5th Biennial Conference of the Society for Philosophy of Science in Practice (SPSP) Aarhus 2015

Parallel Session 3E
Wednesday, 24 June 2015, 15:30–17:30 in G4
Session chair: Joseph Rouse (Wesleyan University)
Collaboration And Explanatory Models
  • Melinda Fagan (University of Utah)


It is well-established that collaboration among researchers is prevalent in scientific inquiry, past and present. Philosophically-informed case studies examine how researchers collaborate, or fail to, across fields and disciplines in physical, life and social sciences (e.g., MacLeod and Nersessian 2014). Andersen and Wagenknecht (2013) propose a general framework for examining scientific collaboration in terms of patterns of epistemic dependence relations, which highlights integration and connection of knowledge from disparate sources. Their social epistemic approach links empirically-based studies of scientific collaboration with philosophical debates about collective scientific knowledge and cooperative activity (reviewed in Fagan 2012a).

This paper extends the social epistemic framework to engage scientific explanation. Recent accounts of mechanistic explanation are informed by careful study of experimental practices of mechanism discovery, particularly in biology and neuroscience (e.g., Bechtel and Richardson 1993, Machamer et al 2000, Bechtel and Abrahamsen 2005, Darden 2006, Craver 2007). However, philosophical discussion is dominated by causal aspects of mechanistic explanation, linking to classic debates about the nature of causality, methods of acquiring knowledge of causes, and the ontic status of mechanisms. This paper explores aspects of mechanistic explanation that have received far less attention.

Building on earlier research (Fagan 2012b), I argue that mechanistic explanation in life science exhibits the same basic structure as scientific collaborations in a social epistemic framework: a ‘lower level’ of non-interchangeable units organized in a dynamic pattern of specific interactions, which constitutes a ‘higher level’ system. In biological models, the units are molecules and macromolecular structures, while the system is a cell, tissue, or organism. In models of collaboration, the units are individual members, the system a scientific group. Theories of the chemical bond offer a third case, accounting for the structure and properties of molecules in terms of the arrangement of interacting (paired) electrons and their relations to atomic nuclei.

All three cases share several features: multiple levels, with explanation ‘directed’ from lower to higher; heterogeneous units and interactions at the lower level; and a crucial role for organization in linking levels. I discuss how these features contribute to the epistemic payoff of successful explanation, traditionally characterized in terms of successful prediction, simplicity, or control. The explanatory structure of multiple levels involves, I propose, equipoise among various epistemic payoffs for explanation. The model simplifies the lower level by displaying how heterogeneous components fit together into an overall system, like the fragmented mess of a jigsaw puzzle’s thousand pieces forms a coherent image as the puzzle is completed. On the other hand, the overall system is explained in terms of its parts, which we can know through concrete experimental manipulation, everyday practical experience, mathematical theory, or some combination thereof - methods that enable prediction and control of the parts. Insofar as the parts’ organization is amenable to the same methods, behavior of the whole system can be predicted/controlled. So there is a balance of epistemic payoffs among different levels: top-down simplicity, bottom-up prediction and control. I conclude by discussing how this view of explanation can enrich our understanding of scientific collaboration.


  • Andersen, H, and Wagenknecht, S (2013) Epistemic dependence in interdisciplinary groups. Synthese 190: 1881-1898.
  • Bechtel, W, and Abrahamson, A. (2005) Explanation: A Mechanist Alternative. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36: 421–41.
  • Bechtel, William, and Richardson, Robert. 2010. Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research, 2nd ed. (1st ed. 1993). Princeton: Princeton University Press.
  • Craver, C. (2007) Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience. Oxford: Oxford University Press.
  • Darden, L. (2006) Reasoning in Biological Discoveries: Essays on Mechanisms, Interfield Relations, and Anomaly Resolution. Cambridge: Cambridge University Press.
  • Fagan, MB (2012a) Collective scientific knowledge. Philosophy Compass 7: 821-831.
  • Fagan, MB (2012b) The joint account of mechanistic explanation. Philosophy of Science 79: 448-472.
  • Machamer, P, Darden, L, and Craver, C. (2000) Thinking About Mechanisms. Philosophy of Science 67: 1-25.
  • MacLeod, M, and Nersessian, NJ (2014) Strategies for coordinating experimentation and modeling in integrative systems biology. Journal of Experimental Zoology (Molecular and Developmental Evolution) 322B: 230-239.
Model Coupling in Resource economics: Conditions for Effective Interdisciplinary Collaboration
  • Michiru Nagatsu (University of Helsinki)
  • Miles MacLeod (University of Helsinki)


