July 2024: Realism for Realistic People
Similarities and differences between science and everyday practical action
OK, I’ve finally been able to shut up about music theory for long enough to read something else. I’m currently working through Hasok Chang’s Realism for Realistic People.1 This is a proper philosophy book with an abstract -ism in the title and chapters called things like “Truth” and “Correspondence”, which would normally scare me off. I’ve got three reasons for trying it out anyway.
First, I’m a big fan of Chang’s Inventing Temperature, which is about the history of how scientists figured out how to measure temperature, starting from unprincipled reference points like the melting points of butter or wax, and slowly developing more accurate measurements and better theory. It’s a great example of philosophy of science in the context of a real, extended, carefully researched example. And from the introduction to Realism it seems like he’s keeping at least some of the example-based style:
This is the first time I have ever attempted to write an entire book of abstract philosophy. In departing from my normal mode of work I felt that I was answering a call of duty, though the work has certainly been pleasurable, too... I did not in fact succeed in writing an entirely abstract book. On the contrary, the discussion to follow will be peppered with many concrete examples from the history of science, as well as everyday life, though there are no sustained historical studies.
The second reason is that
has been reading this book recently and rates it (he talks about it in Carving up clouds). We’ve already talked about the book a bit, and that might go even better if I, you know, actually read it. So that’s what I’m doing.The third reason is that there’s some overlap in content to
’s In the cells of the eggplant book, but also some interesting differences, which I’ll explore a bit.This discussion is a little premature, because I’m only one chapter in! It’s not quite as bad as that sounds, because it’s a substantial chapter that sets up a lot of the ideas and terminology for the rest of the book. And also I’ve done a quick skim of the other four chapters to get a vague idea of what it covers. Still, this is not going to be anything like a review of the book, just some thoughts I’ve had so far.
Science: similar to everyday activity
So, what does Chang mean by “realistic people”? Roughly, he wants to keep the basic intuition of scientific realism – that some theories and practices are more useful, more successful, more reality-based than others – while staying grounded in actual practice:
There is a widespread conceit that modern science has basically the right answers, or at least the right methods for getting the right answers. Many ‘realists’ maintain this idea like an article of faith, even though we have no direct access to the unobservable aspects of Reality and the historical track-record of science shows serious fluctuations in scientist’s views about the most basic aspects of the universe… I cannot help expressing the feeling that this widespread philosophy of scientific realism amounts to an appropriation of the term ‘realism’ to describe a most unrealistic doctrine. I want to propose a realistic kind of realism, close to what William Wimsatt has called ‘realism for limited beings in a rich, messy world’, which is ‘a philosophy of science that can be pursued by real people in real situations in real time with the kinds of tools we actually have’.
Chang’s approach is a variety of pragmatism, concerned with how knowledge connects to practical action. He points out that scientific theories are not just a bunch of propositions, but rely on what he calls “active knowledge”, the clusters of practices and background understanding that enable us to do things:
Metaphorically speaking, propositional knowledge may only be occasional and localized crystallizations in the flow of activity that is the creation and use of active knowledge
The rest of the book looks to be about rebasing scientific realism on top of this substrate of active knowledge, instead of the unworkable propositional approach. This looks to cover some similar ground to the first section of Chapman’s Eggplant book, though Chang’s focus is different.
The classic realist idea of some theories telling you more about the real world than others gets translated to Chang’s notion of operational coherence, which means that a given cluster of theory and practice works together successfully to achieve some aim. A basic example he gives is that putting your trousers on before your shoes works better than the other way round for getting dressed.
Chang emphasises the continuity between everyday practical action (like the shoes example) and scientific practice, which is also built on this same substrate of active knowledge. His first example for operational coherence is the coordination of actions needed to light a match, and then he introduces his second example, the coordination of satellites, atomic clocks and ground stations needed to operate GPS, with the following:
The same kind of coordination takes place in scientific and technological practice, only with more theorized, complicated and careful actions.
Science: different to everyday activity
This is where Chapman’s Eggplant book diverges strongly. Chapman does talk about the grounding of knowledge in practical action, but then he focuses on how the kinds of knowledge used in scientific practice (he calls this “rationality”) differ from ordinary action (which he calls “reasonableness”). He’s interested in the details of what Chang briefly describes as “more theorized, complicated and careful actions”.
