Our science is necessarily perspectival and rooted in metaphor. Like maps, these metaphors are useful, but we shouldn't mistake the map for the territory, writes Andrew Reynolds.
There is a very common view of science, one is inclined to call it the ‘common sensical’ view, that depicts science as the objective description of reality, telling us what kinds of things there are in the world and how they work. We then apply that objective knowledge to create new technologies and medical therapies and so on.
According to this common-sense view, there is a world ‘out there’ that exists independent of our theories and beliefs about it, and this objective reality has its own inherent structure or ‘way that it is.’ The philosopher Hilary Putnam referred to this as a ‘ready-made world’, which is a component thesis of what he called metaphysical realism. This is closely aligned with the position known as scientific realism, which holds that it is science’s job to discover what this way is and to describe it in objectively true terms.
One of the most compelling arguments for scientific realism is that our best theories do manage to accurately describe at least some of this objective reality, otherwise wouldn’t it be miraculous that the theories make such accurate predictions, allow us to control and manipulate phenomena, and explain why things behave the way they do?
Some philosophers, however, refer to metaphysical and scientific realism as naïve realism. Why naïve? Because it seems to assume the existence of a pre-Kantian noumenal ‘world-in-itself’, which even if it does exist, we could not possibly hope to describe or know about in its own objective terms. The best we humans can do is to describe reality as it appears to us to be using the contingent linguistic, mathematical, and visual-pictorial terms that make sense to us. Furthermore, a more realistic appreciation of science as a human activity notes how diverse and distinct its several objectives are: accurately describing what there is, predicting what these things might do next, formulating explanations as to why they behave as they do, and devising ways to manipulate them to our advantage. These are actually quite distinct tasks that may involve different sorts of language and rhetorical strategies to achieve successfully. (See Angela Potochnik’s Idealization and the Aims of Science.)
Science is an irreducibly human activity that requires us to create and experiment with terminology to describe phenomena and to propose explanations that make sense to us.
Throughout the history of science and philosophy, several popular metaphors have expressed the realist picture of science and objective knowledge: ‘reading the book of nature’; ‘the view from nowhere’; ‘knowing without a knower’. The philosopher of science Bas van Fraassen has described scientific realism as the thesis that, “science aims to give us, in its theories, a literally true story of what the world is like.” In one sense it is ironic that realism would be described in terms of a story, for narratives are human constructs that rely on a subjective selection of actors and events. Yet it is not so ironic if we recognize that a literally true statement or description is not the same as an objectively true one - if ‘objectively true’ is supposed to mean in the object’s or nature’s own language and terms. We humans can achieve literal truth, since to use a term in its literal sense is just to use it in the way a community of speakers originally or typically employs it. Scientists make literally true statements all the time, e.g. “The sun is a G-type main-sequence star”, or “Fibroblasts are animal connective tissue cells of mesenchymal origin.” But neither should be mistaken as being expressed in nature’s own objective language (since it doesn’t have one!). These are human inventions, albeit highly useful ones.
Science is an irreducibly human activity that requires us to create and experiment with terminology to describe phenomena and to propose explanations that make sense to us. And unless we are to create wholly new terms for every novel phenomenon, we must press existing words into new roles. That is to say, scientists must engage in metaphor. But in addition to meeting a terminological necessity, it turns out that metaphor is also a very useful cognitive tool, for it facilitates analogical reasoning, allowing us to extend what we understand about one area of experience to another that is less familiar and less well-understood. Metaphor and analogy allow us to recognize similarities in the dissimilar.
This is why the philosopher of science Ernan McMullin said that, “Science aims at ever more fruitful metaphor and at ever more detailed structure.” McMullin was a scientific realist who argued that far from being a hindrance to science’s success, metaphor is a positive contributor. Likewise, the philosopher Mary Hesse showed how important the use of metaphor and analogy is in the expansion of scientific explanation by means of model making. The solar system model of the atom, the billiard ball model of gases, and the wave theory of light are all examples of how metaphors are used to create useful scientific models and theories. (Whether they prove to be empirically adequate or not is a separate matter). Insisting that scientists restrict themselves to literal language would hobble their efforts to devise and test hypotheses.
But metaphor goes beyond mere simile. Whereas simile says A is like B, (which is always literally true in some sense), metaphor asserts an identity: A is B. Simile says DNA is like a code and the genome is like a computer program; metaphor says DNA is a code and the genome is a program. And as a consequence of accepting (or entertaining) the identity asserted through the metaphor, it is only natural that we then attempt to extend it, for instance, by editing the code, to reprogram how cells operate and thereby switch off the cancerous ones. In an interesting example of mixed scientific metaphor, we now hear much talk of using CRISPR—the molecular ‘scissors’—to ‘edit’ genes.
Aside from missing the importance of metaphor, the naïve realist picture misrepresents the actual process and product of science in another important way. For science is less like taking an objective picture of reality and more like creating a map. Maps must accurately refer to the objective (mind-independent) terrain, but every map is created for some particular purpose that necessarily reflects human interests, conventions, values etc. Which means every scientific theory or statement will be necessarily perspectival, approaching the world from a particular vantage point informed by our human neurophysiology, our language, its grammar and the particular vocabulary being employed, and the specific purpose or interest motivating the inquiry.
This best supports, I believe, an instrumentalist interpretation of science, yet one that is still realist. For we do manage to refer to the objective (i.e. mind-independent) world, but insofar as we attempt to describe that objective reality, we, of course, must do so in terms of our own invention, i.e. in a human language, not nature’s own language, simply because nature does not speak any language.
