Hi all, welcome back to The HPS Podcast. I'm Samara Greenwood and today I'm excited to have Mauricio Suárez on to talk about his new book, fresh off the University of Chicago presses, titled Inference and Representation: A Study in Modeling Science. Mauricio is Professor of Logic and Philosophy of Science at the Universidad Complutense de Madrid, a life member of Clare Hall, Cambridge and research associate at the London School of Economics.
As we will hear, Mauricio has had a career-long interest in the ways scientists represent the myriad phenomena they study. For example, complex molecules, living systems, cyclones, galaxies, to name just a few. We often learn about the world through such representations, most commonly through scientific models.
A longstanding concern in philosophy of science is: How are we to best understand the relationship between a scientific model and the aspect of the world the model is intended to represent? Are there substantive criteria, a set of necessary and sufficient conditions, such as similarity, between the model and phenomena that a model must meet to serve as an adequate representation? Or is it better to take a more pragmatic and, what Mauricio calls, a deflationary approach? In this approach, the search for a defining condition for representation is abandoned. Instead, Mauricio argues that the effectiveness of a model lies not in any substantial criteria, but rather in the way it is used in practice, and in particular, its ability to enable appropriate and fruitful scientific inferences.
Samara Greenwood: Hi, Mauricio, and welcome to the podcast. First, could you tell us a little bit about the background to your publication? What inspired you to write this particular book?
[00:01:53] Mauricio Suárez: Well, there's a very long history behind the writing of this book. I have to say, this is probably going to be the book of my life. This book has been an ongoing project of mine for about 20 years now, and it really goes back to my PhD days when I was doing my PhD at the London School of Economics (LSE).
I was working on scientific models and particularly on models in the foundations of quantum mechanics. I got very interested in the nature of models there. A spirited and really very good project ongoing at the LSE at the time was on models in physics and economics involving people like Nancy Cartwright and Margie Morrison and Mary Morgan. Many very good people. We had very distinguished speakers come to town for it.
I got really interested in what's really making models work in science and how do scientists use models in order to achieve their aims. So out of that, my PhD wasn't on scientific representation. It was on models in quantum mechanics. But immediately after my PhD thesis, I started thinking about representation. That's what this book is about. It's about the topic of scientific representation.
When I started working on this, it was a pretty self-contained field with just a few papers that one could read and discuss. But as I started working on this project, it became bigger and bigger and bigger until suddenly there's this enormous amount of literature in the philosophy of science concerning the topic.
At some point, I decided to streamline the project and write a more concise kind of book on specifically my views on scientific representation, what I call the inferential conception of scientific representation. That was about five, six years ago when I'd made that decision.
After I made that decision, it was actually possible to write this book. The book really brought itself very quickly once I decided to focus on an inferential approach to scientific representation.
Basically, it's been a very long-standing interest of mine. It is very central to many different topics in the philosophy of science. It touches on many different areas and affects many different debates in the philosophy of science. So, I think it's a central topic to the field. And really, the driving motive has been to try to bring out all the consequences and all the implications that this inferential conception of scientific representation has for a number of different topics and questions and debates in the philosophy of science. That's why it's taken so long.
And that's really the motivation underlying the book - to come up with a novel account of scientific representation that has some consequences for important debates in the philosophy of science.
[00:04:30] Samara Greenwood: That leads well into my next question. What are these central ideas from the book that you would like your reader to take away with them?
[00:04:37] Mauricio Suárez: I think there are at least three main motives in the book. Three main messages, if you like, that I'm trying to convey to the reader. Firstly, that there are many different accounts of representation and the ones that really work for you and deliver all the philosophical goods you want to have, are deflationary in character. That would be my first message, that a deflationary account of scientific representation is likely to be right.
The second message is that there is a history to all these debates we are having in the philosophy of science concerning the notion of scientific representation, and that it pays to think about that history and to reflect upon that history.
I take this history all the way back to the 19th century. I look at the 19th century modelers in Britain and Germany mainly, what I call the English speaking modelling school and the German speaking modelling school. I trace many of our philosophical concerns to debates that were taking place then. So, another message of the book is that this whole debate in philosophy of science has a history, and in fact, the contemporary mode of scientific research, what I call the modelling attitude, that puts modelling at the centre of scientific work, this also has a history. It has a genealogy. You can trace back its origins. At some point in the 19th century, science becomes an essentially modelling affair.
