Tarja Knuuttila

This chapter discusses the question of how models and theories relate. Its main contention is that there is no such thing as “the” relation between models and theories: models can stand in a multiplicity of relations to theories. After a brief review of how the relation between models and theories is analysed in the Syntactic View and the Semantic View of theories, the chapter discusses cases in which models are independent from theories, designed to explore properties of theories, live in a symbiotic relation with theories by completing them, aid the application of abstract theories to concrete target systems, and serve as mediators between theory and the world. The chapter then discusses where the line between models and theories should be drawn, and it ends with a short conclusion.

This chapter discusses the question of how models and theories relate. Its main contention is that there is no such thing as “the” relation between models and theories: models can stand in a multiplicity of relations to theories. After a brief review of how the relation between models and theories is analysed in the Syntactic View and the Semantic View of theories, the chapter discusses cases in which models are independent from theories, designed to explore properties of theories, live in a symbiotic relation with theories by completing them, aid the application of abstract theories to concrete target systems, and serve as mediators between theory and the world. The chapter then discusses where the line between models and theories should be drawn, and it ends with a short conclusion.

Deidealization as a topic in its own right has attracted remarkably little philosophical interest despite the extensive literature on idealization. One reason for this is the often implicit assumption that idealization and deidealization are, potentially at least, reversible processes. We question this assumption by analyzing the challenges of deidealization within a menu of four broad categories: deidealizing as recomposing, deidealizing as reformulating, deidealizing as concretizing, and deidealizing as situating. On closer inspection, models turn out much more inflexible than the reversal thesis would have us believe, and deidealization emerges as a creative part of modeling.

This paper discusses modeling from the artifactual perspective. The artifactual approach conceives models as erotetic devices. They are purpose-built systems of dependencies that are constrained in view of answering a pending scientific question, motivated by theoretical or empirical considerations. In treating models as artifacts, the artifactual approach is able to address the various languages of sciences that are overlooked by the traditional accounts that concentrate on the relationship of representation in an abstract and general manner. In contrast, the artifactual approach focuses on epistemic affordances of different kinds of external representational and other tools employed in model construction. In doing so, the artifactual account gives a unified treatment of different model types as it circumvents the tendency of the fictional and other representational approaches to separate model systems from their “model descriptions.”

How do philosophers of science make use of historical case studies? Are their accounts of historical cases purpose-built and lacking in evidential strength as a result of putting forth and discussing philosophical positions? We will study these questions through the examination of three different philosophical case studies. All of them focus on modeling and on Vito Volterra, contrasting his work to that of other theoreticians. We argue that the worries concerning the evidential role of historical case studies in philosophy are partially unfounded, and the evidential and hermeneutical roles of case studies need not be played against each other. In philosophy of science, case studies are often tied to conceptual and theoretical analysis and development, rendering their evidential and theoretic/hermeneutic roles intertwined. Moreover, the problems of resituating or generalizing local knowledge are not specific to philosophy of science but commonplace in many scientific practices—which show similarities to the actual use of historical case studies by philosophers of science.

In this chapter we put the work of Finnish conceptual and environmental artist, Lauri Anttila (1938–2022), in dialogue with discussions of the practices of scientific and mathematical representation. We are interested in the counterintuitive claim that abstraction crucially involves concreteness: instead of thinking about abstraction in opposition to the concrete, we focus on the importance of concrete actions, methods, and instruments in achieving abstract representations. In particular, we study the process of abstraction as a chain of “material translations” that engages with our cognitive abilities, representational and other artefacts, and diverse epistemic contexts in various ways. To examine and illustrate abstraction as material translation, we study Anttila’s work, Homage to Werner Holmberg (1985–1986), which parodies scientific representation in seeking to render with scientific methods the landscapes in the paintings of Werner Holmberg, a Finnish landscape artist from the 19th century. We study the striking parallels between Anttila’s artwork and the writings on scientific representation by Bruno Latour and Michael Lynch. The comparison of these analyses of scientific abstraction, rendered both artistically and through scholarly discussion, allows us to widen the scope of the philosophical discussion of abstraction in science that has so far been too narrowly focused on omission.

Synthetic biology aspires toward the biologically possible by reimagining and building novel biological parts, devices, and organisms by combining well-characterized biological components. Given its combinatorial nature, we examine synthetic biology's modeling practice using David Armstrong's combinatorial theory of possibility. It approaches possibilities in terms of combining the actual elements of the world. We argue that apart from combination, two other features of Armstrong's theory are crucial for synthetic biology: structural universals and the requirement of their instantiation. The search for general design principles, which we refer to as structural universals, is one of the primary goals of understanding biological organization, natural or otherwise.

