly, we had the distinction between observation and theory, but now we have Hacking’s idea that “[e]xperiment supersedes raw observation” too. It may be a surprise to some that Hacking simply inverts this particular binary opposition. Perhaps there’s little point in saying that observation supersedes experiment or that experiment supersedes observation.
N.R. Hanson and Paul Feyerabend
The fixation on theory-ladenness at least partly began with the philosopher N.R. Hanson. In his 1959 book Patterns of Discovery, he came up with the term “theory-loaded”, which in subsequent years became a bit of a cliché. Hanson argued that every sentence and term is theory-loaded.
Many of Hanson’s examples are convincing. However, is what is drawn from them legitimate too?
The American philosopher of science Dudley Shapere adds to Hanson. In his case, however, it’s the nature of scientific devices which concerns him. Hacking states that Shapere
“ makes the further point that physicists regularly talk about observing and even seeing using devices in which neither the eye nor any other sense organ could play any essential role at all”.
It can be said that there’s no serious problem with scientists using everyday terms in their own non-everyday work. Why shouldn’t they use the words “observing” and “seeing”? What’s more, it can be doubted that many scientists will be troubled with the quibbles philosophers have with their using these words.
Feyerabend went further than Hanson and Kuhn.
In his 1977 book Against Method, he argued that the observation-theory distinction is bogus. In other words, all scientific observations are always theory-laden.
Some readers may now be wondering what exactly Feyerabend meant by “theory”. (This is true about many other uses of that word too.) Hacking picked up on Feyerabend’s use of the word when he wrote the following:
“Unfortunately the Feyerabend of my quotation used the word ‘theory’ to denote all sorts of inchoate, implicit, or imputed beliefs.”
The word “theory” is often thrown around like confetti. Thus, on Hacking’s reading of Feyerabend’s position, one doesn’t need to express one’s theory explicitly, or even know that one has a theory in the first place. In addition, it’s often not the case that a person has a theory “behind” his words or statements: it’s that other people believe that he has. So a theory can be vague, unexpressed and projected onto others.
Despite all that, Feyerabend still believed that theoretical assumptions underlie
“the material which the scientist has at his disposal, his most sublime theories and his most sophisticated techniques included, is structured in exactly the same way”.
Now an everyday cliché can be used:
If everything is classed as a theory, and if theories can be found in every statement or term, then there are no theories at all. The word simply ceases to have any point.
Hacking agrees:
“Of course if you want to call every belief, proto-belief, and belief that could be invented, a theory, do so. But then the claim about theory-loaded is trifling.”
Hacking wrote the following too:
“Of course we have all sorts of expectations, prejudices, opinions, working hypotheses and habits when we say anything. Some are contextual implications. Some can be imputed to the speaker by a sensitive student of the human mind.”
Did Feyerabend really include expectations, prejudices, opinions and habits under the catchall term theory? Indeed, can’t we have expectations, prejudices, opinions and habits and it still not be the case that what we say (or everything we say) is theory-laden?
Hacking: No Theories At All
Oddly enough, although the theories-are-everywhere idea can easily be criticised, Hacking’s own position seems odd too, at least at first. He continues:
“There have been important observations in the history of science, which have included no theoretical assumptions at all.”
Following on the what was said a moment ago, it can be provisionally accepted that these important observations included no theoretical assumptions at all. Yet, at the very same time, the people who made them were (well) over-laden with expectations, prejudices, opinions and habits…
So what?
Hacking’s position may even strike a scientific realist as being extreme. Just for one. Why were such scientists making their important observations in the first place if they were completely free from theoretical assumptions? Of course, we’ll need to see examples here. Hacking, being an historian of science [see here] as well as a philosopher of science, cites plenty.
To ram the point home. Hacker argued that “[t]here are plenty of pre-theoretical observation statements, but they seldom occur in the annals of science”. Just to remind readers. Hacker took a fairly strict position on what a scientific theory is, whereas someone like Feyerabend was very loose with the term.
