International Society for History, Philosophy, and Social Studies of Biology

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WEDNESDAY, JULY 8  /  09:00 - 10:30  /  DS-M240
Individual papers
Causality, Conservation and Citizen Science

Selecting among ontologically different causes: Toward an account of the pragmatics of causal selection

Brian Hanley (University of Calgary, Canada)

Most philosophers are concerned with distinguishing causes from non-causes, where it is supposed that there are no principled ways of distinguishing among causes. The lack of principled distinctions is then taken to imply ontological parity of causes. Without ontological distinctions among causes, any selection of a cause as ‘the’ cause of some effect is understood in terms of purely pragmatic considerations. In opposition, Waters (2007) and Woodward (2010) offer conceptual machinery for making principled ontological distinctions among causes framed within Woodward’s (2003) interventionist theory of causation. However, Northcott (2009) objects that Waters’ (2007) ‘actual difference makers’ are not strictly ontological due to the pragmatics of causal selection. Northcott claims that the pragmatic selection of effects fixes the causal claim in such a way that the actual difference making cause is determined by this selection, not just by ontology. Of course pragmatics play a role in causal selection, however, Northcott’s argument lacks a clear account of the pragmatics involved in causal selection. Furthermore it is not clear that appeals like Northcott’s to the pragmatics of causal selection render Waters or Woodward’s concepts not purely ontological. Nor is it convincing that dismissing ontological distinctions of causes is philosophically productive. A detailed account of the pragmatics of causal selection is needed in order to shed light on this aspect of causal reasoning. I suggest that a clear account of the pragmatics of causal selection can be developed using the ontological concepts provided by Waters and Woodward. Detailed in this way, causal selection is understood as selecting among ontologically different causes relative to practical and epistemic interests of scientists.Looking at Waters’ (2008 & manuscript) analysis of practice in genetics, I explain how focusing on ontological features of causes helps understand the pragmatics of selecting a cause among other relevant causes.

Were there fishes on the Ark? Re-thinking the ark conservation concept at aquaria

Chris Zarpentine (Wilkes University, United States); Samantha Muka (University of Pennsylvania, United States)

The ark concept in global species conservation holds that ex situ spaces can be used to maintain and breed endangered species for future reintroduction. While zoos espoused this concept to explain the need for programs focused on the breeding of charismatic megafauna throughout the 20th century, such programs face serious practical obstacles and principled objections. In the face of limited successes, many have reevaluated their commitment to this conservation concept. More recently, however, interest in conserving endangered amphibian and reptile species has revived the concept and linked it more closely with aquariums. Still, discussion of the applicability of the ark concept to exclusively aquatic species has been limited. Historically there are important—though often neglected—differences between zoos and aquariums. This makes it particularly important to consider whether there are important differences in the application of the ark concept to aquatic species. This paper seeks to provide a comparative evaluation of the aquatic ark concept. We identify ethical and practical features relevant to the likely success of an ark. Drawing on historical sources, we evaluate how the aquatic ark concept compares to previous attempts to implement the ark concept. We argue that, while an aquatic ark has the potential to be more successful than similar historical programs for terrestrial organisms, serious concerns remain.

Can you help me fold this? Proteins, computer architecture, and citizen science

Shawn Miller (University of California, Davis, United States)

Protein folding science relies heavily on computer simulation because proteins are very small, they transform very rapidly, and they take on myriad shapes, all of which make experimentation of the in vivo or in vitro variety largely unworkable. However, the computational power necessary to simulate the manner in which proteins go from two-dimensional amino-acid strings to three-dimensional structures is extraordinary. Dale L. Bodian et al. noted in the 2011 Pacific Symposium on Biocomputing that simulating the protein collagen for 10 nanoseconds "took approximately 11 months using the CPUs of over a quarter of a million computers." As a result, protein folding scientists have adopted--and in some cases have helped develop--a variety of different kinds of computers, or computer architectures, in search of ever greater processing power. Different computer types impose different limits and constraints on research scientists. Some, e.g., are simply harder to program than others. Specific computer architectures also necessitate particular collaborations between scientists and non-scientists. The constraints imposed, and possibilities afforded, by these collaborations canalize, or channel, science in particular directions. My paper will look at the Folding@Home distributed computing project, which farms out protein folding simulations to volunteers who make their personal computers available for the purpose. Folding@Home volunteers are very often computing enthusiasts, e.g., video gamers, who own relatively powerful personal computers with characteristic architectures. Attracting and retaining these volunteers has required that scientific researchers acknowledge and adopt the non-scientific values of these individuals in ways that sometimes directly conflict with the aim of efficiently using computer power to understand how proteins fold. Additionally, I will show how the computer architectures of the citizen scientists involved have affected both which proteins researchers study and the statistical methods used on the data.