Modeling cuts across sundry scientific practices, contributing to theorizing, experimentation, prediction, measurement, scientific instrumentation, and science education. Beyond the sciences, modeling plays a crucial role in citizen engagement with science and public policy decision-making. It plays a major role in the efforts to address the huge challenges of the 21st century, including but not limited to climate change, shortage of natural resources, loss of biodiversity, and economic forecasting in increasingly unforeseeable situations. The diversity of scientific models is astounding; side-by-side mathematical and scale models, technological advances such as rapidly expanding big data, computational and synthetic approaches, and generative AI are pushing modeling toward new frontiers, redefining the epistemic agency between humans and scientific instruments. A discussion of what we can achieve through modeling, and how we should manage model-based practices, is critical for ensuring a good and responsible use of this epistemic resource. The chapters of The Routledge Handbook of Philosophy of Scientific Modeling, written by experts in various areas of the philosophy of science, seek to provide enduring philosophical insights and useful analyses for understanding modeling in its multiplicity.

Models are used to explore possibilities across all scientific fields. Climate models simulate the potential future climatic conditions under various emissions scenarios, macroeconomic models investigate the implications of various fiscal and monetary policy initiatives, and infectious diseases models study the spread of viral diseases under a range of conditions. Such modeling approaches have not gone ignored by philosophers of science, but they have only recently started to explicitly address modeling the possible. So far, the discussion has been spread across a variety of more or less isolated pockets of debate in the philosophy of science. Modeling the Possible: Perspectives from Philosophy of Science draws together these studies, focusing specifically on how various modeling practices probe possibilities and justify claims concerning them. The volume is divided into three sections, plus an introductory chapter. The introductory chapter provides a state-of-the-art survey of the discussions of modeling possibilities within philosophy of science, as well as an introduction to the book's main themes and individual papers. The three sections focus on different kinds of possibility concepts, possibility spaces, and how-possibly modeling in practical situations. The chapters contained in this volume address conceptual and theoretical issues while also presenting case studies from various scientific domains: physics, evolutionary and synthetic biology, network science, climate science, economics, and epidemiology. Essential reading for philosophers of science, epistemologists, and modelers in various scientific disciplines, Modeling the Possible is also suitable for anyone interested in model-based scientific inferences, their validity, and the policy conclusions derived from them"-- Provided by publisher.

Models are used to explore possibilities across all scientific fields. Climate models simulate the potential future climatic conditions under various emissions scenarios, macroeconomic models investigate the implications of various fiscal and monetary policy initiatives, and infectious diseases models study the spread of viral diseases under a range of conditions. Such modeling approaches have not gone ignored by philosophers of science, but they have only recently started to explicitly address modeling the possible. So far, the discussion has been spread across a variety of more or less isolated pockets of debate in the philosophy of science. Modeling the Possible: Perspectives from Philosophy of Science draws together these studies, focusing specifically on how various modeling practices probe possibilities and justify claims concerning them. The volume is divided into three sections, plus an introductory chapter. The introductory chapter provides a state-of-the-art survey of the discussions of modeling possibilities within the philosophy of science, as well as an introduction to the book’s main themes and individual papers. The three sections focus on different kinds of possibility concepts, possibility spaces, and how-possibly modeling in practical situations. The chapters contained in this volume address conceptual and theoretical issues while also presenting case studies from various scientific domains: physics, evolutionary and synthetic biology, network science, climate science, economics, and epidemiology. Essential reading for philosophers of science, epistemologists, and modelers in various scientific disciplines, Modeling the Possible is also suitable for anyone interested in model-based scientific inferences, their validity, and the policy conclusions derived from them.

Although the emerging field of synthetic biology looks back on barely a decade of development, the stakes are high. It is a multidisciplinary research field that aims at integrating the life sciences with engineering and the physical/chemical sciences. The common goal is to design and construct novel biological components, functions and systems in order to implement, in a controlled way, biological devices and production systems not necessarily found in nature. Among the many potential applications are novel drugs and pesticides, cancer treatments, biofuels, and new materials. According to the most optimistic visions, synthetic biology may thus lead to a biotechnological revolution by transforming microorganisms into ‘factories’ of sorts, which could eventually displace conventional industrial methods.

