Science is the best way to produce facts about reality. The best, at least, that limited human beings have devised so far. Yet, not even scientists quite seem to understand how scientific knowledge is generated. This is not only a philosophical but also a practical problem, as our misunderstandings affect the quality of our research and limit the directions it can take. In light of this, it may be good if we reflected a bit more on how we do science — to become better researchers through philosophy. Here, I provide an accessible introduction to a philosophical approach that achieves precisely this: William Wimsatt’s multi-perspectival realism. It disabuses us of widespread but misleading myths and idealizations about science, such as the idea that everything in the world can be reduced to a fundamental level, or that we can approach a “view from nowhere” — complete and objectively detached knowledge of the world. Wismatt proposes an alternative view based on his thorough studies of actual research practice. It cuts deeply into the layered yet messy structure of reality, and the improvised but potent tools we have available, as limited and evolved beings, to explore it. Wimsatt reframes science as an irregular yet adaptive process rather than a cumulative repository of unalterable facts. His philosophy provides a workable and grounded middle way between radical skepticism and naïve belief in the objective truth of science. It explains how knowledge is conceptually constructed by humans, but still connects us to reality in a trustworthy way. We need such a new view of science, not only to improve our research practices and outcomes but, more generally, to gain a more realistic understanding of ourselves, the world, and our place and role within it.
Illusions of Understanding in the Sciences
Scientists seek to understand the causes of observed phenomena. Beliefs that they have succeeded are based on understanding that is rarely or possibly never complete, and varies in depth and quality. Most often scientists believe they understand more than they do, making their belief an illusion. This illusion then persists in explanations scientists provide in print, in talks, or in discussions. The illusion that a scientist has a valid and complete explanation tends to be magnified when the data are well described by mathematical and computer simulation models due to the precision of such models and their ability to predict well; prediction does not imply causality, but gives the illusion that it does. The first part of this essay supports the case for the universality of partial and incomplete levels of understanding by showing the difficulty of reaching a deep level of understanding for even a simple analysis and model that most scientists use and believe they understand: linear regression. The second part highlights some implications of the existence of many levels of understanding and explanation, and their use by scientists for design, testing, analysis, and theory development. It discusses the way that deduction and induction depend on the levels of understanding and the implications of the illusion that a scientist’s understanding is deep. It makes a case that the many incomplete levels of understanding affect, often unwittingly, the ways scientists design experiments, test theories, comprehend, communicate, and teach.
Artificial intelligence and illusions of understanding in scientific research
Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.
Guest post: If you’re going to critique science, be scientific about it
Loren K. Mell Editor’s note: This post responds to a Feb. 13 article in The Atlantic, “The Scientific Literature Can’t Save Us Now,” written by Retraction Watch cofounders Adam Marcus and Ivan Oran…
Scientific Constructivism | The Paper Pilot
The Paper Pilot's digital garden of thoughts on philosophy, politics, and sociology
Weird Studies
Professor Phil Ford and writer J. F. Martel host a series of conversations on art and philosophy, dwelling on ideas that are hard to think and art that opens up rifts in what we are pleased to call "reality."
Against theory-motivated experimentation: Can random experimental choice lead to better theories?
Scientists must choose which among many experiments to perform. We study the epistemic success of experimental choice strategies proposed by philosophers of science or executed by scientists themselves. We develop a multi-agent model of the scientific process that jointly formalizes its core aspects: active experimentation, theorizing, and social learning. We find that agents who choose new experiments at random develop the most informative and predictive theories of the world. The agents aiming to confirm, falsify theories, or resolve theoretical disagreements end up with an illusion of epistemic success: they develop promising accounts for the data they collected, while misrepresenting the ground truth that they intended to learn about. Agents experimenting in these theory-motivated ways acquire less diverse or less representative samples from the ground truth that also turn out to be easier to account for. Random data collection, on the other hand, combines virtues of diverse and representative sampling from a target scientific domain which enables cumulative development of the successful theoretical accounts of it. We suggest that randomization, already a gold standard within experiments, is also beneficial at the level of experiments themselves.
