what it would take for the public to trust science

Posted on July 22, 2026 by Peter

On July 19, the President posted on the social media platform that he owns:

The National Academy of Sciences has historically published analytical Scientific Manuals expressly for our Federal Judiciary. Of course, the Academy has been run by Radical Left Dumocrats who, it turns out, published fraudulent, biased, and misleading Manuals on Climate Change. These bogus Manuals were used by Judges to decide massive “Climate Change” Cases, and have created huge losses across our Country. These Manuals have been totally DISCREDITED. Our Nation’s Federal Judges deserve Facts and Science, not Political Fraud and False Science on Climate. With this TRUTH, I hereby order Federal Suspension and Debarment Officials to review this conduct. Our Taxpayers should not be funding Climate Fraud, and Judges should never have relied upon it. Thank you for your attention to this matter! President DONALD J. TRUMP

When I heard about this post, I assumed that I would trust the National Academies manual that Trump was denouncing. This was an interesting reaction because I had not seen the document.

I then looked at it (and you can too). It struck me as cogent, informative, and cautious. But I do not independently know whether its claims are true.

Nor do its authors. For the most part, the document refers to large, organized groups, such as the Intergovernmental Panel on Climate Change and the National Research Council. But consider a scientist whose work is very close to the natural phenomena: collecting and measuring atmospheric samples. This person knows what they are doing but not what everyone else’s samples show, let alone how to build the instruments that they all use. And current CO₂ concentrations are only some of the variables that we must measure to understand the climate. The vast majority of what any individual scientist knows comes from reports of what others have done.

As the report that Trump denounced says,

The Earth’s climate system is enormous and complex, consisting of many nested and interlinked subsystems, including the atmosphere, hydrosphere, cryosphere, lithosphere, and biosphere. …. Owing to the breadth and complexity of the climate system, climate research spans many different scientific disciplines. For example, research on the physical science of climate and climate change (i.e., understanding of the physical properties of the climate system and how it is changing) encompasses: (1) mathematics and statistics; (2) basic sciences, e.g., physics and chemistry; (3) earth sciences, e.g., atmospheric sciences, climatology, physical geography, oceanography, meteorology, hydrology, biogeochemistry, and cryospheric sciences; and (4) computer sciences and data analysis. Most climate research involves collaboration across these different fields (National Academies 2025)

The chemists must trust the oceanographers, who must trust the computer scientists. In fact, each chemist must trust the other chemists.

Perhaps all of science depends on trust, although that is less obvious in classic cases, like Galileo dropping objects from the Tower of Pisa, than in climate science. The study of the earth’s climate is intensely collaborative, involving many more people than could ever know each other. It depends on the Integrated Assessment Models, which have developed for more than fifty years (Van Beek, Hajer, Pelzer, van Vuuren & Cassen 2020). Anyone who contributes to these models must presume that the previous contributions have been generally reliable, even when new data changes some of the current findings.

Why do I trust those models? Why do I trust the National Academies summaries of them, or Karen Zraick’s New York Times article that drew my attention to Trump’s “TRUTH”?

On one hand, I have no doubt that the cautiously presented conclusions of the National Academies are valid. On the other hand, my confidence has a sociological explanation. Institutions like universities, scientific disciplines, and the Times have treated me well all my life. I have benefited from operating with an overall model that incorporates confidence in what these institutions say plus direct observations that concur with their findings. I have observed the climate get hotter in New England, but I only believe that this is a global trend and a function of humans’ burning carbon because I believe in institutions, like The New York Times, that report on institutions like the National Academies.

Few people are treated as well or have as much experience with these institutions as I do; I am a college professor in the United States. It is unreasonable to ask people to trust institutions that are unaccountable to them and distant from them. I think that scientific institutions can adjust their behavior to be somewhat less distant from the public, but they cannot bear that responsibility alone.

The classic solution has been intermediary organizations that obtain people’s trust and then independently present information. An important example was the 20th century city or town newspaper. In 1972, the General Social Survey (GSS) found that 69 percent of Americans read a newspaper daily.

Some 20th century US newspapers had specific social or ideological slants, but the market was dominated by local monopoly publications. Like the broadcast television networks of the same period, newspapers would have presented the American Academies’ position on the climate as scientific truth. In those days, Trump’s response would have been much more marginal.

In 1998 and 2008, the GSS asked people whether we trust too much in science. In both years, those who never read a newspaper were much more likely to distrust science than those who did read one. When I made a model that controled for age, race, gender, and years of education, reading the newspaper still predicted less distrust in science, net of the other factors (p <.001).

This doesn’t mean that newspapers caused people to trust science. It could be the other way around–people who trusted science subscribed to newspapers. But I think that newspapers devoted resources and skill to building trust and then guided people’s reactions to other institutions, such as science.

The recipe that sustained metropolitan daily newspapers during the 1900s included hard news plus human-interest stories, comics, sports, gossip, and other features aimed at various members of each household, all funded by a combination of revenue from subscribers and advertisers. Once the internet captured advertising revenue, this model disintegrated. By 2014, just one-quarter of respondents told the GSS that they read a newspaper daily, and the GSS subsequently retired the question, as it ceased to be a useful measure of news consumption.

All intermediary institutions have “business models” of some kind. A 20th century newspaper relied on subscribers and advertisers. An American high school typically gets most of its resources from local property taxes in return for training and minding the community’s teenagers and providing sports and entertainment. Among other things, a school teaches scientific findings and respect for science. Similarly, a public university in the United States offers job training and credentials, health services, quasi-professional sports, patented inventions, and high culture in return for tuition, donations, state subsidies, and fees. Meanwhile, the big social media platforms use algorithms to attract attention, which they sell to advertisers.

Each business model determines the institution’s results, including its impact on trust or distrust for science. Metropolitan daily newspapers tended to boost trust, while today’s social media degrade it.

Trust in science is not self-evidently good; in fact, we can trust too much. And the fact that most newspapers were local monopolies was problematic. They generated trust, but arguably at the expense of freedom. The question is whether we can invent business models for the 21st century that yield genuinely civic outcomes.

Sources: National Academies of Sciences, Engineering, and Medicine. 2025. Reference Manual on Scientific Evidence: Fourth Edition. Washington, DC: The National Academies Press; Van Beek, L., Hajer, M., Pelzer, P., van Vuuren, D., & Cassen, C. (2020). Anticipating futures through models: the rise of Integrated Assessment Modelling in the climate science-policy interface since 1970. Global Environmental Change, 65, 102191.

paradigms of attention

Posted on July 20, 2026 by Peter

Attention as selecting information to accomplish a task

Imagine that you run a laboratory in which you give people or animals stimuli (for instance, showing them shapes on a screen or making sounds) and you record their responses.

Sometimes, your subjects miss things in interesting ways. For example, in the Invisible Gorilla experiment, viewers are told to count how many times players pass a basketball, and most fail to notice a person in a gorilla suit who walks across the court (Chabris and Simon 2011).

