- Submit
-
Browse
- All Categories
- Metaphysics and Epistemology
- Value Theory
- Science, Logic, and Mathematics
- Science, Logic, and Mathematics
- Logic and Philosophy of Logic
- Philosophy of Biology
- Philosophy of Cognitive Science
- Philosophy of Computing and Information
- Philosophy of Mathematics
- Philosophy of Physical Science
- Philosophy of Social Science
- Philosophy of Probability
- General Philosophy of Science
- Philosophy of Science, Misc
- History of Western Philosophy
- Philosophical Traditions
- Philosophy, Misc
- Other Academic Areas
- More
Switch to: Citations
References in:
Add references
You must login to add references.
|
|
|
|
|
|
Recently, biologists have argued that data - driven biology fosters a new scientific methodology; namely, one that is irreducible to traditional methodologies of molecular biology defined as the discovery strategies elucidated by mechanistic philosophy. Here I show how data - driven studies can be included into the traditional mechanistic approach in two respects. On the one hand, some studies provide eliminative inferential procedures to prioritize and develop mechanistic hypotheses. On the other, different studies play an exploratory role in providing useful (...) |
|
|
I argue for the causal character of modeling in data-intensive science, contrary to widespread claims that big data is only concerned with the search for correlations. After discussing the concept of data-intensive science and introducing two examples as illustration, several algorithms are examined. It is shown how they are able to identify causal relevance on the basis of eliminative induction and a related difference-making account of causation. I then situate data-intensive modeling within a broader framework of an epistemology of scientific (...) |
|
|
Big Data has been variously defined in the literature. In the main, definitions suggest that Big Data possess a suite of key traits: volume, velocity and variety, but also exhaustivity, resolution, indexicality, relationality, extensionality and scalability. However, these definitions lack ontological clarity, with the term acting as an amorphous, catch-all label for a wide selection of data. In this paper, we consider the question ‘what makes Big Data, Big Data?’, applying Kitchin’s taxonomy of seven Big Data traits to 26 datasets (...) |
|
|
Is Big Data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of Big Data science does not lie in the sheer quantity of data involved, but rather in the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments (...) |
|
|
Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the (...) |
|
|
Scientific classification has long been recognized as involving a specific style of reasoning and doing research, and as occasionally affecting the development of scientific theories. However, the role played by classificatory activities in generating theories has not been closely investigated within the philosophy of science. I argue that classificatory systems can themselves become a form of theory, which I call classificatory theory, when they come to formalize and express the scientific significance of the elements being classified. This is particularly evident (...) |
|
|
Scientific knowledge production is currently affected by the dissemination of data on an unprecedented scale. Technologies for the automated production and sharing of vast amounts of data have changed the way in which data are handled and interpreted in several scientific domains, most notably molecular biology and biomedicine. In these fields, the activity of data gathering has become increasingly technology-driven, with machines such as next generation genome sequencers and mass spectrometers generating billions of data points within hours, and with little (...) |
|
|
This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make sense of culture, (...) |
|
|
Current research in molecular epidemiology uses biomarkers to model the different disease phases from environmental exposure, to early clinical changes, to development of disease. The hope is to get a better understanding of the causal impact of a number of pollutants and chemicals on several diseases, including cancer and allergies. In a recent paper Russo and Williamson address the question of what evidential elements enter the conceptualisation and modelling stages of this type of biomarkers research. Recent research in causality has (...) |
|
|
This essay explains what the Causal Markov Condition says and defends the condition from the many criticisms that have been launched against it. Although we are skeptical about some of the applications of the Causal Markov Condition, we argue that it is implicit in the view that causes can be used to manipulate their effects and that it cannot be surrendered without surrendering this view of causation. |
|
|
Between 2006 and 2011, humanity accumulated 1,600 EB of data. As a result of this growth, there is now more data produced than available storage. This article explores the problem of “Big Data,” arguing for an epistemological approach as a possible solution to this ever-increasing challenge. |
|
|
Ever since David Hume, empiricists have barred powers and capacities from nature. In this book Cartwright argues that capacities are essential in our scientific world, and, contrary to empiricist orthodoxy, that they can meet sufficiently strict demands for testability. Econometrics is one discipline where probabilities are used to measure causal capacities, and the technology of modern physics provides several examples of testing capacities (such as lasers). Cartwright concludes by applying the lessons of the book about capacities and probabilities to the (...) |
|
|
Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics. |
|
|
Scientific and philosophical literature on causality has become highly specialised. It is hard to find suitable access points for students, young researchers, or professionals outside this domain. This book provides a guide to the complex literature, explains the scientific problems of causality and the philosophical tools needed to address them. |
|
|
The world is complex, but acknowledging its complexity requires an appreciation for the many roles context plays in shaping natural phenomena. In _Unsimple Truths, _Sandra Mitchell argues that the long-standing scientific and philosophical deference to reductive explanations founded on simple universal laws, linear causal models, and predict-and-act strategies fails to accommodate the kinds of knowledge that many contemporary sciences are providing about the world. She advocates, instead, for a new understanding that represents the rich, variegated, interdependent fabric of many levels (...) |
|
|
|
|
|
This paper is divided into two parts. In the first, I shall briefly analyse the phenomenon of “big data”, and argue that the real epistemological challenge posed by the zettabyte era is small patterns. The valuable undercurrents in the ocean of data that we are accumulating are invisible to the computationally-naked eye, so more and better technology will help. However, because the problem with big data is small patterns, ultimately, the game will be won by those who “know how to (...) |
|
|
Both in biology and psychology there has been a tendency on the part of many investigators to focus solely on the mature organism and ignore development. There are many reasons for this, but an important one is that the explanatory framework often invoked in the life sciences for understanding a given phenomenon, according to which explanation consists in identifying the mechanism that produces that phenomenon, both makes it possible to side-step the development issue and to provide inadequate resources for actually (...) |
|
|
|
|
|
|
|
|
Biomedical ontologies are considered a serious innovation for biomedical research and clinical practice. They promise to integrate information coming from different biological databases thus creating a common ground for the representation of knowledge in all the life sciences. Such a tool has potentially many implications for both basic biomedical research and clinical practice. Here I discuss how this tool has been generated and thought. Due to the analysis of some empirical cases I try to elaborate how biomedical ontologies constitute a (...) |
PhilPapers logo by Andrea Andrews and Meghan Driscoll.
This site uses cookies and Google Analytics (see our terms & conditions for details regarding the privacy implications). Use of this site is subject to terms & conditions.
All rights reserved by The PhilPapers Foundation
Server: philpapers-web-7c76586df8-4c2ch uwo