Fear of Knowledge: Against Relativism and Constructivism
In recent times, relativist and constructivist conceptions of truth and knowledge have become orthodoxy in vast stretches of the academic world. In this unique volume, Paul Boghossian critically examines such views and exposes their fundamental flaws. This short, lucid, witty book shows that philosophy provides rock-solid support for common sense against the relativists; it is provocative reading throughout the discipline and beyond.
Many Minds: Science, AI, and illusions of understanding
AI will fundamentally transform science. It will supercharge the research process, making it faster and more efficient and broader in scope. It will make scientists themselves vastly more productive, more objective, maybe more creative. It will make many human participants—and probably some human scientists—obsolete… Or at least these are some of the claims we are hearing these days. There is no question that various AI tools could radically reshape how science is done, and how much science is done. What we stand to gain in all this is pretty clear. What we stand to lose is less obvious, but no less important. My guest today is . Molly is a Professor in the Department of Psychology and the University Center for Human Values at Princeton University. In a recent , Molly and the anthropologist presented a framework for thinking about the different roles that are being imagined for AI in science. And they argue that, when we adopt AI in these ways, we become vulnerable to certain illusions. Here, Molly and I talk about four visions of AI in science that are currently circulating: AI as an Oracle, as a Surrogate, as a Quant, and as an Arbiter. We talk about the very real problems in the scientific process that AI promises to help us solve. We consider the ethics and challenges of using Large Language Models as experimental subjects. We talk about three illusions of understanding the crop up when we uncritically adopt AI into the research pipeline—an illusion that we understand more than we actually do; an illusion that we're covering a larger swath of a research space than we actually are; and the illusion that AI makes our work more objective. We also talk about how ideas from Science and Technology Studies (or STS) can help us make sense of this AI-driven transformation that, like it or not, is already upon us. Along the way Molly and I touch on: AI therapists and AI tutors, anthropomorphism, the culture and ideology of Silicon Valley, Amazon's Mechanical Turk, fMRI, objectivity, quantification, Molly's mid-career crisis, monocultures, and the squishy parts of human experience. Without further ado, on to my conversation with Dr. Molly Crockett. Enjoy! A transcript of this episode is available . Notes and links 5:00 – For more on LLMs—and the question of whether we understand how they work—see our with Murray Shanahan. 9:00 – For the paper by Dr. Crockett and colleagues about the social/behavioral sciences and the COVID-19 pandemic, see . 11:30 – For Dr. Crockett and colleagues’ work on outrage on social media, see this . 18:00 – For a recent exchange on the prospects of using LLMs in scientific peer review, see . 20:30 – Donna Haraway’s essay, 'Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective’, is . See also Dr. Haraway's book, . 22:00 – For the recent essay by Henry Farrell and others on AI as a cultural technology, see . 23:00 – For a recent report on chatbots driving people to mental health crises, see . 25:30 – For the already-classic “stochastic parrots” article, see . 33:00 – For the study by Ryan Carlson and Dr. Crockett on using crowd-workers to study altruism, see . 34:00 – For more on the “illusion of explanatory depth,” see with Tania Lombrozo. 53:00 – For more about Ohio State’s plans to incorporate AI in the classroom, see . For a recent essay by Dr. Crockett on the idea of “techno-optimism,” see . Recommendations , by Adam Becker , by L. A. Paul , by Miranda Fricker Many Minds is a project of the , which is made possible by a generous grant from the John Templeton Foundation to Indiana University. The show is hosted and produced by , with help from Assistant Producer and with creative support from DISI Directors Erica Cartmill and Jacob Foster. Our artwork is by . Our transcripts are created by . Subscribe to Many Minds on Apple, Stitcher, Spotify, Pocket Casts, Google Play, or wherever you listen to podcasts. You can also now subscribe to the Many Minds newsletter ! We welcome your comments, questions, and suggestions. Feel free to email us at: manymindspodcast@gmail.com. For updates about the show, visit or follow us on Twitter () or Bluesky ().
What Makes a Good Theory, and How Do We Make a Theory Good?
