We show how more general knowledge can be built in information security, by the building of knowledge of mechanism clusters, some of which are multifield. By doing this, we address in a novel way the longstanding philosophical problem of how, if at all, we come to have knowledge that is in any way general, when we seem to be confined to particular experiences. We also address the issue of building knowledge of mechanisms by studying an area that is new to (...) the mechanisms literature: the methods of what we shall call mechanism discovery in information security. This domain offers a fascinating novel constellation of challenges for building more general knowledge. Specifically, the building of stable communicable mechanistic knowledge is impeded by the inherent changeability of software, which is deployed by malicious actors constantly changing how their software attacks, and also by an ineliminable secrecy concerning the details of attacks not just by attackers, but also by information security defenders as they protect their methods from both attackers and commercial competitors. We draw out ideas from the work of the mechanists Darden, Craver, and Glennan to yield an approach to how general knowledge of mechanisms can be painstakingly built. We then use three related examples of active research problems from information security to develop philosophical thinking about building general knowledge using mechanisms, and also apply this to develop insights for information security. We show that further study would be instructive both for practitioners and for philosophers. (shrink)

Social machines are a prominent focus of attention for those who work in the field of Web and Internet science. Although a number of online systems have been described as social machines, there is, as yet, little consensus as to the precise meaning of the term “social machine.” This presents a problem for the scientific study of social machines, especially when it comes to the provision of a theoretical framework that directs, informs, and explicates the scientific and engineering activities of (...) the social machine community. The present paper outlines an approach to understanding social machines that draws on recent work in the philosophy of science, especially work in so-called mechanical philosophy. This is what might be called a mechanistic view of social machines. According to this view, social machines are systems whose phenomena are explained via an appeal to socio-technical mechanisms. We show how this account is able to accommodate a number of existing attempts to define the social machine concept, thereby yielding an important opportunity for theoretical integration. (shrink)

Experimentation represents today a ‘hot’ topic in computing. If experiments made with the support of computers, such as computer simulations, have received increasing attention from philosophers of science and technology, questions such as “what does it mean to do experiments in computer science and engineering and what are their benefits?” emerged only recently as central in the debate over the disciplinary status of the discipline. In this work we aim at showing, also by means of paradigmatic examples, how the traditional (...) notion of controlled experiment should be revised to take into account a part of the experimental practice in computing along the lines of experimentation as exploration. Taking inspiration from the discussion on exploratory experimentation in the philosophy of science—experimentation that is not theory-driven—we advance the idea of explorative experiments that, although not new, can contribute to enlarge the debate about the nature and role of experimental methods in computing. In order to further refine this concept we recast explorative experiments as socio-technical experiments, that test new technologies in their socio-technical contexts. We suggest that, when experiments are explorative, control should be intended in a posteriori form, in opposition to the a priori form that usually takes place in traditional experimental contexts. (shrink)

One might poetically muse that computers have the essence both of logic and machines. Through the case of the history of Separation Logic, we explore how this assertion is more than idle poetry. Separation Logic works because it merges the software engineer’s conceptual model of a program’s manipulation of computer memory with the logical model that interprets what sentences in the logic are true, and because it has a proof theory which aids in the crucial problem of scaling the reasoning (...) task. Scalability is a central problem, and some would even say the central problem, in appli- cations of logic in computer science. Separation Logic is an interesting case because of its widespread success in verification tools. For these two senses of model—the engineering/conceptual and the logical—to merge in a genuine sense, each must maintain their norms of use from their home disciplines. When this occurs, both the logic and engineering benefit greatly. Seeking this intersection of two different senses of model provides a strategy for how computer scientists and logicians may be successful. Furthermore, the history of Separation Logic for analysing programs provides a novel case for philosophers of science of how software engineers and computer scientists develop models and the components of such models. We provide three contributions: an exploration of the extent of models merging that is necessary for success in computer science; an introduction to the technical details of Separation Logic, which can be used for reasoning about other exhaustible resources; and an introduction to the problems, process, and results of computer scientists for those outside the field. (shrink)

Phyllis Illari and Jon Williamson propose a formulation for a general mechanistic account, the purpose of which is to capture the similarities across mechanistic accounts in the sciences. Illari and Williamson extract insight from mechanisms in astrophysics—which are notably different from the typical biological mechanisms discussed in the literature on mechanisms—to show how their general mechanistic account accommodates mechanisms across various sciences. We present argumentation that demonstrates why an amendment is necessary to the ontology referred to by the general mechanistic (...) account provided by Illari and Williamson. The amendment is required due to the variability of some components in computing mechanisms: the very same component serves as either entity or activity, both between levels and within the same level of the explanatory hierarchy. We argue that the proper ontological account of these mechanistic components involves disambiguation via explicitly indexing them as entities or activities. (shrink)