This general approach dates back at least as far as Wilhelm Wundt’s psychology lab in Leipzig in the 1880s. Since World War II, psychologists have often posed an analogy between their subjects and computers–specifically, the large class of computers called “von Neumann Machines” (van Neumann 1945). These familiar devices have components that collect data (keyboards, cables, cameras), components that apply stored instructions to process data, components that store data, and components (such as printers and screens) that output the computed data.

Von Neuman sketched the design of a “very high speed automatic digital computing system” and then directed the team that built a prototype computer at the Institute for Advanced Study from 1948-51. According to his sketch, this machine’s computing function, its command function, and its memory function would “correspond to the associative neurons in the human nervous system.” The equivalents of our sensory and motor neurons would be “the input and the output organs of the device” (2.6).

If indeed a person or an animal is like a von Neumann machine, and it fails to notice something, the likely causes would seem to be a limitation of sensory function, a scarcity of computing power, or a failure of memory-storage. For example, if we miss the guy in the gorilla suit–as I did when I was first shown the video–it’s either because something was wrong with my eyes, I was focusing all my processing power on counting the basketball passes, or (conceivably) I saw the guy dressed as a gorilla but I failed to remember him. Most explanations emphasize limitations in our processing power, or “information-bottlenecks.” Then “attention” is whatever process we use to direct our finite processing capacity.

Attention as care for another person

Now imagine that you are observing two friends in a conversation. One tells the other a sad story, and you think that she seeks an expression of sympathy or compassion from the other. But instead of displaying any emotion in response to the story, the other begins to talk about himself.

Here it is unlikely that we will think of a computer or an information-bottleneck. The problem is not that Harry is receiving too much data to be able to process Sally’s story. The problem is motivational. Harry does not care enough about Sally, possibly because he cares too much about himself.

Von Neumann machines have no motives and do not care. Their designers do. Engineers can make machines that scan patients to assist with medical care or that identify enemy soldiers to destroy them. Such differences are crucial, but they are not caused by the machines. They are encoded in the machines’ design, which could be different and which can be changed. Further upstream is an institution, such as a hospital or an army, that paid for (or ordered) the machine to be built. Behind the institution are the people who designed, supported, and led it.

Thus, when we think about this second case, we are likely to ask what Harry and Sally want and possibly what the culture and society have taught or encouraged each of them to do–not about their cognitive limitations.

In fact, people can overcome their inability to grasp or process another’s story by devoting more time to it. In lab experiments, reaction-time is often treated as evidence of attention, but in relationships, the time that we devote to attending anything or anyone is a variable under our control. Indeed, some people choose to devote their whole lives to attending to one thing.

Attention as respect for something beyond me

Another example comes from Iris Murdoch:

If I am learning, for instance, Russian, I am confronted by an authoritative structure which commands my respect. The task is difficult and the goal is distant and perhaps never entirely attainable. My work is a progressive revelation of something which exists independently of me. Attention is rewarded by a knowledge of reality. Love of Russian leads me away from myself towards something alien to me, something which my consciousness cannot take over, swallow up, deny or make unreal (2001, p. 87)

In his beautiful and important book, Shop Class as Soulcraft, Matthew B. Crawford quotes this passage and suggests that the same is true of “any hard discipline, whether it be gardening, structural engineering, or Russian,” when “one submits to things that have their own intractable ways. Such hardness is at odds with the ontology of consumerism” (p. 65).

In these cases, a person attends less to another human being than to a complex and integrated object, whether it is a motorcycle, a natural language, or a garden. Duration is a sign of attention. I am not attending to a foreign language unless I commit considerable time to studying it.

The important questions about attention

I have learned from the laboratory research on attention, and I am sure that I would learn more if I read more and would benefit from that study. But this whole literature seems only distantly relevant (if at all) to urgent concerns about attention:

What should we attend to?
Who should we attend to?
What should we do when we notice something about someone else?
Do we attend too much to some things? (Candidates: ourselves, our own pleasures and aversions, trivia, doomscrolling)
Do we attend too little to other things? (Candidates: nature, people who suffer)
Which actions tend to involve worthy attention? (Candidates: skilled manual crafts, “deep reading,” friendship, prayer)
Who and what affects our attention? Is their impact positive or negative?
How can we design contexts in which attention is more often beneficial?

Why the first paradigm came to dominate

Graham Burnett (2026) offers a compelling history of the idea that attention means selecting information to accomplish a task. He mentions that “a huge percentage of the laboratory research on human attention conducted in laboratories in the United States across the watershed decades of the last century was hot-war- and Cold War–directed inquiry into human abilities to track and trigger on a stimulus—work centrally concerned with military operations” (Burnett 2026). Partly due to funding from the Defense Department, but not only for that reason, psychologists used increasingly sophisticated tools for measuring responses to stimuli. I would also emphasize the powerful metaphor of von Neumann-style computers after 1945 and the genuinely fascinating, interdisciplinary field of cybernetics, which peaked in the 1960s. All of these factors help explain why so much research has treated attention as selection of data to process.

Burnett says, “if in the 1950s the entire budget of the U.S. Department of Defense had been rerouted to fund the attentional research of a set of Tibetan monks, we would surely have inherited by 2020 quite a different scientific literature on human attention. Probably less essentially ‘cybernetic,’ and, it stands to reason, less focused on a variety of operationally oriented ‘tasks.'”

As Burnett acknowledges, the fact that any body of research has contingent reasons does not invalidate it. Psychology labs have generated real findings about attention as a solution to cognitive limitations. It’s just that we need to understand other phenomena that also deserve the word “attention” and that seem barely connected to this experimental literature.

Weil’s alternative

Simone Weil (1909-1943) suggests a very different agenda. She begins her short essay (or long aphorism) on “Attention and Will” by announcing, “It is not about understanding new things, but—through patience, effort, and method—coming to understand self-evident truths with one’s whole being.”

As if imagining a psychology lab (although I doubt she had that in mind), she writes, “The will has power only over a few movements of a few muscles, associated with the model of moving nearby objects. I can will to place my hand flat on the table. If inner purity, or inspiration, or truth in thought were necessarily associated with postures of this kind, they could be objects of the will.” But the will is not what concerns Weil. “Attention is an entirely different thing.”

A bit later, she writes,

The poet creates beauty through attention fixed upon reality. The same is true of the act of love. To know that this man—who is hungry and thirsty—truly exists just as I do: that is enough; the rest follows of its own accord. The authentic, pure values ?of truth, beauty, and goodness in human activity arise from one and the same act: a certain application of full attention to the object. The sole aim of education should be to prepare for the possibility of such an act through the exercise of attention.

