Research on interdisciplinary science has for the most part concentrated on the institutional obstacles that discourage or hamper interdisciplinary work, with the expectation that interdisciplinary interaction can be improved through institutional reform strategies such as through reform of peer review systems. However institutional obstacles are not the only ones that confront interdisciplinary work. The design of policy strategies would benefit from more detailed investigation into the particular cognitive constraints, including the methodological and conceptual barriers, which also confront attempts to work (...) across disciplinary boundaries. Lessons from cognitive science and anthropological studies of labs in sociology of science suggest that scientific practices may be very domain specific, where domain specificity is an essential aspect of science that enables researchers to solve complex problems in a cognitively manageable way. The limit or extent of domain specificity in scientific practice, and how it constrains interdisciplinary research, is not yet fully understood, which attests to an important role for philosophers of science in the study of interdisciplinary science. This paper draws upon two cases of interdisciplinary collaboration; those between ecologists and economists, and those between molecular biologists and systems biologists, to illustrate some of the cognitive barriers which have contributed to failures and difficulties of interactions between these fields. Each exemplify some aspect of domain specificity in scientific practice and show how such specificity may constrain interdisciplinary work. (shrink)

Sustainability science seeks to extend scientific investigation into domains characterized by a distinct problem-solving agenda, physical and social complexity, and complex moral and ethical landscapes. In this endeavor it arguably pushes scientific investigation beyond its usual comfort zones, raising fundamental issues about how best to structure such investigation. Philosophers of science have long scrutinized the structure of science and scientific practices, and the conditions under which they operate effectively. We propose a critical engagement between sustainability scientists and philosophers of science (...) with respect to how to engage in scientific activity in these complex domains. We identify specific issues philosophers of science raise concerning current sustainability science and the contributions philosophers can make to resolving them. In conclusion we reflect on the steps philosophers of science could take to advance sustainability science. (shrink)

In this paper we take a close look at current interdisciplinary modeling practices in the environmental sciences, and suggest that closer attention needs to be paid to the nature of scientific practices when investigating and planning interdisciplinarity. While interdisciplinarity is often portrayed as a medium of novel and transformative methodological work, current modeling strategies in the environmental sciences are conservative, avoiding methodological conflict, while confining interdisciplinary interactions to a relatively small set of pre-existing modeling frameworks and strategies (a process we (...) call crystallization). We argue that such practices can be rationalized as responses in part to cognitive constraints which restrict interdisciplinary work. We identify four salient integrative modeling strategies in environmental sciences, and argue that this crystallization, while contradicting somewhat the novel goals many have for interdisciplinarity, makes sense when considered in the light of common disciplinary practices and cognitive constraints. These results provide cause to rethink in more concrete methodological terms what interdisciplinarity amounts to, and what kinds of interdisciplinarity are obtainable in the environmental sciences and elsewhere. (shrink)

In this article, we provide a case study examining how integrative systems biologists build simulation models in the absence of a theoretical base. Lacking theoretical starting points, integrative systems biology researchers rely cognitively on the model-building process to disentangle and understand complex biochemical systems. They build simulations from the ground up in a nest-like fashion, by pulling together information and techniques from a variety of possible sources and experimenting with different structures in order to discover a stable, robust result. Finally, (...) we analyze the alternative role and meaning theory has in systems biology expressed as canonical template theories like Biochemical Systems Theory. (shrink)

The importation of computational methods into biology is generating novel methodological strategies for managing complexity which philosophers are only just starting to explore and elaborate. This paper aims to enrich our understanding of methodology in integrative systems biology, which is developing novel epistemic and cognitive strategies for managing complex problem-solving tasks. We illustrate this through developing a case study of a bimodal researcher from our ethnographic investigation of two systems biology research labs. The researcher constructed models of metabolic and cell-signaling (...) pathways by conducting her own wet-lab experimentation while building simulation models. We show how this coupling of experiment and simulation enabled her to build and validate her models and also triangulate and localize errors and uncertainties in them. This method can be contrasted with the unimodal modeling strategy in systems biology which relies more on mathematical or algorithmic methods to reduce complexity. We discuss the relative affordances and limitations of these strategies, which represent distinct opinions in the field about how to handle the investigation of complex biological systems. (shrink)