The omnipresence of the same basic equations, function forms, algorithms, and quantitative methods is one of the most spectacular characteristics of contemporary modeling practice. Recently, the emergence of the discussion of templates and template transfer has addressed this striking cross-disciplinary reach of certain mathematical forms and computational algorithms. In this paper, we develop a notion of a model template, consisting of its mathematical structure, ontology, prototypical properties and behaviors, focal conceptualizations, and the paradigmatic questions it addresses. We apply this notion to three widely disseminated and powerful model templates: the Sherrington-Kirkpatrick model of spin glasses, scale-free networks, and the Kuramoto model of synchronization. We argue that what appears to be an interdisciplinary model transfer between different domains turns out, from a broader perspective, to be the application of transdisciplinary model templates across a multitude of domains. We also point out a further feature of template-based modeling that so far has not been discussed: template entanglement. Such entanglement enhances and makes manifest the conceptual side of model templates.

This paper presents an artifactual approach to models that also addresses their fictional features. It discusses first the imaginary accounts of models and fiction that set model descriptions apart from imagined-objects, concentrating on the latter :251–268, 2010; Frigg and Nguyen in The Monist 99:225–242, 2016; Godfrey-Smith in Biol Philos 21:725–740, 2006; Philos Stud 143:101–116, 2009). While the imaginary approaches accommodate surrogative reasoning as an important characteristic of scientific modeling, they simultaneously raise difficult questions concerning how the imagined entities are related to actual representational tools, and coordinated among different scientists, and with real-world phenomena. The artifactual account focuses, in contrast, on the culturally established external representational tools that enable, embody, and extend scientific imagination and reasoning. While there are commonalities between models and fictions, it is argued that the focus should be on the fictional uses of models rather than considering models as fictions.

The growing role of universities in the knowledge economy as well as technology transfer has increasingly been conceptualized in terms of the hybridization of public academic work and private business activity. In this article, we examine the difficulties and prospects of this kind of intermingling by studying the long-term trajectories of two research groups operating in the fields of plant biotechnology and language technology. In both cases, the attempts to simultaneously pursue academic and commercial activities led to complicated boundary maintenance, which arose from the conflicting procedures and requirements of the two activities as well as from the double roles assumed by the actors involved. We, thus, argue that the construction of boundaries is not as contingent and strategic as has often been assumed but is built, instead, on the characteristic goals and tasks of the activities in question. Moreover, we suggest that the discussion on university—industry relationships and the entrepreneurial university has by and large neglected the fact that most universities are either public sector entities or tax-exempt organizations thereby being subject to strict rules and regulations that govern the ways in which they may become engaged in commercial activities. Furthermore, several other enduring cultural features, such as the university’s commitment to open scholarly communication, make the boundary between university and commerce relatively stable. As a consequence, the results of this study lend support to the thesis according to which boundaries in science are not always created at will but reflect the long history and multifaceted societal relevance of this particular institution. This in turn implies that the commodification of university research is bound to be more difficult than what the proponents of the entrepreneurial university seem to assume.

The epistemic value of models has traditionally been approached from a representational perspective. This paper argues that the artifactual approach evades the problem of accounting for representation and better accommodates the modal dimension of modeling. From an artifactual perspective, models are viewed as erotetic vehicles constrained by their construction and available representational tools. The modal dimension of modeling is approached through two case studies. The first portrays mathematical modeling in economics, while the other discusses the modeling practice of synthetic biology, which exploits and combines models in various modes and media. Neither model intends to represent any actual target system. Rather, they are constructed to study possible mechanisms through the construction of a model system with built-in dependencies.

This comment on 'Economic theories and their Dueling interpretations' questions the descriptive adequacy of the ‘sociology of economics' proposed by Gilboa, Postlewaite, Samuelson, and Schmeidler (GPSS) (2022). We ask whether economists still perceive the role of microeconomic theory as central as do GPSS. In particular, is present-day economics unified by the principles of maximising, subject to constraints and equilibrium analysis? We argue that this is not the case. GPSS’ appeal to the interpretative flexibility of economic theories appears apologetic, especially the suggestion that theories and models, which once were considered positive descriptions or predictive instruments, are now cast as analytical or methodological exercises. We conclude on a more constructive note, drawing from the recent philosophical discussion of modelling which, quite paradoxically, grants highly idealized and simplified models a more important role than GPSS appear to allow.

