I am an Assistant Professor in the Department of Philosophy at Stockholm University. I am also a Research Associate at both the Institute of Philosophy, School of Advanced Study, University of London, the Centre for Philosophy of Natural and Social Science at the London School of Economics and Political Science. Most of my research has concerned scientific modelling: how do we use models to represent and explain natural and social phenomena? I think that to address these questions it's worth listening to what other philosophers have to say about linguistic or pictorial representation. But I also believe that the question won't be answered fully unless we investigate how scientists use models in practice. I'm currently thinking about the broader implications of the idea that all of the representations - from science, art, and the humanities - we use to situate ourselves in the world are partial and imprecise. I wrote an easy introduction to these sorts of issues for the Institute of Arts and Ideas.
This Element presents a philosophical exploration of the notion of scientific representation. It does so by focusing on an important class of scientific representations, namely scientific models. Models are important in the scientific process because scientists can study a model to discover features of reality. But what does it mean for something to represent something else? This is the question discussed in this Element. The authors begin by disentangling different aspects of the problem of representation and then discuss the dominant accounts in the philosophical literature: the resemblance view and inferentialism. They find them both wanting and submit that their own preferred option, the so-called DEKI account, not only eschews the problems that beset these conceptions, but further provides a comprehensive answer to the question of how scientific representation works. This Element is also available in the Open Access on Cambridge Core.
(2020) Modelling Nature: An Opinionated Introduction to Scientific Representation (with Roman Frigg), Synthese Library - Studies in Epistemology, Logic, Methodology, and Philosophy of Science, Cham: Springer Blurb
This monograph offers a critical introduction to current theories of how scientific models represent their target systems. Representation is important because it allows scientists to study a model to discover features of reality. The authors provide a map of the conceptual landscape surrounding the issue of scientific representation, arguing that it consists of multiple intertwined problems. They provide an encyclopaedic overview of existing attempts to answer these questions, and they assess their strengths and weaknesses. The book also presents a comprehensive statement of their alternative proposal, the DEKI account of representation, which they have developed over the last few years. They show how the account works in the case of material as well as non-material models; how it accommodates the use of mathematics in scientific modelling; and how it sheds light on the relation between representation in science and art. The issue of representation has generated a sizeable literature, which has been growing fast in particular over the last decade. This makes it hard for novices to get a handle on the topic because so far there is no book-length introduction that would guide them through the discussion. Likewise, researchers may require a comprehensive review that they can refer to for critical evaluations. This book meets the needs of both groups.
I argue that fictional models, construed as models that misrepresent certain ontological aspects of their target systems, can nevertheless explain why the latter exhibit certain behaviour. They can do this by accurately representing whatever it is that that behaviour counterfactually depends on. However, we should be sufficiently sensitive to different explanatory questions, i.e., `why does certain behaviour occur?' vs. `why does the counterfactual dependency invoked to answer that question actually hold?'. With this distinction in mind, I argue that whilst fictional models can answer the first sort of question, they do so in an unmysterious way (contra to what one might initially think about such models). Moreover, I claim that the second question poses a dilemma for the defender of the idea that fictions can explain: either these models cannot answer these sorts of explanatory questions, precisely because they are fictional; or they can, but in a way that requires reinterpreting them such that they end up accurately representing the ontological basis of the counterfactual dependency, i.e., reinterpreting them so as to rob them of their fictional status. Thus, the existence of explanatory fictions does not put pressure on the idea that accurate representation of some aspect of a target system is a necessary condition on explaining that aspect.
(2021) Mathematics is not the only Language in the Book of Nature (with Roman Frigg), Synthese, 198, pp. S5941-S5962Abstract
How does mathematics apply to something non-mathematical? We distinguish between a general application problem and a special application problem. A critical examination of the answer that structural mapping accounts offer to the former problem leads us to identify a lacuna in these accounts: they have to presuppose that target systems are structured and yet leave this presupposition unexplained. We propose to fill this gap with an account that attributes structures to targets through structure generating descriptions. These descriptions are physical descriptions and so there is no such thing as a solely mathematical account of a target system.
Veritism, the position that truth, or accuracy, is necessary for epistemic acceptability, seems to be in tension with the observation that much of our best science is not, strictly speaking, true when interpreted literally. This generates a paradox: (i) truth is necessary for epistemic acceptability; (ii) the claims of science have to be taken literally; (iii) much of what science produces is not literally true and yet it is acceptable. We frame Elgin’s project in True Enough as being motivated by, and offering a particular resolution to, this paradox. We discuss the paradox with a particular focus on scientific models and argue that there is another resolution available which is compatible with retaining veritism: rejecting the idea that scientific models should be interpreted literally.