I conclude with a passage from the same essay:

Solitude: In what, then, does its value lie? For one is in the presence of mere matter (even the sky, the stars, the moon, the blossoming trees)—of things of lesser worth (perhaps) than a human spirit. Its value lies in the superior capacity for attention. If only one could be attentive to the same degree in the presence of a human being…

Sources: Christopher Chabris and Daniel Simons, The Invisible Gorilla: How Our Intuitions Deceive Us (Harmony 2011); John von Neumann, First Draft of a Report on the EDVAC (1945); Iris Murdoch, The Sovereignty of the Good (Routledge Classics, 2001); Matthew B. Crawford, Shop Class as Soulcraft (Penguin 2009); Graham D. Burnett, “Human Attention as a Philosophical Problem: The Question, and the Nature of Questions,” Metaphilosophy 57:1-2 (2026): 3–22; Simone Weil, La pesanteur et la grâce (Librairie Plon, 1947).

why the humanities could never be automated

Posted on July 2, 2026 by Peter

Sometimes, we want to answer questions to accomplish practical outcomes. For example, we want to know whether a vaccine works so that we can decide whether to use it. Or we may seek basic insights about viruses so that we can develop vaccines in the first place.

Sometimes, we want to know things because we are simply curious. It is hard to justify a lot of astronomy (for example) on the basis of its practical implications. But we want to understand the universe.

And sometimes, we want to understand people–and perhaps animals–because we are in relationships with them. When you ask friends how they’re doing, your motive may not be to solve a problem, nor mere curiosity, but care. You should give your friend your attention. The benefits are psychological, ethical, or spiritual–a change in one’s mind and in the relationship with the other person.

Friends may tell you things that you should believe for practical reasons or to satisfy pure curiosity. Someone might tell you that a vaccine works, so that you will take it, or that all the planets in our solar system could fit between the earth and the moon, because that’s kind of interesting to know. Paying attention means taking such claims seriously. But the main point of learning what other people think is not to find out what is objectively true; it is to know the other people. In fact, exploring the beliefs and values of a wide range of people can shake our confidence in beliefs, in general, and hence our feelings that we can and must get our own beliefs right.

Attending to a text or an artifact from a distant time or place is a little different from an ordinary conversation. For one thing, you cannot directly benefit long-dead or faraway authors by giving them your attention. Although we have ethical obligations to the dead, the influence is basically one-way. However, reading, listening to music, and viewing art are similar to regular conversations in important ways. They too are practices that develop compassion and reduce our attachment to our own prejudices and concerns.

While Michel de Montaigne’s married friend Diane de Foix was expecting a child, he sent her advice about education. He recommended foreign travel and conversation with peers. A youth should listen and appreciate, he said, not try to form and share beliefs.

And so should adults. Montaigne told de Foix, “In this school of human interaction, I have often observed this vice: instead of getting to know others, we only strive to give ourselves and are more concerned with using our own goods than with acquiring new ones. Silence and modesty are qualities very well suited to conversation.” A passage in the same letter could stand as a justification of Montaigne’s whole way of life:

This vast world, which some think is just one species in a larger genus, is the mirror in which we must look to truly know ourselves. In short, I want [our world] to be my student’s book. The variety of moods, sects, judgments, opinions, laws, and customs teach us to judge our own people soundly and teach our judgment to recognize its imperfection and natural weakness: which is no small apprenticeship.

If the purpose of the humanities–the disciplines that interpret texts and artifacts–is not to determine truth but to attend to other people, then we cannot outsource this thinking to machines or even to other human beings. The point is the experience, not the outcome.

Here is a complication: professional scholars in the humanities do pursue the answers to questions. At one extreme, they may seem much like scientists when they establish the date and provenance of a painting or correct a primary text. At the opposite extreme, they may offer highly creative or even counterintuitive interpretations, but usually they still claim to be telling us something valid about an object in the world. We can ask whether they are right or wrong, persuasive or unconvincing.

I think scholarship is very valuable, but it ultimately contributes to the humanities as experience. The point of watching or reading Shakespeare is to get out of one’s own head by attending to the author and his characters. The point of philological, historical, or interpretive scholarship about Shakespeare is to enrich performances and readings of the works.

Some of the scholarly labor can be assigned to machines. Because I cannot read Pali but I am interested in classical Indian philosophy, I have been very carefully using ClaudeAI to give me dictionary-type definitions of all the Pali words in select passages. This is an algorithmic task. Claude is probably using published Pali-English dictionaries, plus previous translations made by people who used the same dictionaries. A dictionary is also an algorithmic device, a kind of machine that generates a range of words in one language for each word in the other language. I believe that the first dual-language dictionary (Sumerian-Akkadian) was written more than 4,000 years ago.

Thus there is nothing fundamentally new about creating devices that automatically assist readers, listeners, and viewers of human artifacts. In addition to lexicons and dictionaries, we might mention grammars, concordances, indices, card catalogues, provenance lists for artworks, search functions for digital texts, and many other scholarly resources. LLMs can often do these things better.

The difference is that we were never tempted to view the tools as ends in themselves. They were meant to assist a reader in a practice that would enrich that person’s mind. Because the LLM’s speak in the first-person and purport to interpret and explain texts, it is tempting–and I feel this temptation–to imagine that they can do our reading for us. To use a simile that is becoming a cliché, that would be like getting a machine to lift barbells to save us the effort. This would not count as exercise.

Source: Montaigne, vol. 1, essay :26 (“Of the instruction of children”). I follow Screech in interpreting “que les uns multiplient encore comme especes soubs un genre” to mean that our world is one species in a greater genre of worlds.

universities and newsrooms as laboratories or debating societies

Posted on June 16, 2026 by Peter

Higher education and the press are important sources of knowledge and insight in modern societies. I think that many people who are interested and concerned about these institutions view them as similar to either 1) labs or 2) debating societies. Both metaphors contain some truth but also mislead.

If you imagine an academic program or a newsroom as similar to a lab, then you will presume that its outputs are information and knowledge. You will probably expect the professionals (professors or reporters) to apply rigorous methods. Any good method counters biases, emotions, and other forms of subjectivity. For example, your own political views should not affect the results of a survey that you conduct if your sample is representative and your statistical techniques are appropriate. In this respect, sampling Americans’ views of Donald Trump is just like taking water samples to measure pH levels. Likewise, your opinions shouldn’t matter if you report on the municipal budget after interviewing a range of insiders and experts.

On this model, you would expect students and novice professionals to learn methods and to be aware of the best supported findings of previous research. Methods and findings should constitute the primary content of education.

When you see a professional consensus about a topic, that is a sign that its methods are working well. Disagreement is problematic, although you can hope that new data or new methods will resolve any temporary dispute.

On the other hand, if you imagine a college as similar to a debating society, then you will think first of a seminar room where there is a free-flowing discussion of a contentious issue (or perhaps a late-night argument in a dorm room). Similarly, you will think first of the op-ed page of a newspaper or a broadcast talk show.

Then you will expect to observe people expressing opinions. Disagreement is desirable–a debate is pointless if everyone agrees–and consensus can be a warning that the whole institution is biased. When someone makes an authoritative claim, along the lines of “We know that X,” you will be quick to suspect them of suppressing alternative views. Your evaluative criteria may include whether the expressed opinions are diverse, whether participants are appropriately open to alternative opinions, whether certain views should be excluded because they are out of bounds, and whether the institution reflects the range of opinions of some appropriate population. (For example, maybe a US broadcast network should present all opinions popular in the US electorate–although that claim is debatable.)