Integrative systems biology is an emerging field that attempts to integrate computation, applied mathematics, engineering concepts and methods, and biological experimentation in order to model large-scale complex biochemical networks. The field is thus an important contemporary instance of an interdisciplinary approach to solving complex problems. Interdisciplinary science is a recent topic in the philosophy of science. Determining what is philosophically important and distinct about interdisciplinary practices requires detailed accounts of problem-solving practices that attempt to understand how specific practices address the (...) challenges and constraints of interdisciplinary research in different contexts. In this paper we draw from our 5-year empirical ethnographic study of two systems biology labs and their collaborations with experimental biologists to analyze a significant problem-solving approach in ISB, which we call adaptive problem solving. ISB lacks much of the methodological and theoretical resources usually found in disciplines in the natural sciences, such as methodological frameworks that prescribe reliable model-building processes. Researchers in our labs compensate for the lack of these and for the complexity of their problems by using a range of heuristics and experimenting with multiple methods and concepts from the background fields available to them. Using these resources researchers search out good techniques and practices for transforming intractable problems into potentially solvable ones. The relative freedom lab directors grant their researchers to explore methodological options and find good practices that suit their problems is not only a response to the complex interdisciplinary nature of the specific problem, but also provides the field itself with an opportunity to discover more general methodological approaches and develop theories of biological systems. Such developments in turn can help to establish the field as an identifiably distinct and successful approach to understanding biological systems. (shrink)

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to (...) handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations. (shrink)

This article, which is intended both as a position paper in the philosophical debate on natural kinds and as the guest editorial to this thematic issue, takes up the challenge posed by Ian Hacking in his paper, “Natural Kinds: Rosy Dawn, Scholastic Twilight.” Whereas a straightforward interpretation of that paper suggests that according to Hacking the concept of natural kinds should be abandoned, both in the philosophy of science and in philosophy more generally, we suggest that an alternative and less (...) fatalistic reading is also possible. We argue that abandoning the concept of natural kinds would be premature, as it still can do important work. Our concern is with the situation in the (philosophy of the) life sciences. Against the background of this concern we attempt to set something of an agenda for future research on the topic of natural kinds in the (philosophy of the) life sciences. (shrink)

Really statistical explanation is a hitherto neglected form of noncausal scientific explanation. Explanations in population biology that appeal to drift are RS explanations. An RS explanation supplies a kind of understanding that a causal explanation of the same result cannot supply. Roughly speaking, an RS explanation shows the result to be mere statistical fallout.

Interdisciplinarity is widely considered necessary to solving many contemporary problems, and new funding structures and instruments have been created to encourage interdisciplinary research at universities. In this article, we study a small technical university specializing in green technology which implemented a strategy aimed at promoting and developing interdisciplinary collaboration. It did so by reallocating its internal research funds for at least five years to “research platforms” that required researchers from at least two of the three schools within the university to (...) participate. Using data from semi-structured interviews from researchers in three of these platforms, we identify specific tensions that the strategy has generated in this case: in the allocation of platform resources, in the division of labor and disciplinary relations, in choices over scientific output and academic careers. We further show how the particular platform format exacerbates the identified tensions in our case. We suggest that certain features of the current platform policy incentivize shallow interdisciplinary interactions, highlighting potential limits on the value of attempting to push for interdisciplinarity through internal funding. (shrink)

We lay groundwork for applying ethnographic methods in philosophy of science. We frame our analysis in terms of two tasks: to identify the benefits of an ethnographic approach in philosophy of science and to structure an ethnographic approach for philosophical investigation best adapted to provide information relevant to philosophical interests and epistemic values. To this end, we advocate for a purpose-guided form of cognitive ethnography that mediates between the explanatory and normative interests of philosophy of science, while maintaining openness and (...) independence when framing such an investigation to achieve robust unbiased results. (shrink)