(2020) Judgement Aggregation in Scientific Collaborations: The Case for Waiving Expertise (with Alexandru Marcoci), Studies in History and Philosophy of Science, 84, pp. 66-74Abstract
The fragmentation of academic disciplines forces individuals to specialise. In doing so, they become experts over their narrow area of research. However, ambitious scientific projects, such as the search for gravitational waves, require them to come together and collaborate across disciplinary borders. How should scientists with expertise in different disciplines treat each others' expert claims? An intuitive answer is that the collaboration should defer to the opinions of experts. In this paper we show that under certain seemingly innocuous assumptions, this intuitive answer gives rise to an impossibility result when it comes to aggregating the beliefs of experts to deliver the beliefs of a collaboration as a whole. We then argue that when experts' beliefs come into conflict, they should waive their expert status.
In a series of recent papers we have developed an account of scientific representation according to which models should be viewed as fictions that represent their targets via keys. These keys provide a systematic way to move from model-features to features to be imputed to their targets. To the extent that these features are present in the latter, the representation is accurate. These keys are implicitly associated with modelling frameworks. Accordingly, to understand these frameworks we must understand the keys that accompany them. Here we analyse a key that that is crucial in many parts of physics, namely what we call the limit key. This key exploits the fact that the features exemplified by physical models are limits of the features of the target. We demonstrate how these keys allow for accurate representation in the presence of idealisation, and further illustrate how investigating them provides novel ways of approaching certain currently debated questions in the philosophy of science.
(2020) It's Not a Game: Accurate Representation with Toy Models, The British Journal for the Philosophy of Science, 71(3), pp. 1013-1041Abstract
Drawing on `interpretational' accounts of scientific representation, I argue that the use of so-called `toy models' provides no particular philosophical puzzle. More specifically; I argue that once one gives up the idea that models are accurate representations of their targets only if they are appropriately similar, then simple and highly idealized models can be accurate in the same way that more complex models can be. Their differences turn on trading precision for generality, but, if they are appropriately interpreted, toy models should nevertheless be considered accurate representations. A corollary of my discussion is a novel way of thinking about idealization more generally: idealized models may distort features of their targets, but they needn’t misrepresent them.
The idea that gauge theory has `surplus' structure poses a puzzle: in one much discussed sense, this structure is redundant; but on the other hand, it is also widely held to play an essential role in the theory. In this paper, we employ category-theoretic tools to illuminate an aspect of this puzzle. We precisify what is meant by `surplus' structure by means of functorial comparisons with equivalence classes of gauge fields, and then show that such structure is essential for any theory that represents a rich collection of physically relevant fields which are `local' in nature.
(2019) The Limitations of the Arrovian Consistency of Domains with a Fixed Preference, Theory and Decision, 87(2), pp. 183-199Abstract
In this paper I investigate the properties of social welfare functions defined on domains where the preferences of one agent remain fixed. Such domains are a degenerate case of those investigated, and proved Arrow consistent, by Sakai and Shimoji (2006). Thus they admit functions from them to a social preference that satisfy Arrow's conditions of Weak Pareto, Independence of Irrelevant Alternatives, and Non-Dictatorship. However, I prove that according to any function that satisfies these conditions on such a domain, for any triple of alternatives, if the agent with the fixed preferences does not determine the social preference on any pair of them, then some other agent determines the social preference on the entire triple.
Kuhn argued that scientific theory choice is, in some sense, a rational matter, but one that is not fully determined by shared objective scientific virtues like accuracy, simplicity, and scope. Okasha imports Arrow's impossibility theorem into the context of theory choice to show that rather than not fully determining theory choice, these virtues cannot determine it at all. If Okasha is right, then there is no function (satisfying certain desirable conditions) from `preference' rankings supplied by scientific virtues over competing theories (or models, or hypotheses) to a single all-things-considered ranking. This threatens the rationality of science. In this paper we show that if Kuhn's claims about the role that subjective elements play in theory choice are taken seriously, then the threat dissolves.
(2018) The Turn of the Valve: Representing with Material Models (with Roman Frigg), European Journal for Philosophy of Science, 8(2), pp. 205-224Abstract
Many scientific models are representations. Building on Goodman and Elgin's notion of representation-as we analyse what this claim involves by providing a general definition of what makes something a scientific model, and formulating a novel account of how they represent. We call the result the DEKI account of representation, which offers a complex kind of representation involving an interplay of, denotation, exemplification, keying up of properties, and imputation. Throughout we focus on material models, and we illustrate our claims with the Phillips-Newlyn machine. In the conclusion we suggest that, mutatis mutandis, the DEKI account can be carried over to other kinds of models, notably fictional and mathematical models.
(2017) Scientific Representation and Theoretical Equivalence, Philosophy of Science, 84(5), pp. 982-995Abstract
In this paper I fruitfully connect two debates in the philosophy of science; the questions of scientific representation and model, and theoretical, equivalence. I argue that by paying attention to how a model is used to draw inferences about its target system, we can define a notion of theoretical equivalence that turns on whether their models licence the same inferences about the same target systems. I briefly consider the implications this has with respect to two questions that have recently been discussed in the context of the formal philosophy of science.