One drawback of the debating society model is that it overlooks the main activities of most professors and reporters: collecting information, applying methods, and reporting results. A session of a college course is much more likely to be spent discussing p-values or prosody than debating politics. In the case of journalism, the number of Americans paid to collect news has fallen by about 77 percent, on a per capita basis, since 1990. There may also be a declining public commitment to academic research across a range of fields.

Also, people who see universities and newsrooms as debating platforms may simply fail to reckon with stubborn information. Sometimes a professional consensus reflects facts, whether we like it or not.

However, if you assume that a university or a newsroom is like a lab, then you will not admit that any question pursued by a journalist or a professor reflects values–beliefs about what is important and why–and assumptions about which methods and sources are legitimate. There may be a neutral way to apply Ordinary Least Squares (OLS) regression to a dataset, but there is no such thing as a neutral dataset. Someone chose to measure certain things because they seemed important.

Instead of being willing to debate and justify your own values and hear critiques of them, you may try to claim that values are irrelevant to your professional work. You will be most comfortable with domains where methods and findings seem relatively uncontroversial, such as the natural sciences and certain kinds of “hard news.” (There either was or was not a fire on Main Street last night).

As topics become controversial, you will become increasingly wary of the observers’ objectivity. For instance, humanities scholars study religion without endorsing specific religions, but you may wonder why something as contestable as a religious belief is a worthy topic, let alone whether an interpretive scholar of religion can be reliable.

For people who see research as value-free science, ethics is unintelligible. It clearly isn’t like a lab science, but if it’s just a matter of opinions, then it isn’t a discipline at all. At best, ethics is a set of legalistic boundaries around the research enterprise, like “Don’t collect data without people’s permission.”

In the modern world, we are confronted with the challenge of navigating both facts and values when the two are deeply connected. We must respect both rigorous methods and free debates. We are trying to grasp truths and honor other people who believe different things. These combinations are difficult.

We might also remember that institutions that are a bit like labs and a bit like debating societies are also other things. Colleges are literal homes for resident students, large-scale employers, institutional investors, landlords, developers, performance venues, and gatekeepers to valuable credentials. Many news agencies are for-profit companies, employers, advertising platforms, and entertainers. Blindness to those realities can make us too comfortable with either model–the lab or the debating society.

the worlds we can lose when intelligence becomes artificial

Posted on May 27, 2026 by Peter

In 1958, Hannah Arendt could see where were were headed:

This future man, whom the scientists tell us they will produce in no more than a hundred years, seems to be possessed by a rebellion against human existence as it has been given, a free gift from nowhere (secularly speaking), which he wishes to exchange, as it were, for something he has made himself. …

It would be as though our brain, which constitutes the physical, material condition of our thoughts, were unable to follow what we do, so that from now on we would indeed need artificial machines to do our thinking and speaking. If it should turn out to be true that knowledge (in the modern sense of know-how) and thought have parted company tor good, then we would indeed become the helpless slaves, not so much of our machines as of our know-how, thoughtless creatures at the mercy of every gadget which is technically possible, no matter how murderous it is. (Hannah Arendt, The Human Condition, 1958, p. 3)

What is “human existence as it has been given”?

For most of our history, most human beings have lived with other people whose names they know. They have worked individually and collaboratively with materials in their context to make an environment that I will call a “world.”

A world has these features:

It is imbued with moral significance, because other people have made it, given it meaning, cared about it, and been affected by it. An individual cannot interact with a world without causing good or harm to other people.
It is real, not imaginary, and therefore it is stubborn. It rarely turns out the way we want, but we can learn from experience to work more effectively with it.
The other people involved in any world hold partially conflicting interests and goals and can be stubborn in their own way. Both the materials and the people resist any single will.
Each person has partial and even biased knowledge, beliefs, and feelings about the world. But their varied ideas can accumulate as they express them and record them. Each person can therefore explore not only a world but the accumulated human experience of that world.
Because we must act in the company of other people and learn by acting, our “thinking and speaking” are closely connected.
Because our deepest concerns (moral, spiritual, and otherwise) relate to the world that we shape with our minds and hands, our “thought” is also connected to our “know-how.”
Each world typically predates each human being and survives the person’s death, yet each person can affect it. In fact, the birth of any human being automatically changes the world, if for no other reason than a birth turns people into parents, siblings, and other kinds of relatives.
There is not one world but many human worlds. But worlds can interact to various degrees without becoming subsumed into one bigger world.

Why it is good to live in a world

It is not obvious that living in this kind of world is the best imaginable form of life. Most people have envisioned heaven or a political utopia differently. (For instance, in an ideal world, the other people usually become less stubborn!) But I could make three arguments in favor of living in a world like this.

First, it seems plausible that homo sapiens evolved for such a life. Our brains, senses, and bodies are equipped to navigate it.

For instance, newborn infants already recognize faces, which are designed to communicate information and emotions. And our languages and cultures have accumulated deep resources for sharing a world with finite other human beings. The Proto-Indo-European language already used first-, second-, and third-person verbs and indicative, imperative, and subjunctive moods to make distinctions that are useful for group discussions about a common world. Thus a world is arguably our habitat.

Second, the combination of agency and humility seems morally compelling. It is fitting that we can affect our environment but not do just anything we individually want with it. And we should see our context as imbued with moral significance.

Third, navigating a world is a way for creatures like us to achieve comprehension, to make sense of matters. As Arendt writes:

There may be truths beyond speech, and they may be of great relevance to man in the
singular, that is, to man in so far as he is not a political being, whatever else he may be. Men in the plural, that is, men in so far as they live and move and act in this world, can experience meaningfulness only because they can talk with and make sense to each other and to themselves (1958, p. 4).

Threats to human worlds

Each human world has always been fragile, subject to destruction if invaders arrive, a plague strikes, or the community breaks down.

In addition, tyrants threaten any shared world because they can turn individuals into means to their solo ends.

Mass society puts each world at risk by bringing us into relationships with millions of others, whose names we will never learn. And mass economic exploitation makes matters worse. In Origins of Totalitarianism, Arendt says, “loneliness, on the experience of not belonging to the world at all, … is among the most radical and desperate experiences of man. [It is] is closely connected with uprootedness and superfluousness which have been the curse of modern masses since the beginning of the industrial revolution and have become acute with the rise of imperialism at the end of the last century and the break-down of political institutions and social traditions in our own time.”

When history seems to move quickly and beyond anyone’s control, humans cease to feel that they are agents in any recognizable world.

Ideology can be defined as any system of thought that substitutes core assumptions for actual engagement with other people in a common world.

Finally, although media can enrich any given world, it can also disrupt it. Imagine people sitting alone or in passive company before a TV screen that tells them about gruesome crimes. Their actual world may be safe, or less dangerous than it was in the past, but the mediated world is cruel.