Logical Positivists and Quine on Observation
Feyerabend was largely reacting against logical positivism. [See here.] Hacking puts the positivist position at its most extreme when he tells his readers that the
“positivist, we recall, is against causes, against explanations, against theoretical entities and against metaphysics”.
More clearly, “The real is restricted to the observable.”
All this depends on what “the real” means. If it means observable, then that statement is true by definition. Of course, the core of the planet Earth can’t be observed, and neither can distant planets and quarks. What about numbers? Positivists had various answers to some of these examples, but not to all.
To run through Hacking’s list. Being against causes is a Humean position. Obviously, causes can’t be observed. Constant conjunctions can be observed, but not the nature of the cause itself. To be honest, I can only guess at what “against explanations” means. Is the argument that if you rely exclusively on observation, then you don’t need explanation too? The case against metaphysics is obvious from a positivist and observation-based point of view.
Now take Hacking’s criticisms of W.V.O. Quine’s position, which stresses not observations, but “observation sentences”. Quine, as quoted by Hacking, states that we should “drop the talk of observation and talk instead of observation sentences, the sentences that are said to report observations”. Hacking had a problem with Quine’s distinction (observations vs observation sentences) within another distinction (observation vs theory).
Hacking stated that Quine was “quite deliberately writing against the doctrine that all observations are theory-loaded”. Quine articulated this position in his 1974 book The Roots of Reference. That was long after Kuhn, Hansen and Feyerabend had first articulated their own controversial theories. As many readers will know, the critics of Kuhn and Feyerabend focussed on their “relativism”, rather than their stress on theory-ladenness. Of course, these two issues have been intimately tied together.
The first thing that can be said here is that Quine’s observation sentences simply seem to be proxies for… well, observations. And Hacking’s general point against Quine is that his theory of observation sentences is just as naïve as some talk about (mere) observations. So Hacking clarifies Quine’s position, which is essentially communal in nature.
Quine believed that “observations are what witnesses will agree about, on the spot”. That’s a non-scientific (or non-academic) three words to use: “on the spot”. Still, Quine provided details. He argued that
“a sentence is observational insofar as its truth value, on any occasion, would be agreed to by just about any member of the speech community witnessing the occasion”.
What’s more, “we can recognise membership in the speech community by mere fluency of dialogue”.
So we have these everyday utterances from Quine: “on the spot”, “just about any member of the speech community”, etc. But let’s remember here that Quine was a pragmatist of sorts. [See here.] So why shouldn’t he have been imprecise on these matters and simply focussed on his own self-referential observation sentences about how communities use words and sentences.
Hacking too has a problem with the above. He stated that “[i]t is hard to imagine a more wrong-headed approach to observation in natural science”. His main argument, at least at this point, was against Quine’s stress on the community. He cited the case of Caroline Herschel, the wife of William Herschel:
“No one in Caroline Herschel’s speech community would in general agree or disagree with her about a newly spotted comet, on the basis of one night’s observation. Only she, and to a lesser extent William, had the requisite skill.”
This seems to go against any notion of communal truth. Alternatively put, it shows that truth can be arrived at independently of any community.
Various communal ideas about truth and knowledge can be dated back to Wittgenstein [see here], as well as before, and Quine was much influenced by him. [See here.] Hacking’s own stress was different. The last two words, “requisite skill”, are important here. In the community of the late 18th century there wouldn’t have been many — if any — people with same skills as Caroline Herschel. But did she or didn’t she discover eight new comets? Yes she did. [See here.] Yet she didn’t need — or rely on — any community to do so, except in less direct and rather obvious senses. In other words, Herschel needed the traditions of science, the devices of science, to share a natural language with various communities, etc. However, none of this is directly connected to Herschel discovering comets, and the knowledge she gained by doing so.
Written by Paul Austin Murphy
MY PHILOSOPHY: https://paulaustinmurphypam.blogspot.com/
My Flickr Account: https://www.flickr.com/photos/193304911@N06/
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