This second message is important to me because there is a way in the book in which I argue historically for my conclusions, and you have to know a little bit about the history. So, this really is an HPS project. It's a History and Philosophy of Science project. I think the history is important to it. This genealogy of the modelling attitude is another message that the book is sending.
Finally, the third message that the book is sending concerns the relationship of philosophy of science to other areas of philosophy, and in particular to the philosophy of art.
I do something unusual in the book, which is that I bring to bear lessons from the philosophy of art onto the philosophy of science. And I claim philosophers of science can learn some things, some important lessons, in fact, from philosophers of art, and vice versa. So, the other message in the book is to try to establish a conversation between the philosophy of science and the philosophy of art and aesthetics, which is again, quite an unusual message for a book in the philosophy of science.
[00:07:01] Samara Greenwood: Could you tell us a bit more about that view on scientific modelling and scientific representation and how your position differs from other views?
[00:07:09] Mauricio Suárez: Right. The book is a defence of an inferential conception of representation, and this is what I call a deflationary account of scientific representation.
I have to start by distinguishing deflationary from substantive accounts of representation, which are at the heart of many of the discussions on the nature of models.
The vehicle that's doing the representing is typically the model, and the vehicle that is getting represented is what we call the target system, or the object, or the process, or whatever phenomenon of interest that scientists are trying to model.
So substantive accounts take it that there is substantial relation, that can be defined by means of necessary and sufficient conditions, in virtue of which a model is a representation of its target. I think this view confronts a number of insurmountable objections, and it's really not a plausible view when you think carefully about it.
Modelling is really a practical affair, and it involves many different relations between the modelled sources and the targets in different contexts. So, I defend a kind of pluralism about the means of representation, which is not really consistent with a substantive account of representation.
Then if you want to focus particularly on what constitutes the relation of representation that models hold to their targets, I claim that you can only find very minimal platitudes, which are inferential in nature. They have to do with the fact that models are always used to draw inferences about their targets and to therefore provide us with information about their targets. That's as much as you can say generally about the notion of representation, and therefore that's as much as you can say generally about all models in general and what they have in common.
It's sufficient to have a general account, but it's not substantive enough that you would want to have a philosophical theory of this relation. It's a very, very thin account of representation, hence the name deflationary. Here I draw on some similar distinctions that have been employed in the literature, in the philosophy of language, in particular, with respect to the notion of truth. It is a very established part of that discussion that there are deflationary and substantive accounts of the notion of truth. I argue that, analogously, we should think that when it comes to the notion of representation and modelling in science, there are also substantive and deflationary accounts, and I favour the deflationary accounts. I provide a large number of arguments against the substantive accounts and in favour of the deflationary accounts.
That already distinguishes my position from that of many other writers who have written on the topic of models who often, unreflectively, I think, have taken for granted a substantive account of representation.
Another way in which I differ is that I have a specifically inferential account of scientific representation which puts the notion of surrogative inference at the heart of modelling. So models are about enabling scientists to draw inferences about their target, so that they can get to learn something new about their target. But those inferences can be many and varied and different, and they can be valid in accordance to very many different standards of validity. Not just only in terms of logical validity, whether it be deductive or inductive or abductive, but also in every context, there will be specific norms of valid surrogative reasoning that apply to different models.
Again, this is very distinct about my view that it puts this notion of surrogative inference at the heart of modelling. I think many people nowadays share the intuition that a lot of scientific modelling is about surrogative inference, but many see that as an epiphenomenon of the notion of representation. While I think of it as the central component of the notion of representation and the only general necessary condition for a scientific model to represent a target.
That's really what's distinct about the view. I think you can have a number of different views. I'm not saying that my view is the only legitimate view to have on scientific models, but I put forward some important arguments, in the book, why you should certainly seriously consider models are just inferential devices towards their targets.
[00:11:37] Samara Greenwood: Fantastic. Maybe you could provide some concrete examples related to real life models that might help us better understand this perspective. I know on your cover you have a bridge. Perhaps you could tell us about that particular example?
[00:11:50] Mauricio Suárez: Yes. So, in the third chapter of the book, I go into quite a lot of detail in the discussion of a number of scientific models. I even distinguish models in four different kinds; scale models, analogical models, mathematical models and theoretical models.