New threats in the age of AI

This theoretical framework comes from Arendt, who drew on Heidegger’s fundamental insight that the human form of being (Dasein) is always “‘in’ the world in the sense that it deals with entities encountered within-the-world, and does so concernfully and with familiarity” (Being and Time, H105, trans. by Macquarrie & Robinson). Arendt makes Heidegger’s theory political and republican by emphasizing that people can talk and decide what to do with their worlds.

I have sketched this view to help make sense of a new phenomenon: intelligence that is artificial (AI). But Arendt already feared that we might “need artificial machines to do our thinking and speaking.”

When a person expresses a view, the content of what they say helps us to understand the world that the person inhabits. Even when people are flat-out wrong, the fact that they err or lie is part of our reality. In addition, a human view comes from a creature that can suffer. As such, it makes a claim on our compassion. In short, we attend not only to the content of the statement but also to the person who expressed it.

In contrast, when a large language model (LLM) answers a query (typically in the first-person singular and with emotive language like “I will be glad to …”), it does not reflect any particular perspective, nor does it come from a body that is capable of suffering. It just pretends to be a fellow participant in our world. We can attend to the words but not to the speaker.

Walter Cronkite was not really a visitor to Americans’ living rooms in 1970. He just appeared on TV screens. But he was a real person who could be assessed as such. An LLM is qualitatively different.

An LLM can be just another tool or resource, like a Heidegger’s hammer or perhaps like a library. I have collaborated with teams of Tufts engineering students to build the Civic Helpdesk and other applications of AI that are not yet publicly available. Working with them to fine-tune instructions or to design a user interface feels very much like collaborative work in a shared world. Note that I naturally said we “built” these tools, because the work feels roughly like building a shed, or perhaps an organization.

I have also developed what I think is a fairly tight practice of asking Claude about the Sanskrit and Pali original words in texts that I can only read in translation. This feels like a modest expansion of my inner life, if not a contribution to any shared world. (By the way, Claude is probably pulling these definitions from a finite set of published lexicons that have human authors.)

On the other hand, as Pope Leo notes in Magnifica humanitas, “current AI systems are more ‘cultivated’ than ‘built,’ for developers do not directly design every detail, but instead create a framework within which the intelligence ‘grows.’ As a result, fundamental scientific aspects — such as the internal representations and computational processes of these systems — remain, at present, unknown.” This sounds more like Arendt’s nightmare of a time when our thoughts cannot grasp what we have done.

The deepest concern is that we have developed biologically and culturally to flourish in what Arendt would call a world, but an individual who uses AI is no longer there.

the papal encyclical on AI

Posted on May 26, 2026 by Peter

Magnifica humanitas (“Magnificent Humanity”) is Pope Leo ~~XIX’s~~ XIV’s first encylical, subtitled “On Safeguarding the Human Person in the Time of Artificial Intelligence.”

Near the beginning, Leo makes a plea for “a shared discernment process.” He warns against worrying only about “contingencies” and “a succession of emergencies.” It is urgent to think ahout AI more deeply.

He observes that “most people are watching and waiting, observing from afar and merely hoping for the best. For this very reason, crucial questions impose themselves on our conscience and can no longer be avoided: Where are we going? Toward what goal do we wish to orient ourselves? What direction should we choose as a people and as a human community?”

When the Catholic Church practices “discernment” about social conditions, it “does not claim to offer ‘a definitive opinion'” but strives “to listen to and distinguish the many voices of our times and to interpret them in the light of God’s word.” Leo says that the Church is open not only to technical expertise but also to “a diversity of opinions” about values. Leo mentions previous papal letters with gratitude but also “gratefully acknowledges” the development of human rights doctrine through documents like the 1948 Universal Declaration of Human Rights, which did not originate with the Church.

Coming from outside the Church, I also welcome a basic inquiry into human intelligence at a time when computers are supporting a different kind of intelligence that poses risks for humans. The “central question” of the encyclical is indeed a basic question of our day: “what does it mean to safeguard our humanity?” I welcome the normative contributions of Catholic social doctrine, in much the way that Leo says he welcomes other views.

In paragraphs 11-14, Leo names four principles that are essential for “building a city founded on the common good.” The first is “a firm relationship with God.” I respect that idea but cannot follow it. But the second one is important and can be developed outside of Christianity. Leo says:

Today, the human desire for fullness of life is at risk of being misled by deceitful goals, such as the prospect of a technology that promises to free us from all weakness, and models of wellbeing that leave behind entire populations. All too often, we place our hope in unlimited ‘upgrades,’ in forms of progress that exacerbate inequalities, and in immediate solutions incapable of healing people’s wounds. As a result, while some pursue the illusion of unlimited self-assertion, many are deprived of basic necessities. The Church reminds us, with a firm yet humble voice, that true fulfilment is not achieved by eliminating weakness but through harmonious growth. It is found where freedom and responsibility are intertwined with mutual care and true solidarity, and where progress is measured by the dignity of each person and the good of all peoples.

This is very much like Hannah Arendt’s understanding of love for the world (amor mundi). We must love the species we happen to be (including the male portion, by the way). Our love for people should not be contingent on believing that we are good or smart.

When we can improve the human condition, we should. For example, if we can use gene therapy to cure a debilitating disease, then it is our obligation to do so. But the goal is never to perfect human beings. It is to help humans do the best we can with what we are, together.

Leo’s reference to a “city” based on love involves two Biblical stories that he briefly sketches near the outset. The Tower of Babel resembles a modern Large Language Model (LLM):

Fearing being scattered across the earth, [the people] sought to guarantee stability and power for themselves, and above all to ‘make a name’ for themselves. It was an impressive feat: a single language, a single technology, a single direction. However, the project concealed a profound danger. It was a project conceived without reference to God, supported by a uniformity that eliminated diversity and that chose homogenization over communion. When a city is built on pride and the claim to self-sufficiency, communication breaks down, languages are confused and people no longer understand each other. The result is not unity, but dispersion.

The other city is Jerusalem as it was rebuilt by Nehemiah–a story with deep civic resonance that I have discussed on this blog and in What Should We Do? A Theory of Civic Life (pp. 81-83)

The teachings of the Church are grounded in biblical narratives like these but have developed through previous efforts of discernment in modern times. Leo discerns the following components of today’s Catholic Social Doctrine: the equal dignity of all human beings; the supreme value of human rights; the principle of the common good; the principle of the universal destination of goods; the principle of subsidiarity; the principle of solidarity and the principle of social justice.

I will mention a few points that interested me from this framework.

First, the encyclical develops an interesting view of property rights in an era of data science and AI. Leo acknowledges that “certainly there is a right to private property, which has its own specific meaning and purpose.” (He does not explain the purpose of private property, but it could be to permit individuality and thus human dignity.) However, for Leo, private property is “always subordinate to the universal destination of goods,” which means that “the earth’s goods — soil, water, air and natural resources — are given by God to the entire human family to sustain the lives of all, and … every person has an inherent right to the use of such goods, both now and in the future.”