I present an ontology of criteria for evaluating theory to answer the titular question from the perspective of a scientist practitioner. Set inside a formal account of our adjudication over theories, a metatheoretical calculus, this ontology comprises the following: (a) metaphysical commitment, the need to highlight what parts of theory are not under investigation, but are assumed, asserted, or essential; (b) discursive survival, the ability to be understood by interested non-bad actors, to withstand scrutiny within the intended (sub)field(s), and to negotiate the dialectical landscape thereof; (c) empirical interface, the potential to explicate the relationship between theory and observation, i.e., how observations relate to, and affect, theory and vice versa; (d) minimising harm, the reckoning with how theory is forged in a fire of historical, if not ongoing, abuses—from past crimes against humanity, to current exploitation, turbocharged or hyped by machine learning, to historical and present internal academic marginalisation. This work hopes to serve as a possible beginning for scientists who want to examine the properties and characteristics of theories, to propose additional virtues and vices, and to engage in further dialogue. Finally, I appeal to practitioners to iterate frequently over such criteria, by building and sharing the metatheoretical calculi used to adjudicate over theories.
The Origins Of <br>‘Planetary Realism’ And <br>‘Whole Earth’ Thinking | NOEMA
There is no escaping an interconnected world.
A Vision of Metascience
How does the culture of science change and improve? Many people have identified shortcomings in core social processes of science, such as peer review, how grants are awarded, how people are selected to become scientists, and so on. Yet despite often compelling criticisms, strong barriers inhibit widespread change in such social processes. The result is near stasis, and apathy about the prospects for improvement. People sometimes start new research institutions intended to do things differently; unfortunately such institutions are often changed more by the existing ecosystem than they change it. In this essay we sketch a vision of how the social processes of science may be rapidly improved. In this vision, metascience plays a key role: it deepens our understanding of which social processes best support discovery; that understanding can then help drive change. We introduce the notion of a metascience entrepreneur, a person seeking to achieve a scalable improvement in the social processes of science. We argue that: (1) metascience is an imaginative design practice, exploring an enormous design space for social processes; (2) that exploration aims to find new social processes which unlock latent potential for discovery; (3) decentralized change must be possible, so outsiders with superior ideas can't be blocked by established power centers; (4) ideally, change would align with what is best for science and for humanity, not merely what is fashionable, politically popular, or media-friendly; (5) the net result would be a far more structurally diverse set of environments for doing science; and (6) this would enable crucial types of work difficult or impossible within existing environments. For this vision to succeed metascience must develop and intertwine three elements: an imaginative design practice, an entrepreneurial discipline, and a research field. Overall, it is a vision in which metascience is an engine of improvement for the social processes and ultimately the culture of science.
Reinventing Discovery: The New Era of Networked Science
In Reinventing Discovery, Michael Nielsen argues that w…
Midjourney
An independent research lab exploring new mediums of thought and expanding the imaginative powers of the human species.
How To Think Real Good | Meta-rationality
Practical epistemology: heuristics for how to think about difficult things, particularly using formal methods.
Without a theory of intelligence
The history of science — and of progress — is a series of benefits that are direct results of new tools.
Ecocivilization: making a world that works for all
""One of the greatest thinkers of our age" (The Guardian) presents a new way of living-one modeled on nature's design instead of capitalism's-for fans of Guns, Germs, and Steel and Doughnut Economics It has often been said that it is easier to imagine the end of the world than it is to imagine the end of capitalism-and yet that is what the historical moment urgently calls for. Climate change has reached an emergency state, inequality continues to grow, and, for many, the future has never seemed more bleak. Incremental policy improvements are no longer enough-we need a deep transformation of our current civilization to continue to survive. In Ecocivilization, leading thinker Jeremy Lent reimagines the basis of our civilization, and argues for a new global system of living, one based on life-affirming principles modeled after nature's own design. What enfolds is a robust framework incorporating Lent's own expertise, and the lived experiences of those on the ground already putting ecological civilization's core tenants into practice-justice, mutuality, diversity, and symbiosis. From the global economy to universal housing and income, from infrastructure to agriculture, every major aspect of our society could be redesigned to work together as a coherent whole, setting the conditions for all people to flourish. Ecocivilization shows how this future on a regenerated Earth is not only desirable, but entirely feasible"--