His story for this is fairly complex and there’s too much going on to fully explain in a few paragraphs of a newsletter post, but maybe I can point at some things. I really like this “J-curve” sketch graph from his draft page on Taking rationality seriously:2
The x-axis tracks “development” or complexity of activities, from everyday actions like Chang’s match-lighting example to the more complicated ones needed to make GPS work. “Meaningfulness” on the y-axis is a sort of shorthand for context- and purpose-dependence. Everyday reasonable activities are normally strongly embedded in a context (for example, a birthday party) and have an context-specific purpose (you want to light the candles on the cake), so score fairly high on meaningfulness.
In Chapman’s model, the distinctive thing about rational systems (scientific, technical, bureaucratic) is their use of formalism, where a lot of meaning is stripped away to create a decontextualised, abstracted system that applies to many situations at once and not just the one situation that’s immediately in front of you.
Formalism is still ultimately grounded in reasonable action – he agrees with Chang that there’s no Real World of free-floating propositions that we can somehow access – but it’s made up of carefully systematised actions that we isolate from the mess of the everyday world. We have to make formalism work.
In the GPS example, there are a ton of types of formalism. Most obviously there’s the mathematical formalism describing the fundamental principles of how system as a whole works: things like the geometry of the satellites and the relativistic corrections needed to account for clock differences. Understanding the mathematical formalism grounds out in practical actions like writing symbols on a whiteboard, but it’s a very constrained, stylised set of practical actions.
There’s also a massive amount of standardisation in the equipment used and the protocols used to communicate between subsystems. And plenty of bureaucratic formalism in managing the project, probably involving a whole lot of three letter acronyms.3
Formal systems are low on the “meaningfulness” axis because of the removal of context.4 They often feel kind of meaningless when you’re working with them: “just symbol pushing” or “just tedious form filling”. In comparison, lighting a match feels more embodied, more “like real life”. However, this decoupling from the mess of the everyday world also gives formal methods their power.5 They’re simple and clean enough to be tractable.
This whole story of how science differs from everyday action feels important to me. I don’t know how much it affects Chang’s central interest of finding a workable form of realism that takes active knowledge seriously. From that perspective, he’s interested in how any sort of knowledge is helpful for accomplishing things, and his idea of operational coherence seems relevant to both everyday action and scientific practice. My guess though is that it would still be useful to think about the differences as well as the similarities. I need to read more of the book and think about this some more.
Other stuff
I wrote one more music theory piece on the notebook, Snarking at Rousseau again.
Also, I’m thinking of dropping the monthly updates and just posting when I have something ready. Having to post something every month was a good constraint to get me writing again, but I think the engine is running OK by now and I don’t need it.
I’m starting to miss having a normal blog like my old Drossbucket one, and it feels like Substack is probably the best option for that at the moment. It’s nice having at least some level of built-in community now that Twitter has gone to shit and I can’t plug my stuff there. (I still like the notebook, so I’ll keep that too, but it scratches a slightly different itch.)
It’ll probably be slightly less frequent than now, because I’m not especially fast at writing and even monthly posting is a bit over what I normally end up doing naturally. I’m not going to suddenly start bombarding you with posts.
So there might be another post on 1 September, or it might be some other day. See you soon-ish, anyway.
Cover image shows a GPS satellite (source)
I’m a pretty unrealistic person, but they let me buy it anyway.
It’s very sketch-y because this page is in draft. Caption: “Dude: units?? Also: if you can’t draw, use a program”. I like the sketch version.
A couple of years ago I worked on some planning software used in satellite ground control stations and there was a painful number of acronyms.
Context can be brought back later in when you use your understanding of a particular situation to inform how you use and relate to the formalism: that’s the “metarational” right side of the graph, which is out of scope for this post if I ever want to finish it.
I really like Catarina Dutilh Novaes’s Formal Languages in Logic for explaining this.
Good read! Thank you for writing it. Another book in this canon that I think pairs nicely with Chang’s ideas is Inference and Representation by Mauricio Suárez. Have you seen that one?