One striking feature of the contemporary modeling practice is its interdisciplinarity: the same function forms and equations, and mathematical and computational methods are being transferred across disciplinary boundaries. Within philosophy of science this interdisciplinary dimension of modeling has been addressed by both analogy and template-based approaches that have proceeded separately from each other. We argue that a more fully-blown account of model transfer needs both perspectives. We examine analogical reasoning and template application through a detailed case study on the transfer of the Ising model from physics into neuroscience. Our account combines the analogy and template-based approaches through the notion of a model template that highlights the conceptual side of model transfer.

New mechanical philosophy posits that explanations in the life sciences involve the decomposition of a system into its entities and their respective activities and organization that are responsible for the explanandum phenomenon. This mechanistic account of explanation has proven problematic in its application to mathematical models, leading the mechanists to suggest different ways of aligning abstract models with the mechanist program. Initially, the discussion centered on whether the Hodgkin-Huxley model is explanatory. Network models provided another complication, as they apply to a wide number of materially diverse systems. In this article, we examine the various attempts to integrate abstract models within the mechanist program, also presenting a further challenge: the Heimburg-Jackson model, which was introduced as an alternative to the Hodgkin-Huxley model. We argue that although the notion of abstraction as the omission of irrelevant mechanistic details appears to give a mechanistic solution for accommodating abstract models, this notion does not suit models whose epistemic strategy is not decompositional. As a result, the mechanist has to choose whether to dilute the mechanistic approach nearly beyond recognition or to claim that many, if not most, abstract theoretical models do not deliver mechanistic explanations, or qualify as explanatory at all.

This paper examines how scientific understanding is enhanced by virtual entities, focusing on the case of the synthetic cell. Comparing it to other virtual entities and environments in science, we argue that the synthetic cell has a virtual dimension, in that it is functionally similar to living cells, though it does not mimic any particular naturally evolved cell (nor is it constructed to do so). In being cell-like at most, the synthetic cell is akin to many other virtual objects as it is selective and only partially implemented. However, there is one important difference: it is constructed by using the same materials and, to some extent, the same kind of processes as its natural counterparts. In contrast to virtual reality, especially to that of digital entities and environments, the details of its implementation is what matters for the scientific understanding generated by the synthetic cell. We conclude by arguing for the close connection between the virtual and the artifactual.

New mechanical philosophy posits that explanations in the life sciences involve the decomposition of a system into its entities and their respective activities and organization that are responsible for the explanandum phenomenon. This mechanistic account of explanation has proven problematic in its application to mathematical models, leading the mechanists to suggest different ways of aligning abstract models with the mechanist program. Initially, the discussion centered on whether the Hodgkin-Huxley model is explanatory. Network models provided another complication, as they apply to a wide number of materially diverse systems. In this article, we examine the various attempts to integrate abstract models within the mechanist program, also presenting a further challenge: the Heimburg-Jackson model, which was introduced as an alternative to the Hodgkin-Huxley model. We argue that although the notion of abstraction as the omission of irrelevant mechanistic details appears to give a mechanistic solution for accommodating abstract models, this notion does not suit models whose epistemic strategy is not decompositional. As a result, the mechanist has to choose whether to dilute the mechanistic approach nearly beyond recognition or to claim that many, if not most, abstract theoretical models do not deliver mechanistic explanations, or qualify as explanatory at all.

Natalia Carrillo and Tarja Knuuttila claim that there are two traditions of thinking about idealization offering almost opposite views on their functioning and epistemic status. While one tradition views idealizations as epistemic deficiencies, the other one highlights the epistemic benefits of idealization. Both of them treat idealizations as deliberate misrepresentations, however. They then argue for an artifactual account of idealization, comparing it to the traditional accounts of idealization, and exemplifying it through the Hodgkin and Huxley model of the nerve impulse. From the artifactual perspective, the epistemic benefits and deficiencies introduced by idealization frequently come in a package due to the way idealization draws together different resources in model construction. Accordingly, idealization tends to be holistic in that it is not often easily attributable to some specific parts of the model. They conclude that the artifactual approach offers a unifying view into idealization in that it is able to recover several basic philosophical insights motivating both the deficiency and epistemic benefit accounts, while being simultaneously detached from the idea of distortion by misrepresentation.

Synthetic biology has a strong modal dimension that is part and parcel of its engineering agenda. In turning hypothetical biological designs into actual synthetic constructs, synthetic biologists reach towards potential biology instead of concentrating on naturally evolved organisms. We analyze synthetic biology’s goal of making biology easier to engineer through the combinatorial theory of possibility, which reduces possibility to combinations of individuals and their attributes in the actual world. While the last decades of synthetic biology explorations have shown biology to be much more difficult to engineer than originally conceived, synthetic biology has not given up its combinatorial approach.