So far, this is established Catholic Social Doctrine, but the novel point in Magnifica humanitas involves intellectual property: “Today, among the goods that are universally intended for everyone, we must also include new forms of property, such as patents, algorithms, digital platforms, technological infrastructure and data.” Later, Leo says, “Data is the product of many contributors and should not be treated as something to be sold off or entrusted to a select few. It is necessary to think creatively in order to manage data as a common or shared good.”

Prevalent legal frameworks treat software and intellectual property as the work of human beings who have the right to own the fruits of their work. But Leo traces all goods back to God. When a human being invents software or a machine, there is no second creation. The output is still meant for the entire human family.

Leo also makes a strong argument against tech-bro economics:

It is important to ensure that this growth in appreciation of human dignity is not obscured by the pressure of new ideologies or very powerful interests in today’s world. Among these ideologies, I consider particularly insidious the one that suggests that every person must earn or justify his or her own worth, to the point of attributing greater value to those who are more efficient or effective. From this perspective, persons end up being reduced to a means of achieving results, a resource to be used and exploited, and are no longer recognized as a proper end in themselves who should never be instrumentalized. The value of persons, however, does not depend on what they achieve or produce. There are rights that apply to everyone simply by virtue of being human, and no human power can legitimately deny or arbitrarily limit them.

The practical concerns that the encyclical catalogues include propaganda and misinformation, loss of meaningful work, rising inequalities of power and wealth, and deadly militarism enabled by “autonomous” weapons systems.

These are valid topics, and the encyclical sometimes reads like a thoughtful but relatively conventional policy white paper.

At times, however, the specifically Catholic perspective lends additional depth to its conventional recommendations. For instance, here is some general advice regarding education in a time of AI:

We need adults to rediscover their vocation as artisans of education, prepared to work patiently each day, with the support of extensive and shared educational partnerships. Today, accompanying children and young people in using technology for developing responsible relationships, helping them to recognize the risks and choose what fosters inner freedom, is a concrete form of charity and will safeguard their dignity. Teaching new generations that technological evolution does not follow a predetermined path, but can be guided by personal and collective responsibility, constitutes one of the most valuable services to the common good.

A purely secular nonprofit could have written those sentences, although perhaps without the reference to charity (caritas). But only the Church would preface this passage with the preceding sentence: “Indeed, we must consider the digital world as a new continent to be evangelized, one that requires generous missionaries who are mature in the faith.”

I suppose I would have liked to read a bit more about the spiritual costs of intelligence that is artificial rather than an activity of the human mind. In the history of the Catholic Church, technology has repeatedly changed how human beings have formed and communicated ideas and meanings. The codex, the confessional, the cathedral, the printing press, and the broadcast studio have restructured individual and collective mentalities. We have survived such changes so far, as has the Church. But right now, we must think deeply and act effectively to prevent AI from reducing us to “data, a cog in a machine or a commodity” so that it can instead become an “instrument of growth, justice and fraternity.”

what is a brute fact?

Posted on March 27, 2026 by Peter

During the twenties, so a story goes, [the former Prime Minister of France, Georges] Clemenceau, shortly before his death, found himself engaged in a friendly talk with a representative of the Weimar Republic on the question of guilt for the outbreak of the First World. War. “What, in your opinion,” Clemenceau was asked, “will future historians think of this troublesome and controversial issue?” He replied, “This I don’t know. But I know for certain that they will not say Belgium invaded Germany.” (Hannah Arendt, “Truth and Politics,” 1967, p. 239 )

Arendt uses this anecdote as an example of “brutally elementary data.” On p. 237, she mentions the “unyielding, blatant, unpersuasive stubbornness” of certain “truths seen and witnessed with the eyes of the body, and not the eyes of the mind.”

I agree that Belgium did not invade Germany in August 1914. (The reverse is true.) However, this example is complicated.

First, it is not a literal fact that “Germany” invaded “Belgium.” The name of any country is a concept, a metaphor, or a simplification. Perhaps the “brutally elementary data” is that some people moved from locations in German territory to locations in Belgian territory, and these people were (among other things) soldiers in the German Army. But even that formulation introduces information that would not be evident to an observer who was unaware of European politics.

Second, you and I do not remember seeing German troops cross the border. We believe that Germany invaded because that is what we have learned in school or from media. Our knowledge is entirely contingent on trust in these institutions.

Third, the word “invaded” is normatively loaded. An invasion isn’t necessarily bad. The Allied landings in Normandy were an invasion in a just cause. But Clemenceau uses the the word to imply that Germany broke its obligations and started the war. He would disagree with someone who said, “In August 1914, Imperial German troops had to extend the front into Belgian territory to protect the Fatherland,” even though that would also describe the same event.

Finally, Clemenceau used this example because he presumed–and expected his audience to presume–that the act of invading Belgium was the crucial causal factor. What if someone replied that the invasion was only one event in a sequence that begin with the assassination in Sarajevo on June 28, 1914, Austro-Hungary’s declaration of War on Serbia one month later, and Russia’s declaration of war against Austro-Hungary?

Clemenceau could have remarked, “They will not say that the Archduke Franz Ferdinand assassinated Gavrilo Princip.” (The reverse was the case). But he did not choose that example because his motive was to cast blame on Germany. There are infinite facts, and Clemenceau selected one to make a point.

Lenin argued that the cause of the First World War was imperialism. Europeans had run out of countries to conquer and exploit and had turned on each other. Some would say that Lenin’s thesis was an interpretation, whereas “Germany invaded Belgium” is a fact. But Clemenceau implied (or “implicated“) a whole interpretation by choosing a particular fact. And Lenin could cite many facts in support of his interpretation.

Insofar as we can know facts by direct observation or reliable methods, we don’t really need a variety of opinions to attain knowledge. If you think of a school, a university, or a newspaper as a purveyor of facts, then you may be uninterested in whether the people involved hold diverse views, and you may be suspicious when they seem to be editorializing. They should stick to the truth. Disagreement is a sign that an issue hasn’t yet been resolved–as it should be.

On the other hand, if you think that every important claim is an opinion, then you will see such institutions as forums for debate. (I think that is how Bari Weiss sees both CBS News and the University of Austin.) You may want these institutions to be pluralistic, but you won’t count on them to generate reliable information. And you may be quick to assert a right to disagree with any claim, no matter the nature of the evidence.