Then I have three case studies that I draw on throughout the book to illustrate some of these discussions with very specific examples of scientific models. The one that you allude to, which features prominently on the cover is the model which was a blueprint for the engineers who built the fourth rail bridge in Scotland. The bridge that connects the Lothian region to the Fife region, basically Edinburgh to St. Andrews. When you go from Edinburgh to St. Andrews, you have to cross this bridge. You either cross the fourth road bridge, if you're driving, or you still cross the fourth rail bridge, which was this bridge that was built at the end of the 19th century.
So this is an engineering feat in Victorian engineering history. It is also an emblem for Scotland. So, it's a very well-known bridge. If you ever find yourself in Scotland, you can ask anybody, they will immediately recognize it.
I turned it into one of the main case studies for different reasons. One reason is because I really wanted a case study from engineering to contrast with the other case studies from theoretical science, whether it be physics or biology. This is a very well-studied case, so there was a large literature to draw on. Another reason why I draw on this case study is because it was employed in a very celebrated history of art book by Michael Baxandall called Patterns of Intention. This is very well known amongst historians of art. It has been very influential.
As you know, I wanted to draw connections between the philosophy of science and the philosophy of art. And I wanted to be able to draw on those connections from the word go.
made a beautiful study of this engineering feat in order to compare it with a bunch of other historical cases that he studies in the history of art. He extracts a number of important lessons about how we should go about interpreting these historical artifacts. I wanted to draw on that literature and it was important to me.
The final reason why I use this case study and the reason why it features on the cover is that it is one magnificent example of a model as a vehicle for surrogate reasoning, which is moreover animated. The main engineer to this bridge, Benjamin Baker, came up with this so-called ‘living model’ of the main principle that underlies the construction of the bridge. This is a cantilever bridge, basically built on the principle of the cantilever, and this living model - that he enacted with actual people in the building site - perfectly exemplifies the cantilever principle.
So, the model entices you in a very animated way. You can actually do things with it. You can go into the model, you can walk into the model. This then enables you to draw relevant inferences about how the bridge is being built and what the engineering principles are underlying the bridge. So, I found that a fascinating example of a model in action.
A model that you can touch, and you can actually become part of the model yourself, if you just put yourself in the position of some of the characters that appear in this living model. That way you can enact the principles of the target system that the model is representing.
I thought this is a fascinating example of a model along the lines of my conception of models as representations. I really wanted to give it a prominent place in the book. It ended up making the cover because it is so wonderful, I think.
[00:15:37] Samara Greenwood: A wonderful example. I've got a whole lot of visuals associated with it. You mentioned then the connections you make between scientific representation and aesthetics and the philosophy of art. Could you tell us a little bit more about what kind of connections you found there?
[00:15:51] Mauricio Suárez: In the eighth chapter of the book, I developed this analogy between philosophy of science and philosophy of art. I talk extensively about artistic representation. I really like the chapter because I think it's quite innovative and it does a number of things that had not been done before.
I look back to some of the main pieces and views in the philosophy of art concerning representation, mainly in the works of Nelson Goodman, his very influential Languages of Art published in 1975. I already mentioned Michael Baxandall and his Patterns of Intention. This is a book that is important to my book because I drew lots of lessons as I was working on this analogy between science and art.
And finally, I pick up particularly on the work of Richard Volheim, the prominent British philosopher of art. I drew a number of lessons from his phenomenological approach to artistic depiction. So, Volheim is well known for introducing this notion of two foldedness, which is this idea that when you're standing in front of a painting, you're simultaneously aware of the features of the canvas and of what it represents. You can be simultaneously experiencing both. In fact, the experience of a representational painting involves a simultaneous awareness of both these elements.
A number of philosophical lessons follow from this view. I claim in this chapter in my book that something very similar applies to scientific models. There is a similar sense in which models are standing in for their target systems. In order to employ the model coherently, as a scientist, you have to be aware both of the features of the model and of the features of the target that that model is capturing or representing. A number of similar consequences follow from this twofold awareness of the model and its target. I explore those consequences in quite a bit of detail in that chapter.
So this is all quite a novel approach to the connection between the philosophy of art and the philosophy of science. It is what people in the philosophy of art call a phenomenological approach to the notion of representation and I take inspiration from a lot of it for my account of models.
[00:18:07] Samara Greenwood: Talking about that twofold awareness, my background is in architecture. We do a lot of modelling in that and there is this sense where sometimes models offer affordances for allowing you to understand certain things about the object that it's representing. But in other times it really throws up some problems as well. Does that come into it?