Recent years have seen a burgeoning interest in promoting and analyzing interdisciplinary collaboration in the natural and social sciences by researchers and university administrators. There is now a substantial collection of academic work and policy documents available on the subject. Most interdisciplinarity research so far has been the domain of science policy and science and technology studies. This research has focused on the institutional, organizational and social dimensions of scientific research that promote or inhibit interdisciplinary interactions, while developing policy frameworks and guidelines for structuring scientific institutions and organization to promote interdisciplinary interactions (see e.g. Gibbons et al. 2004). What is largely missing however is actual case-based study of how the available cognitive resources of different scientific fields and disciplines – their extant theories, modeling templates, experimental and evidential resources – get combined to create functional collaborative platforms for investigation and problem-solving, as well as precise descriptions of what is gained from these combinations. Discussions have identified the need for researchers to integrate values, goals, methods and so on in order to collaborate, but with little concrete guidelines or case studies of how this can happen conceptually and methodologically (see Mattila 2005 as one of the few existing case studies of this kind).

We have here an opportunity for philosophers to actually contribute their expertise on the conceptual and methodological side of scientific processes, to formulate more informed policy criteria on how to construct effective interdisciplinary collaborations. While the philosophical literature studying the explanatory affordances of different types of conceptual and methodological integration is starting to grow, there are yet few philosophical investigations exploring how effective a strategy of integration might be in creating functional collaborative problem-solving platforms given the constraints and difficulties of interdisciplinary research. Our goal in this paper is to demonstrate that conceptual frameworks developed in order to integrate background models and model-building practices can be structured in ways that facilitate collaborative responses to problems. Such frameworks can thus be measured and analyzed according to their ability to facilitate effective and gainful collaborative responses.

We identify and examine one such relatively clear conceptual framework for integrating ecological and economic models developed through successful collaborative interactions between groups of resource economists and ecologists. Their interdisciplinary interaction relies upon what we call a coupled model framework. After a brief introduction to current interdisciplinary studies (section 2) and integration in economics and ecology (section 3), we show how various features of this framework serve to demarcate the nature and structure of the collaboration required between ecologists and economists (section 4). Further using this case we apply two informal measures for assessing the degree to which this conceptual framework generates effective collaboration in practice; by assessing 1) the features of the framework that facilitate efficient collaborative interaction in practice given the various constraints of working across disciplinary boundaries (collaborative affordances), and 2) the gain these approaches afford through the agency of collaboration in comparison with what could be achieved working purely with one’s disciplinary resources and skills (collaborative gain) (section 5). From this information we draw several lessons on both the affordances of this kind of methodological set-up for interdisciplinary research in ecology and economics, and interdisciplinarity more broadly (section 6). We conclude by summarizing our arguments (section 7).

Authorship, Collaboration, and Joint Commitment
  • Haixin Dang (Department of History and Philosophy of Science, University of Pittsburgh)


In this paper, I argue that philosophers of science ought to pay more attention to issues surrounding scientific authorship. Science has become an increasingly collaborative enterprise. Today the overwhelming majority of scientific papers published are multi-authored. Authorship allocation is a fraught issue among scientists. Different journals have taken different policies towards how collaborators are treated as authors. For example, in 2009 Nature has revised their authorship policy to require senior members of collaborations to formally take responsibility of the content of the paper and also require authors to explicitly list their contributions to the paper. A statement of author contributions is now required for publication. The ICMJE (International Committee of Medical Journal Editors), on the other hand, requires a specific set of requirements to be met for authorship and emphasizes the condition that all listed authors must be accountable for all aspects of the work. Further problems arise when the work is produced by an extremely large group of scientists, as in the case in high-energy physics. Journal editors are struggling with defining authorship, credit, and accountability in a time when these concepts are being challenged by how scientists collaborate. The modern fragmentation of scientific authorship has been examined by historians and STS scholars (i.e. Biagioli & Galison 2002), but little discussed by philosophers, besides Wray (2006). In this paper, I work towards a further philosophical understanding of scientific authorship and collaboration in contemporary science. I take disputes in authorship allocation as an indicator of how collaborations function.

