Software Intensive Science

John Symons, Jack Horner
2014 Philosophy & Technology  
Introduction The increasingly central role of computing technologies has changed science in significant ways. While the practical import of this transformation is undeniable, its implications for the philosophical understanding of scientific inquiry are less clear. Some philosophers have argued (and we agree) that there is more to recent changes than simply the appearance of a more powerful set of tools for scientists. In our view, there are important new philosophical questions that arise with
more » ... the use of computing technology in science. There are at least two aspects of recent changes in science that merit philosophical reflection. From our perspective, the clearest and most fundamental of these is the role of software in science. Strikingly, the impact of software on scientific practice has attracted almost no attention from philosophers of science. By contrast, many philosophers have addressed the second prominent aspect of this change; the appearance of powerful and relatively inexpensive computing technology for scientific modelers. Philosophers have noted that greater processing power has led to more sophisticated scientific models and that new possibilities for modeling have opened new domains for scientific inquiry. Since the mid-1990s there has been extensive philosophical research into the role of computational models in scientific explanation. 1 While we agree that the quantitative increase in power and the accompanying widening of the scope of computational models and simulations is exciting and important, in this paper we will focus on the less widely discussed role of software in modern science. The purpose of this paper is to explain the difference between pre-and post-software intensive science in precise terms and to explain why this difference should matter to philosophers of science. We will argue that the clearest difference between contemporary software intensive scientific practice and more traditional non-software intensive varieties derives from the characteristically high conditionality of most software used in modern science. Very roughly, by conditionality we mean that the course of a computation (i.e., the order in which software instructions are executed) is determined by conditional schemata, e.g. "If x obtains then do y". The if-then (or if-then-else) schema is at the heart of most modern programming languages and as we will show it is this very basic characteristic that marks the most 1 See Symons 2008 for a discussion of how computational models have figured in discussions of the metaphysics and epistemology of science. important difference between modern software intensive science and its predecessors. 2 Our purpose in this paper is to help to clarify the significance and implications of high conditionality for software intensive science in formal terms. Appreciating the difference between software intensive and nonsoftware intensive science will facilitate a properly informed discussion of the challenges that accompany the ongoing transformation of scientific inquiry in our time. Recent work in the philosophy of software intensive science Even a superficial survey of the changing landscape of scientific inquiry reveals potential areas of philosophical interest. Consider, for example two prominent papers from Science and Nature respectively: In 2009, Michael Schmidt and Hod Lipson described how their program Eureqa inferred Newton's second law and the law of conservation of momentum from descriptions of the behaviour of a double-pendulum system. (Schmidt and Lipson 2009) More recently, Eugene Loukine and colleagues demonstrated a model that was able to predict unforeseen side-effects for pharmaceuticals that were already approved for consumption. (Loukine et al, 2012) These two papers represent very different examples of software intensive science: One, a system is capable of generating theoretical insights and law-like relationships from a data set while the other makes dramatic progress on a specific practical
doi:10.1007/s13347-014-0163-x fatcat:epn7sktin5gblgawzguxiv5tni