[00:18:26] Mauricio Suárez: Yes, I think you're absolutely right. Models are very useful tools for, as I mentioned, surrogate inference. Models have lots of different uses for scientists, but they can also mislead. And this is an important part of this modelling approach to scientific inquiry.
With models, you're always presented with an analogy. An analogy is always going to have some limits and some limitations, and there are some ways in which the analogy will not lead you to the right conclusions. Most models will idealize in some ways, they will simplify in some ways, they may caricaturize in some ways, they may distort the target system in some ways. They will do this for reasons that have to do with either the ease of handling the model or how plausible they can be to be applied in a particular context, how they connect with all the background knowledge that we may have, that we want to preserve. There are many different reasons why modelers will introduce distortions, idealizations, even fictional assumptions into models for the sake of advancing scientific inquiry.
So, this introduces a paradox, which is an epistemic paradox that has been often discussed in the field. How is it that models allow us to gain knowledge of their target systems while at the same time introducing all these kinds of distortions and even fictional elements or assumptions into the model that we know do not correspond to the real target systems?
There's a long debate about this and whether this is a good or a bad thing about models, whether this is something that we should preserve or try to eradicate, whether this means that modelling is a lesser kind of scientific activity than theorizing or experimenting or theoretical demonstration, which normally proceeds on the basis of first principles and is more secure or safe.
There's a long discussion about this and I tend to follow on the modeler's side. These assumptions, whether they be fictitious or idealized, they're important. We have to learn to live with them. Not all scientific knowledge is fact-ive in this way. There is a lot of scientific knowledge that is mediated by these fictional assumptions and idealizations.
Modelling is indispensable. You cannot eradicate it. And it is not an inferior mode of knowledge once you know that there are all these assumptions are involved in it, and that you have to be careful, when you draw conclusions from a model about its target, what are the conclusions that are really warranted by the assumptions that are more legitimate? And what are the conclusions that may not be so warranted because the assumptions have been introduced for practical purposes, even though we may know that they're not faithful assumptions or veridical assumptions?
So I think this is a very important point about what I call the modelling attitude. This attitude in the development of science that I claim goes back to the 19th century, is very much mediated by all this fictional, idealized assumptions, abstractions of all these different kinds and distortions. This becomes part of the everyday way of doing science, and it therefore becomes important for anybody who wants to interpret science appropriately, to be very aware of this and to be very capable of distinguishing the different kinds of assumptions that come into play in any given model.
This is very important.
[00:21:56] Samara Greenwood: That makes a lot of sense. On a slightly different topic, how do you think the insights that you bring through in your book might be useful for practicing scientists? What could they take away from this?
[00:22:07] Mauricio Suárez: Yes, I hope that practicing scientists will look at the book too. It is a scholarly book and addresses mainly philosophers and historians of science, but I've written it in a way that I hope will at least in parts lend itself to be useful to practicing scientists in different ways.
I don't have the hope that the book will make scientists better modelers. I'm not one of those philosophers of science - I don't think there are that many of them nowadays - who think that philosophy of science ought to instruct scientists about how to do their work. But nonetheless, I think it can help scientists to read books like this to become more reflective about their practice. To carefully think and consider some of the assumptions that go into modelling work. How we build models, why we build those models, what are our aims in building those models.
Besides these methodological questions, there are also historical questions that are important to this development of the modelling attitude. I think scientists may gain some sense of the historical depth that goes into this modelling attitude and these modelling strategies, and how they have roots in this 19th century conception of science.
Finally, for any scientist who has ever thought about the epistemic implications of their work, anybody who's ever considered questions about realism, and I think many scientists do at some point think about realism and instrumentalism, I discuss all these topics in light of the lessons about modelling and representation. I think this may also help scientists who want to think a little bit about the epistemic consequences and implications of their work.
I think many scientists are increasingly, I'd say, becoming interested and aware of these epistemic and epistemological debates. Then you may be interested in the epistemological chapter, chapter number nine, where I discuss all these issues.
[00:24:02] Samara Greenwood: Hmm. No, wonderful. That does sound like a rich resource for all of those aspects.
Is there anything that the broader public might be interested in here? Beyond scientists, beyond scholars, is there anything more general? I think the history side of things sounds really interesting. Is there anything else you can think of?