(2016) On the Pragmatic Equivalence between Representing Data and Phenomena, Philosophy of Science, 83(2), pp. 171-191 Abstract
I investigate van Fraassen's claim that, for a given scientist, in a given context, there is no pragmatic difference between taking a model to accurately represent a target system (a physical system out there in the world) and a data model (a mathematical object extracted from that system). I reconstruct van Fraassen's argument for this claim before demonstrating that it turns on the false premise that an act of representing that P commits the representer to the belief that P. So van Fraassen's claim that denying that models represent target systems would result in an instance of Moore's paradox fails. Unlike assertion, acts of representation fail to generate any doxastic commitments.
(This paper won me the Popper Prize for distinguished work by a graduate student in the philosophy department at the LSE.)
In this paper we explore the constraints that our preferred account of scientific representation places on the ontology of scientific models. Pace the Direct Representation view associated with Arnon Levy and Adam Toon we argue that scientific models should be thought of as fictional imagined systems, and clarify the relationship between imagination and representation.
(Forthcoming) Maps, Models, and Representation (with Roman Frigg), in I. Lawer, K. Khalifa, and E. Shech (eds.) Scientific Understanding and Representation: Modeling in the Physical Sciences, London: RoutledgeAbstract
Maps are often invoked as a way to understanding scientific modelling: a model represents its target as a map represents its territory. However, without an account of how maps represent this analogy is suggestive at best. We reverse the direction of explanation and show that maps represent like models. Utilising the DEKI account of representation, we provide an account of cartographic representation. This shows that maps and models indeed represent in the same manner, and it provides insight into two areas of philosophical inquiry, namely the nature of representational accuracy and the purpose relativity and historical situatedness of representations.
(Forthcoming) DEKI and the Mislocation of Justification: A Response to Millson and Risjord (with Roman Frigg), in I. Lawer, K. Khalifa, and E. Shech (eds.) Scientific Understanding and Representation: Modeling in the Physical Sciences, London: RoutledgeAbstract
In their “DEKI, Denotation, and the Fortuitous Misuse of Maps” Jared Millson and Mark Risjord take the DEKI account to task for being unable to “distinguish justified surrogative inferences from unjustified ones”, which is problem because an analysis of representation “must block unjustified surrogative inferences” (p. 5). This, they say, means that DEKI fails to meet our own Surrogative Reasoning Condition. In this note we respond to this criticism and defend our the DEKI account.
This entry explores how scientific models represent what is possible; how they are justified; and their functions. Two different approaches to these issues are discussed: the first concerns models “embedded” in a well-confirmed theory; the second concerns models not so embedded. When the former models represent possibilities, they provide us with information about what is possible according to the theory in which they are embedded. They are justified to the extent that the theory is actually confirmed. Models of the second kind cannot rely on theoretical support in the same way. Yet, scientists regularly extract modal information from such models, and they hold some epistemic merit. What could underwrite their justification is explored. Finally, various functions played by scientific models that represent possibilities are considered.
We present a detailed statement and defense of the fiction view of scientific models, according to which they are akin to the characters and places of literary fiction. We argue that while some of the criticisms this view has attracted raise legitimate points, others are myths. In this chapter, we first identify and then rebut the following seven myths: (1) that the fiction view regards products of science as falsehoods; (2) that the fiction view holds that models are data-free; (3) that the fiction view is antithetical to representation; (4) that the fiction view trivializes epistemology; (5) that the fiction view cannot account for the use of mathematics in the modeling; (6) that the fiction view misconstrues the function of models in the scientific process; and (7) that the fiction view stands on the wrong side of politics. As a result, we conclude that the fiction view of models, suitably understood (as an account of the ontology of models, rather than their function), remains a viable position.
Many models function representationally. Considerable differences notwithstanding, most accounts of representation involve the notion that models denote their targets. Denotation is a dyadic relation that obtains between certain symbols and certain objects. This does not sit well with the fact that many models are not concrete objects. If a model does not exist,how can it denote? We present an antirealist theory of models that reconciles the notion that models don’t exist with the claim that there is real denotation between models and their targets.
(2017) Of Barrels and Pipes: Representation As in Art and Science (with Roman Frigg), in O. Bueno, G. Darby, S. French, and D. Rickles (eds.) Thinking about Science and Reflecting on Art: Bringing Aesthetics and the Philosophy of Science Together, London and New York: Routledge, pp. 41-61
We discuss what scientific representation and artistic representation have in common, and how they differ.
We provide an extensive overview of the recent literature concerning scientific representation.