Integrative systems biology is among the most innovative fields of contemporary science, bringing together scientists from a range of diverse backgrounds and disciplines to tackle biological complexity through computational and mathematical modeling. The result is a plethora of problem-solving techniques, theoretical perspectives, lab-structures and organizations, and identity labels that have made it difficult for commentators to pin down precisely what systems biology is, philosophically or sociologically. In this paper, through the ethnographic investigation of two ISB laboratories, we explore the particular (...) structural features of ISB thinking and organization and its relations to other disciplines that necessitate cognitive innovation at all levels from lab PI’s to individual researchers. We find that systems biologists face numerous constraints that make the production of models far from straight-forward, while at the same time they inhabit largely unstructured task environments in comparison to other fields. We refer to these environments as adaptive problem spaces. These environments they handle by relying substantially on the flexibility and affordances of model-based reasoning to integrate these various constraints and find novel adaptive solutions. Ultimately what is driving this innovation is a determination to construct new cognitive niches in the form of functional model building frameworks that integrate systems biology within the biological sciences. The result is an industry of diverse and different innovative practices and solutions to the problem of modeling complex, large-scale biological systems. (shrink)

Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must meet to achieve these objectives as predictive robustness—predictive reliability over large domains. Drawing on the results of an ethnographic investigation and various studies in the systems biology literature, I explore four current obstacles to (...) achieving predictive robustness; data constraints, parameter uncertainty, collaborative constraints and system-scale requirements. I use a case study and the commentary of systems biologists themselves to show that current practices in the field, rather than pursuing these goals, frequently use models heuristically to investigate and build understanding of biological systems that do not meet standards of predictive robustness but are nonetheless effective uses of computation. A more heuristic conception of modeling allows us to interpret current practices as ways that manage these obstacles more effectively, particularly collaborative constraints, such that modelers can in the long-run at least work towards prediction and control. (shrink)

Interdisciplinarity is one of the most prominent ideas driving science and research policy today.1 It is applied widely as a conception of what particularly creative and socially relevant research processes should consist of, whether in the natural sciences, the social sciences, the humanities, or elsewhere. Its advocates, many of whom are located in current science and research administration themselves, are using ideas of interdisciplinarity to reshape university organization and research funding. For the last 40 years, researchers studying interdisciplinarity have built (...) up a substantial body of literature constructing various visions of what it should be and how to taxonomize the different forms it can... (shrink)

The aim of this article is to detail some reservations against the beliefs, claims, or presuppositions that current essentialist natural kind concepts (including homeostatic property cluster kinds) model grouping practices in the life sciences accurately and generally. Such concepts fit reasoning into particular preconceived epistemic and semantic patterns. The ability of these patterns to fit scientific practice is often argued in support of homeostatic property cluster accounts, yet there are reasons to think that in the life sciences kind concepts exhibit (...) a diversity of grouping practices that are flattened out by conceptualizing them as natural kinds. Instead this article argues that the process of understanding grouping practices needs to start from a more neutral position independent of any ontological account. Following Love (Acta Biotheor 57:51–75, 2009) this paper suggests that typical natural kind concepts should be broached in the first place as grouping strategies that use a variety of semantic and epistemic tactics to apply group-bound information to tasks of explanation and understanding. (shrink)

We argue that many recent philosophical discussions about the reference of everyday concepts of intentional states have implicitly been predicated on descriptive theories of reference. To rectify this, we attempt to demonstrate how a causal theory can be applied to intentional concepts. Specifically, we argue that some phenomena in early social de- velopment ðe.g., mimicry, gaze following, and emotional contagionÞ can serve as refer- ence fixers that enable children to track others’ intentional states and, thus, to refer to those states. (...) This allows intentional concepts to be anchored to their referents, even if folk psy- chological descriptions turn out to be false. (shrink)