In the first part of the paper, I outline the relationship between different kinds of collaborations and authorship policies (both formal and de facto). Drawing from my larger project on the epistemology of scientific collaborations, I define scientific collaborations as a specific way collaborators share goals and intentions, but most importantly information and data. I argue that differences in authorship allocation correspond to how information and data are generated, shared and interpreted. Here I will introduce a taxonomy of collaborations according to their structure. Large-scale collaborations, like ATLAS, treat authors in an egalitarian way because information is circulated throughout the collaboration strategically in which each member of the collaboration plays a role in generating and/or verifying results. Small-scale collaborations, like labs, can have a more top-down structure in which senior members play a more crucial role in directing how information is shared among the collaboration.

In the second part of the paper, I use Gilbert’s concept of joint commitment to examine issues surrounding scientific authorship. Co-authors can be said to have joint committed to the content of their paper. Joint commitment is established among collaborators as they negotiate their shared goals and intentions. I will discuss the process of bringing about joint commitment in collaborations. But the final section of the paper will focus on the normative force of joint commitment. Authorship not only designates credit, but also responsibilities. Collaborators hold each other to accountable for their contributions and place epistemic trust in each other. I will argue that ultimately we need a more robust account of joint commitment to capture the complexities of authorship.


  • Biagioli, M., & Galison, P. (Eds.). (2002). Scientific authorship: Credit and intellectual property in science. Routledge.
  • Wray, K. Brad (2006). Scientific authorship in the age of collaborative research. Studies in History and Philosophy of Science Part A, 37(3), 505-514.
Philosophy of Citizen Science in Practice
  • Kristian H. Nielsen (Aarhus University)


There are many ways in which to practice citizen science. This paper first makes a distinction between three types of citizen science and provides contemporary examples of each type. It then links the diversity of citizen science in practice to its philosophical and sociological interpretations. It will be argued that citizen science embeds divergent, often conflicting, assumptions about the means and ends of science and the role of the citizen/scientist in contemporary democracy. The philosophical and practical challenges of citizen science are to understand how and why these conflicting meanings co-exist and interact (Lewenstein, 2004).

A typology of citizen science based on Lewenstein (2004):

  • Citizen science1: Scientific work undertaken by members of the general public, often in collaboration with or under the direction of professional scientists and scientific institutions. Examples of citizen science1 include amateur astronomy, bird counts, distributed computing, and gamification.
  • Citizen science2: Participation of nonscientists in decision-making about policy issues that have scientific or technological components. Examples are consensus conferences, citizen juries, and protest movements.
  • Citizen science3: Participation of scientists and engineers in public debate and policy-making. Examples include the Intergovernmental Panel on Climate Change and other bodies established to link science and policy-making.

Citizen science1 has fuelled debates about the nature of scientific expertise and the (proper) practice of science. Scientists normally are defined by means of their specialist knowledge and certified competencies. This definition is challenged by citizen scientists1 with little or no formal training in science who appears to be able to contribute to scientific research. Harry Collins and Robert Evans (2002) coined the term “interactional expertise” to denote situations in which laypersons acquire enough expertise to talk the language of specialized science, but still are not able to produce actual science. Other sociologists of science such as Michel Callon and coworkers have argued that citizen scientice1 is part and parcel of the emergence of new modes of research where what counts as expertise and competence is open to negotiation and change (Callon, Lacoumes, & Barthe, 2009).

The challenge of citizen science2+3 turns on the role of science and expertise in public affairs. Alan Irwin (1995) for example used the term “citizen science” not only to understand how environmental issues often generate counter-expertise, ambivalent public attitudes towards science, and reflexivity about risks, but also to open for more equal relationships between scientific and nonscientific understandings and expertise. In particular, he argued that the role of science in public affairs often is heterogeneous and context-dependent. Examples such as consensus conferences where citizens and experts meet to deliberate environmental and ethical issues show that citizen science2 sometimes is mixed with citizen science3.


  • Callon, M., Lacoumes, P., & Barthe, Y. (2009). Acting in an Uncertain World: An Essay on Technical Democracy (G. Burchell, Trans.). Cambridge, MA: MIT Press.
  • Collins, H., & Evans, R. (2002). The third wave of science studies: Studies of expertise and experience. Social Studies of Science, 32(2), 235-296.
  • Irwin, A. (1995). Citizen Science: A Study of People, Expertise and Sustainable Development. London: Routledge.
  • Lewenstein, B. (2004). What does citizen science accomplish? Paper prepared for meeting on citizen science, Paris, France, 8 June, 2004. Retrieved 5 January, 2015, from http://hdl.handle.net/1813/37362