(2017) Scientific Representation is Representation as (with Roman Frigg), in H-K. Chao and J. Reiss (eds.) Philosophy of Science in Practice: Nancy Cartwright and the Nature of Scientific Reasoning, Cham: Springer, pp. 149-179 Abstract
Nelson Goodman distinguished between the notions of representation-of and representation-as. The former is bare denotation, akin to the relationship between a proper name and its bearer. The latter can be informative: the representation may be used to learn about the target. We propose a framework in which to understand how scientific representation is a specific case of representation-as.
In a recent paper, Okasha imports Arrow's impossibility theorem into the context of theory choice. He shows that there is no function (satisfying certain desirable conditions) from profiles of preference rankings over competing theories, models or hypotheses provided by scientific virtues to a single all-things-considered ranking. This is a prima facie threat to the rationality of theory choice. In this paper we show this threat relies on an all-or-nothing understanding of scientific rationality and articulate instead a notion of rationality by degrees. The move from all-or-nothing rationality to rationality by degrees will allow us to argue that theory choice can be rational enough.
We provide a concise overview of the recent literature concerning scientific representation.
I have designed and taught courses in the philosophy of science and aesthetics at the undergraduate and graduate levels, and various graduate level courses on topics in the philosophy of science (including external examination of a PhD). I have also served as an `Hours' mentor for an MFA student, with whom I organised a directed reading on Representation in Science and Art.
During my time at the LSE I was a teaching assistant for the following courses.
|Philosophy of Science||Philosophy of Biology|
I am comfortable teaching introduction to philosophy, philosophy of science, aesthetics and the philosophy of art, philosophy of language, history of analytic philosophy, and introductory logic and/or formal methods for philosophers.
In the academic year 2015-2016 I won the Teaching Prize for excellent class teaching from the Department of Philosophy, Logic and Scientific Method at the LSE (in the 2014-2015 academic year I was awarded an honourable commendation).
|British Academy Rising Star Engagement Award for a project on Epistemological Pluralism. Events were held at Senate House in the 2019-2020 academic year.|
|Jeffrey Rubinoff Sculpture Park Postdoctoral Award provides support for a project on the cognitive value of art.|
|"Modelling and Idealisation in the Philosophy of Science", Idealization and Modelling in Science and the Aristotelian Tradition, Pontifical University of St. Thomas Aquinas (Angelicum), Rome, 19-20/10/21.|
|"Learning From (Literary) Fiction", First Workshop on the Aesthetics of Scientific Experiments, Cambridge (online), 25/6/21.|
|"Learning From (Literary) Fiction", Colloquium, Institue of Philosophy, Bern (online), 10/12/20.|
|"Why (At Least Some) Idealisations Aren't False", FraMEPhys Workshop on Idealised Models, Birmingham (online), 6/10/20.|
|"How Fictions Explain", History and Philosophy of Science Seminar, Leeds (online), 20/5/20.|
|"Modelling and Mathematics", Mathematics and its Applications: Philosophical Issues, Leeds, 23-24/9/19.|
|"The Future of Philosophy of Science" (ECR Roundtable), Philosophy of Science Today, LSE, 19/9/19.|
|"Non-Literal Model Interpretations", CamPoS, Cambridge, 6/2/19 .|
|"Interpreting Models: A Suggestion and its Payoffs", Oxford Philosophy of Physics Thursday Seminar, Oxford, 22/11/18.|
|"Idealisation, Abstraction, and (Mis)Representation", PSA 2018, Seattle, 1-4/11/18.|
|"Data, Models, and Data Models", Lakatos Award Expert Workshop, LSE, 25/10/18.|
|"It's not a Game: Accurate Representation with Toy Models", Models and Simulations 8, South Carolina, 15-17/3/18.|
|"Scientific Consensus without Inconsistency", APA Central Division 2018, Chicago, 21-24/2/18.|
|"It's not a Game: Accurate Representation with Toy Models", TINT Workshop on Highly Unrealistic Models, Helsinki, 12-13/10/17.|
|"Scientific Consensus without Inconsistency", BSPS 2017, Edinburgh, 13-14/7/17.|
|"Mathematics is not the only Language in the Book of Nature", Models and Explanations in Economics 2017, Rostock, 1/6/17 - 2/7/17.|
|"Scientific Representation and Theoretical Equivalence", PSA 2016, Atlanta, 3-5/11/16.|
|"Arrovian Consistency of Domains with a Fixed Preference", Central European Program in Economic Theory, Udine, 23-24/6/16.|
|"Models, Fictions, and Scientific Representation", Models and Explanations in Economics 2016, Innsbruck, 10-12/6/16.|
|"From Hydraulic Machines to Immortal Rabbits", Models and Simulations 7, Barcelona, 18-16/5/16.|
|"Moving Beyond Arrow’s Theorem: Social Choice and Theory Choice", Choice Group, London, 4/5/16.|