Interdisciplinarity has become a dominant research policy imperative1 – exercised by European Research Council and other funding agencies at different scales – and a substantial topic in science studies fields outside philosophy of science, including science education, research management (particularly team management) and scientometrics. Philosophers of science have only recently begun to dedicate more attention to this feature of contemporary science. The present collection of studies aspires to promote this line of philosophical inquiry in terms of case studies on various (...) aspects of interdisciplinarity in science, and to bring philosophical concepts and principles to bear in its analysis. While much current philosophical work has focused on the possibility of conceptual and methodological unification and integration amongst specific fields, we aim to widen the scope of philosophical treatment of this issue by mapping out the broader landscape of philosophical issues that emerge from interdisciplinary interactions, and by identifying the points where philosophical analysis can make important and relevant contributions. (shrink)

Many of the current comparisons of taxic phylogenetic and biological homology in the context of morphology focus on what are seen as categorical distinctions between the two concepts. The first, it is claimed, identifies historical patterns of conservation and variation relating taxa; the second provides a causal framework for the explanation of this conservation and variation. This leads to the conclusion that the two need not be placed in conflict and are in fact compatible, having non-competing epistemic purposes or mapping (...) the same extensions in the form of monophyletic groupings (see Roth, The biological basis of homology 1–26, 1988; Sluys, J Zool Syst Evol Res 34:145–152, 1996; Abouheif, Trends Ecol Evol 12:405–408, 1997; Brigandt, J Exp Zool 299:9–17, 2003, Biol Philos 22:709–725, 2007; Assis and Brigandt, Evol Biol 36:248–255, 2009). This article argues that moves in this direction miss the essential disagreement between these concepts as they have been developed in the context of the debate concerning the best concept for evolutionary investigation. We should rather see these concepts employing a common fundamental methodological approach to homology, but disagreeing about how to apply the methodology effectively. Both concepts employ class reasoning, which pursues homologies as units of generalization—more precisely, as sources of reliable and relevant group-bound information in the form of shared underlying causes. The dispute can be better understood by two poles that structure such reasoning: the need for a reliable basis for projections about the causal history of shared structures, and the desire to identify homologous characters with more informative and specific causal information relevant to generalizing about evolutionary processes. Judgments in favor of one or the other in turn have affected the scope or extension of these competing homology concepts. (shrink)

Participatory modeling in sustainability science allows scientists to take stakeholders’ interests, knowledge and values into account when designing a model-based solution to a sustainability problem, by incorporating stakeholders in the model-building process. This improves the chance of generating socially robust knowledge and consensus on solutions. Part of what helps in this regard is that scientists, through involving stakeholders, limit their own values from influencing the outcome, thus achieving some level of value-neutrality. We argue that while it might achieve this to (...) some extent, it comes at a cost to the reliability of the outcomes, which is ethically problematic. (shrink)

Modelers are tackling ever more complex systems with the aid of computation. Model-based inferences can play a key role in their ability to handle complexity and produce reliable or informative models. We study here the role of model-based inference in the modern field of computational systems biology. We illustrate how these inferences operate and analyze the material and theoretical bases or conditions underlying their effectiveness. Our investigation reiterates the significance and centrality of model-based reasoning in day-to-day problem-solving practices, and the (...) role that debugging processes of partial or incomplete models can play in scientific inference and scientific discovery, particularly with respect to complex systems. We present several deeper implications such an analysis has for philosophy of science regarding the role of models in scientific practice. (shrink)

Philosophy of science is a rapidly evolving and increasingly inclusive academic field. It is one of the most dynamic branches of philosophy. However, for the most part, philosophy of science has been taught historically by recounting and tracing through discussions and debates from the early to late twentieth century. Great texts of positivism, instrumentalism, demarcation, falsification, paradigm shifts, realism, observation and so on are handed out to students and critically assessed. There is something rather puzzling about this way of teaching (...) philosophy of science, since contemporary philosophy of science has moved on significantly and is arguably distinct from the historical subject, just as current science has many features different from the science of Kuhn’s and Popper’s day. Such philosophical work, however, often only seems to make a late appearance in such courses. It often seems that we teach philosophy of science solely as an exercise in point and refutation, leaving stu .. (shrink)