Presumably, we should navigate between these extremes, valuing both information and opinion and recognizing the two as intrinsically linked. Arendt wants us to remain connected to the actual world, and she is worried that ideology disconnects us from facts. But she also wants us to remain connected to other people, who inevitably have different interpretations. As she writes in The Human Condition (p. 57):

… the reality of the public realm relies on the simultaneous presence of innumerable perspectives and aspects in which the common world presents itself and for which no common measurement or denominator can ever be devised. For though the common world is the common meeting ground of all, those who are present have different locations in it, and the location of one can no more coincide with the location of another than the location of two objects. Being seen and being heard by others derive their significance from the fact that everybody sees and hears from a different position. This is the meaning of public life, compared to which even the richest and most satisfying family life can offer only the prolongation or multiplication of one’s own position with its attend ing aspects and perspectives. ….Only where things can be seen by many in a variety of aspects without chang ing their identity, so that those who are gathered around them know they see sameness in utter diversity, can worldly reality truly and reliably appear.

can AI solve “wicked problems”?

Posted on February 12, 2026 by Peter

I’ve been reading predictions that artificial intelligence will wipe out swaths of jobs–see Josh Tyrangiel in The Atlantic or Jan Tegze. Meanwhile, this week, I’m teaching Rittel & Webber (1973), the classic article that coined the phrase “wicked problems.” I started to wonder whether AI can ever resolve wicked problems. If not, the best way to find an interesting job in the near future may be to specialize in wicked problems. (Take my public policy course!)

According to Rittel & Webber, wicked problems have the following features:

They have no definitive formulation.
There is no stopping rule, no way to declare that the issue is done.
Choices are not true or false, but good or bad.
There is no way to test the chosen solution (immediate or ultimate).
It is impossible, or unethical, to experiment.
There is no list of all possible solutions.
Since each problem is unique, inductive reasoning can’t work.
Each problem is a symptom of another one.
You can choose the explanations, and they affect your proposals.
You have no “No right to be wrong.” (You are affecting other people, not just yourself. And the results are irreversible.)

Rittel and Webber argue that those features of wicked problems deflate the 20th-century ideal of a “planning system” that could be automated:

Many now have an image of how an idealized planning system would function. It is being seen as an on-going, cybernetic process of governance, incorporating systematic procedures for continuously searching out goals; identifying problems; forecasting uncontrollable contextual changes; inventing alternative strategies, tactics, and time-sequenced actions; stimulating alternative and plausible action sets and their consequences; evaluating alternatively forecasted outcomes; statistically monitoring those conditions of the publics and of systems that are judged to be germane; feeding back information to the simulation and decision channels so that errors can be corrected–all in a simultaneously functioning governing process. That set of steps is familiar to all of us, for it comprises what is by now the modern-classical mode planning. And yet we all know that such a planning system is unattainable, even as we seek more closely to approximate it. It is even questionable whether such a planning system is desirable (p. 159)

Here they describe planning systems that would have been very labor-intensive in 1973, but many people today imagine that this is how AI works, or will work.

why are problems wicked?

Some of the 10 reasons that some problems are “wicked,” according to Rittel & Webber, relate to the difficulty of generating knowledge. Policy problems involve specific things that have many features or aspects and that relate to many other specific things. For example, a given school system has a vast and unique set of characteristics and is connected by causes and effects to other systems and parts of society. These qualities make a school system difficult to study in conventional, scientific ways. However, could a massive LLM resolve that problem by modeling a wide swath of the society?

Another reason that problems are wicked is that they involve moral choices. In a policy debate, the question is not what would happen if we did something but what should happen. When I asked ChatGPT whether AI will be able to resolve wicked problems, it told me no, because wicked problems “are value-laden.” It added, “AI can optimize for values, but it cannot choose them in a legitimate way. Deciding whose values count, how to weigh them, and when to revise them is a normative, political act, not a computational one.”

Claude was less explicit about this point but emphasized that “stakeholders can’t even agree on what the problem actually is.” Therefore, an AI agent cannot supply a definitive answer.

A third source of the difficulty of wicked problems involves responsibility and legitimacy. In their responses to my question, both ChatGPT and Claude implied that AI models should not resolve wicked problems because they don’t have the right or the standing to do so.

what’s our underlying theory of decision-making?

Here are three rival views of how people decide value questions:

First, perhaps we are creatures who happen to want some things and abhor other things. We experience policies and their outcomes with pleasure, pain, or other emotions. It is better for us to get what we want–because of our feelings. Since an AI agent doesn’t feel anything, it can’t really want anything; and if it says it does, we shouldn’t care. Since we disagree about what we want, we must decide collectively and not offload the decision onto a computer.

Some problems with this view: People may want very bad things–should their preferences count? If we just happen to want various things, is there any better way to make decisions than to maximize as many subjective preferences as possible? Couldn’t a computer do that? But would the world be better if we did maximize subjective preferences?

In any case, you are not going to find a job making value-judgments. Today, lots of people are paid to make decisions, but only because they are assumed to know things. Nobody will pay for preferences. Life works the other way around: you have to pay to get your preferences satisfied.

Second, perhaps value questions have right and wrong answers. A candidate for the right answer would be utilitarianism: maximize the total amount of welfare. Maybe this rule needs constraints, or we should use a different rule. Regardless, it would be possible for a computer to calculate what is best for us. In fact, a machine can be less biased than humans.

Some problems with this view: We haven’t resolved the debate about which algorithm-like method should be used to decide what is right. Furthermore, I and others doubt that good moral reasoning is algorithmic. For one thing, it appears to be “holistic” in the specific sense that the unit of assessment is a whole object (such as a school or a market), not separate variables.

Third, perhaps all moral opinions are strictly subjective, including the opinion that we should maximize the satisfaction of everyone’s subjective opinions. Then it doesn’t matter what we do. We could outsource decisions to a computer, or just roll a die.

The problem with this view: It certainly does matter what we do. If not, we might as well pack it in.

AI as a social institution

I am still tentatively using the following model. AI is not like a human brain; it is like a social institution. For instance, medicine aggregates vast amounts of information and huge numbers of decisions and generates findings and advice. A labor market similarly processes a vast number of preferences and decisions and yields wages and employment rates. These are familiar examples of entities that are much larger than any human being–and they can feel impersonal or even cruel–but they are composed of human inputs, rules, and some hardware.

Another interesting example: integrated assessment models (IAMs) for predicting the global impact of carbon emissions and the costs and benefits of proposed remedies. These models have developed collaboratively and cumulatively for half a century. They take in thousands of peer-reviewed findings about specific processes (deforestation in Brazil, tax credits in Germany) and integrate them mathematically. No human being can understand even a tiny proportion of the data, methods, and instruments that generate the IAMs as a whole. But an IAM is a human product.

A large language model (LLM) is similar. At a first approximation, it is a machine that takes in lots of human generated text, processes it according to rules, and generates new text. Just the same could be said of science or law. This description actually understates the involvement of humans, because we do not merely produce the text that the LLM processes to generate output. We also conceive the idea of an LLM, write the software, build the hardware, construct the data centers, manage the power plants, pour the cement, and otherwise work to make the LLM.

If this is the case, then a given AI agent is not fundamentally different from a given social institution, such as a scientific discipline, a market, a body of law, or a democracy. Like these other institutions, it can address complexity, uncertainty, and disagreements about values. We will be able to ask it for answers to wicked problems. If current LLMs like ChatGPT and Claude refuse to provide such answers, it is because their authors have chosen–so far–to tell them not to.