[00:24:18] Mauricio Suárez: Yes. The history side, besides the purely historical interest, may appeal to any member of the public who has an interest in how science works. They may find a number of aspects of the historical development of this idea interesting.
I think in drawing this history of the modelling attitude, this genealogy of the modelling attitude, I'm also drawing on certain cultural resources. There is a bit of cultural history in the book. In fact, I discuss different cultural approaches to the place of science in society that emerged in the 19th century.
I only touch very sparingly on these topics, but they are there. In fact, I'm becoming so interested in these cultural dimensions that I'm now working on another book where I develop these cultural implications of this modelling attitude. I claim it has some implications for how we see science in society. What sort of trust may we deposit on scientific work? We touched a little bit on this in this interview when you asked me about all these distortions and idealizations that come into modelling. That already tells you something about the limits of the trust that the public ought to deposit on scientific work.
I'm somebody who thinks that on the whole science provides us with the best sources of our knowledge. We ought to trust it quite generally, but not always to the letter, maybe, and not in the application of every model, because models sometimes, as we mentioned before, can distort and idealize and contain assumptions that are far from the truth. So, I think the public is probably wise to trust science with some caution. The book is working to approach and to understand the measures of caution that go into this trust that we may have in science.
Finally, I'm also becoming interested in the way the modelling attitude bears on the so called two cultures divide between science and the humanities. One thing that you find is that the main protagonists in the birth of the modelling attitude in 19th century science were very well versed in humanities, and in particular they were very well trained in philosophy.
I talk about James Clerk Maxwell and William Thomson, Lord Calvin. I talk about Hermann von Helmholtz, and I talk about Henrik Hertz and Ludwig Boltzmann in Vienna. They are very interesting scientists because they are the first technical scientists. They inaugurate this modelling attitude, but they are also the last generation of natural philosophers. They are all very well versed in philosophy and a lot of their work is philosophical in their spirit and their inspiration.
I think there is something that happens with the birth of the modelling attitude that divides our culture into two - the scientific disciplines and the humanities. I would like to think that reflecting upon that division and how it comes about may help us bridge the divide between the two cultures. So I think that there are some lessons there concerning the place of science in culture more generally and not just in society.
[00:27:35] Samara Greenwood: Oh, that sounds wonderful. I can't wait to read the book in full. I still have to wait until I get it here, but it sounds absolutely wonderful. Was there anything that you wanted to add?
[00:27:44] Mauricio Suárez: I just really hope people will read the book and I'm looking forward to feedback and reactions. This is an ongoing topic of debate, and I can imagine that there will be some objections and there will be some endorsements and it's been quite a pleasure to write the book over such a long time. I'm very pleased that it's finally coming out and I'm being able to give this interview about it.
It feels very special. Thank you.
[00:28:10] Samara Greenwood: Oh, that is fantastic. I just want to thank you so much for being on the podcast, Mauricio, and discussing you're very exciting new book with us.
[00:28:18] Mauricio Suárez: Thank you so much for this interview and for the wonderful questions. I think the questions were all wonderful, they really go to the heart of the project. Thanks a lot.
[00:28:26] Samara Greenwood: Fabulous. Thank you.
If you would like to learn more about Mauricio's book, we've provided links in the show note. On the publisher's website, there are also some great reviews, including one by another wonderful podcast guest, Hasok Chang, who says Mauricio's book provides “a compelling deflationary account of representation without metaphysics. Engaging fully with the complex realities of the inferential practices, this landmark work should appeal to philosophers, historians of science, and practicing scientists alike.”
With that, I would once again like to thank you for listening and helping us launch The HPS Podcast in 2023 to such great success. According to our stats on Buzzsprout, our podcast has been listened to almost 10,000 times and our weekly downloads put us well into the top 25% of podcasts hosted on their site, which does seem crazy for an Australian podcast talking about a niche academic area and run by two grad students. We knew we loved HPS, but it is great to know there are so many others out there who love this field too.
If you would like to engage further with us and the HPS community more generally, I do encourage you to join the BlueSky community. If you are after an invite code for this social media, we have quite a few. You can just email me if you like at samarag@unimelb.edu.au and I can pass one on to you.
So now we're on a very well-deserved break until March 2024, but do please reach out via that email or social media if you would like to give us your feedback on the podcast. Thank you once again for supporting us in our very first year. It has been an absolute blast, and we look forward to having you back again next time.
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