Many philosophers of science think scientific practice can benefit from philosophical concepts, and as such philosophy of science should play a direct role in science and engineering education. In this paper we consider a highly integrative course design strategy for integrating philosophy of science in specific disciplinary educational programmes through adaptation, operationalization and embedding of philosophy of science material to fit both the scientific and educational structure of a programme. The goal of the strategy is to help encourage students to (...) recognize the value of philosophical concepts to scientific decision making and to apply them in their own scientific practice. We use the example of a 7.5 ECTS civil engineering course which implements this design at a European technical university, to elaborate these concepts, and present some evidence on how students receive the course. We discuss some of challenges and limitations of implementing this kind of strategy for teaching philosophy of science. (shrink)

In 1966 Richard Levins argued that applications of mathematics to population biology faced various constraints which forced mathematical modelers to trade-off at least one of realism, precision, or generality in their approach. Much traditional mathematical modeling in biology has prioritized generality and precision in the place of realism through strategies of idealization and simplification. This has at times created tensions with experimental biologists. The past 20 years however has seen an explosion in mathematical modeling of biological systems with the rise (...) of modern computational systems biology and many new collaborations between modelers and experimenters. In this paper I argue that many of these collaborations revolve around detail-driven modeling practices which in Levins’ terms trade-off generality for realism and precision. These practices apply mathematics by working from detailed accounts of biological systems, rather than from initially idealized or simplified representations. This is possible by virtue of modern computation. The form these practices take today suggest however Levins’ constraints on mathematical application no longer apply, transforming our understanding of what is possible with mathematics in biology. Further the engagement with realism and the ability to push realistic models in new directions aligns well with the epistemological and methodological views of experimenters, which helps explain their increased enthusiasm for biological modeling. (shrink)

Large-scale book-length treatises on natural kinds are rather few compared to the amount of discussion on the subject and not since Brian Ellis’ Scientific Essentialism perhaps has anyone attempted to build a philosophical “world view” around a theory of natural kinds. Most discussion about natural kinds of the last decade has restricted itself to specific issues, such as the species debate or chemical kinds, or, as in the case of LaPorte (2009), the semantic practices surrounding kind concepts and conceptual change. (...) In this respect, Magnus’ Scientific Enquiry and Natural Kinds: From Planets to Mallards has a worthy ambition, not least for displaying how rich and informative natural kind discussion can be despite its recent characterization as a philosophical dead end (see Hacking 2007). This holds even more so because Magnus attempts this within the context of a highly naturalistic account of natural kinds, rather than a metaphysically driven approach already predisposed to metaphysic .. (shrink)

Re-Engineering Philosophy for Limited Beings is about new approaches to many of the big topics in philosophy of science today, but with a very different take. To begin with, we are urged to reject the received Cartesian-Laplacean myths: Descartes’ certainty and Laplace’s computational omniscience. Instead, Wimsatt re-engineers a philosophy for human beings with all their cognitive limitations. His approaches find their starting point in the actual practices of scientists themselves, which he strongly identifies with engineering practices as the source of (...) researchers’ solutions for dealing with a complex world. He aims to construct an understanding of scientific methodology around the central role of reduction. But he dismisses eliminative reductionism in favor of a heuristic-based realist view. Wimsatt’s world is a complex one, and this means that science needs to do away with all the absolute and simple answers, because they do not reflect the world we are living in. A complex world requires the mindset and tinkering of an engineer to uncover its reality. The appropriate response must be heuristics all the way down as we constantly seek out reliable inferences on often shifting ground. To this end, we aim for models and theories that are robust, just as engineers aim to build robust machines. And although errors occur and approaches are fallible, they allow us to continually adapt the heuristics applied and sharpen our perceptions so as to develop more refined tools for investigating and understanding the world. (shrink)

In this chapter we explore basic mathematical and other constraints which limit the often novel uses of computation employed in modern computational system biology. These constraints generate substantial obstacles for one goal prominent in the field; namely, the goal of producing models valid for predictive uses in clinical and other contexts. However on closer examination many applications of computation and simulation in the field have more pragmatic or investigative goals in mind, suggesting an important role for rationalizing uses of computation (...) in systems biology and elsewhere as investigative tools. We discuss the concept of an “investigative tool”, and what insights it might offer our understanding of modern computational strategies and the bases for them. (shrink)