However, AI’s rules are different from those in law, democracy, or science. I am biased to think that its rules are worse, although that could be contested. The threat is that AI will start to generate answers to wicked problems, and we will accept its answers because our own responses are not definitively better and because it responds instantly at low cost. But then we will lose not only the vast array of jobs that involve decision-making but also the intrinsic value of being decision-makers.

Source: Rittel, Horst WJ, and Melvin M. Webber. “Dilemmas in a general theory of planning.” Policy sciences 4.2 (1973): 155-169. See also: the human coordination involved in AI; the difference between human and artificial intelligence: relationships; the age of cybernetics; choosing models that illuminate issues–on the logic of abduction in the social sciences and policy

why policy debates continue

Posted on December 12, 2025 by Peter

I’m at Stanford today to discuss a paper, Policy Models as Networks of Beliefs. After circulating my draft, I realized that the following is really my argument. …

We use mental models to think about and discuss contested questions of policy. Worthy models typically have these features:

They have many components, not just a few. A model might include a causal inference, such as “spending more on x produces better outcomes.” But those two components (the spending and the outcomes) must be part of a much larger model that also explains why certain outcomes are valuable, where the money would come from, what else effects the system, and so on.
The components should be connected, and the resulting structure matters. Structures can take various forms (e.g., root-cause analysis, vicious cycles). There is no single best structure.
Pieces of models may prove regular. For instance, maybe spending more on x regularly produces better outcomes, all else considered. But such regularities only apply to small aspects of good models. The science-like effort to find regularities can only get us so far.
Some components of any worthy model should be values or normative claims. Some normative components have regular significance in all models. However, many value components change their significance depending on the context. Equality, for example, does not consistently mean the same thing and may not always be desirable.
If a model proves influential, it can change the world, which can require a new model. For example, arguing that more money should be spent on X could cause more funds to be allocated to X, at which point it would no longer be wise to increase the funding. Models are dynamic in this sense.

I believe this account supports a pluralistic, polycentric, pragmatist, and deliberative approach to policymaking, as opposed to a positivistic one.

Cezanne’s portait of Gustave Geffroy

Posted on October 4, 2024 by Peter

In “Cézanne’s Doubt” (1946), Maurice Merleau-Ponty discusses Paul Cézanne’s portrait of the critic Paul Geffroy (1895-6), which led me to some congruent reflections.

Merleau-Ponty notes that the table “stretches, contrary to the laws of perspective, into the lower part of the picture.” In a photograph of M. Geffroy, the table’s edges would form parallel lines that would meet at one point, and the whole object would be more foreshortened. That is how an artist who followed what we call “scientific perspective” would depict the table. Why does Cézanne show it otherwise?

Imagine that you actually stood before Paul Geffroy in his study. You would not instantly see the whole scene. Your eye might settle on your host’s face, then jump to the intriguing statuette next to him. The shelves would at first form a vague pattern in the background. Objects for which you have names, such as books, would appear outlined, as borders filled with color. On the other hand, areas of the fireplace or wall would blend into other areas.

You would know that you could move forward toward M. Geffroy, in which case the table would begin to move below you. Just as you see a flying ball as something moving–not as a round zone of color surrounded by other colors–so you might see the table as something that could shift if you moved your body forward.

A photograph of this real-world scene would be a representation of it, very useful for knowing how M. Geffroy looked in his study, and possibly an attractive object in its own right. But the photo would not represent anyone’s experience of the scene. Instead, it would be something that you could experience, rather like the scene itself, by letting your eye move around it, identifying objects of interest, and gradually adding information. You would experience the photograph somewhat differently from the actual scene because you would know that everything was fixed and your body could not move into the space.

A representation of this scene using perspective’s “laws” would make the image useful for certain purposes–for instance, for estimating the size of the table. Michael Baxandall (1978) argued that Renaissance perspective originated in a commercial culture in which patrons enjoyed estimating the size, weight, and value of objects represented in paintings.

But other systems have different benefits. Here is a print in which Toyoharu Kunichika (1835-1900) uses European perspective for the upper floor and a traditional Chinese system (with lines that remain parallel and objects placed higher if they are further away) for the lower floor. As Toshidama writes, this combination is useful for allowing us to see as many people and events as possible.

Print by Toyoharu Kunichika from Toshidama Japanese Prints

Perspective does not tell us how the world is–not in any simple way. The moon is not actually the size of a window, although it is represented as such in a perspectival picture (East Asian or European). Perspective is a way of representing how we experience the world. And in that respect, it is partial and sometimes even misleading. It overlooks that for us, important things seem bolder; objects can look soft, cold or painful as well as large or small; and some things appear in motion or likely to move, while others seem fixed. We can see a whole subject (such as a French intellectual in his study) and parts of it (his beard), at once and as connected to each other.

Merleau-Ponty writes:

Gustave Geoffrey’s [sic] table stretches into the bottom of the picture, and indeed, when our eye runs over a large surface, the images it successively receives are taken from different points of view, and the whole surface is warped. It is true that I freeze these distortions in repainting them on the canvas; I stop the spontaneous movement in which they pile up in perception and in which they tend toward the geometric perspective. This is also what happens with colors. Pink upon gray paper colors the background green. Academic painting shows the background as gray, assuming that the picture will produce the same effect of contrast as the real object. Impressionist painting uses green in the background in order to achieve a contrast as brilliant as that of objects in nature. Doesn’t this falsify the color relationship? It would if it stopped there, but the painter’s task is to modify all the other colors in the picture so that they take away from the green background its characteristics of a real color. Similarly, it is Cézanne’s genius that when the over-all composition of the picture is seen globally, perspectival distortions are no longer visible in their own right but rather contribute, as they do in natural vision, to the impression of an emerging order, of an object in the act of appearing, organizing itself before our eyes.

The deeper point is that a science of nature is not a science of human experience. Third-person descriptions or models of physical reality are not accounts of how we experience things. And even when we are presented with a scientific description, it is something that we experience. For instance, we actively interpret a photograph or a diagram; we do not automatically imprint all of its pixels. And we listen to a person lecture about science; we do not simply absorb the content.

There are truths that can be expressed in third-person form–for example, that human eyes and brains work in certain ways. But there are also truths about how we experience everything, including scientific claims.

And Cézanne is a scientist of experience.

Quotations from Maurice Merleau-Ponty, “Cézanne’s Doubt” (1946), in Sense and Non-sense, translated by Hubert L. Dreyfus and Patricia Allen Dreyfus (Northwestern University Press 1964); image by Paul Cézanne, public domain, via Wikimedia Commons. The image on the Mus?e d’Orsay’s website suggests a warmer palette, but I don’t know whether it’s open-source. I also refer to Michael Baxandall, Painting and Experience in Fifteenth Century Italy : A Primer in the Social History of Pictorial Style (Oxford, 1978).

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