But the realism-anti-realism-post-realism issue is like a frayed rope with lots of split ends. It’s easy for people to talk past one another because they are focused on distinct fibers of that frayed rope or to lose the thread of the argument and jump from one separate issue to another. For instance, just because we can’t claim to describe the natural world in its own objective terms, it does not follow that we cannot successfully refer to objective reality with our subjective human language. Putnam did not argue that our words fail to ‘correspond’ to reality, but that, on the contrary, there are too many possible correspondences, so we cannot assume to achieve a unique canonical one-one correspondence between word and object. (Quine’s point about ontological relativity is equivalent.) There may in fact be an objectively ‘ready made world’ with its own inherent structure (as the philosopher John Searle seems to want to defend), the problem is we could never know that we have captured it in our human linguistic terms. But we shouldn’t be bothered by that impossibility in any case, because science is more similar to map-making than to picture-taking. Even when science attempts to take accurate (objective) pictures of the world, it does so for specific purposes.
Additionally, what Hilary Lawson calls Post-realism strikes me as an overreaction to the problems with naïve realism. His argument that we should stop talking about truth because it encourages dangerously overconfident and intolerant behaviour is a fallacious appeal to consequences. It may be true, for instance, that maybe we shouldn’t tell young children that their parents will one day die because it will upset them, but that doesn’t show that it is false or evidentially unfounded that parents are mortal. Likewise, even if it is true that some people become overconfident and intolerant when they are convinced they have truth on their side, it does not follow that truth is unattainable (literal truth if not fully objective truth) or that our language is incapable of referring to objective (i.e. mind independent) reality.
Science is more similar to map-making than to picture-taking. Even when science attempts to take accurate pictures of the world, it does so for specific purposes.
Science does achieve literal truth, if not a uniquely objective truth in the sense of descriptions of reality or nature in its own terms or language. Literal truth suffices for our human purposes (and whose else’s purposes should we attempt to satisfy?). But because science aims at more than describing reality, it can and does make very productive use of metaphor: to create new categories of kinds of things and explanations of how things work (the search for ‘mechanisms’); and to allow us to alter and control things to our advantage, for example by creating vaccines that will assist our immune system’s ability to create antibodies that bind to the spike protein on the SARS-CoV-2 virus responsible for COVID-19.
One of science’s most currently powerful disciplines, molecular biology, is rife with metaphors: DNA is ‘the code of life’, genes are ‘blueprints’ or ‘programs’, the genome is a ‘book of life’. Powerful new technologies like CRISPR and its associated nucleases (Cas9, Cas3 etc.) are described as ‘programmable molecular scissors’ with which scientists ‘edit’ the genetic ‘instructions’ that allow ‘cells’ to ‘manufacture’ all the protein ‘machinery’ required to perform the essential functions of life.
Of course, the fundamental units of life aren’t really ‘cells’, genes aren’t really ‘blueprints’ or ‘programs’, and CRISPR and its related endonuclease enzymes aren’t really ‘programmable scissors’. And yet scientists do manage to refer to real objective stuff and processes with these metaphorical terms, and they are having real success in editing out (or ‘silencing’) the genetic mutations that lead to forms of hereditary blindness and blood diseases like sickle-cell anemia and thalassemia. We should wish them further success.
This reveals that there is a coevolution of our technologies with the metaphors and models that help us make sense of the world. Our technologies, metaphors, and the desiderata for what we consider a good explanation or description are constantly changing, feeding off one another in a kind of mutually beneficial symbiotic relationship. A term like ‘cell’ may begin as a metaphor, but in time come to be used quite literally.
Science reflects its human-origins just as clearly as any map with its decision on one of many possible mathematical projections, and all its conventions regarding the use of colours, lines and dots. And yet for all that, science does still manage to refer accurately and successfully to objective reality. Like those mapmaking conventions about colours and lines, science’s use of metaphorical language and analogical reasoning are an important feature of its remarkable success.
Still, no map should be confused for the objective terrain it is meant to represent. The identities asserted by metaphor can be deceptive. Nor is any map appropriate for every purpose, no matter how useful it is for one or a few specific tasks. And if scientific terms (both metaphorical and literal) are, as I have suggested, more like tools than purely objective pictures, then this emphasizes the importance of wielding them wisely and responsibly. As our mothers told us, one shouldn’t run with scissors.
Hesse, Mary. 1963. Models and Analogies in Science. London: Sheed and Ward.
Lawson, Hilary. Post-Realism. IAI News 94, 16 February 2021.
-----. In defence of Post-Realism. IAI News 94, 5 March 2021.
McMullin, Ernan. 1984. A case for scientific realism. In Scientific Realism, edited by Jarrett Leplin, Berkeley: University of California Press.
Potochnik, Angela. 2017. Idealization and the Aims of Science. Chicago: University of Chicago Press.
Putnam, Hilary. 1983. Why there isn’t a ready-made world. Philosophical Papers. Cambridge: Cambridge University Press: 205-228.
Reynolds, Andrew S. 2018. The Third Lens: Metaphor and the Creation of Modern Cell Biology. Chicago: University of Chicago Press.
------. Forthcoming. Understanding Metaphors in the Life Sciences. Cambridge: University of Cambridge Press.
Searle, John. 1999. Mind, Language and Society: Doing Philosophy in the Real World. London: Weidenfeld & Nicholson.
Van Fraassen, Bas. 1980. The Scientific Image. New York: Oxford University Press.
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