Archive for the ‘Philosophy of Science’ Category

Launch of the PhilMath Archive

Monday, May 29th, 2017

Launch of the PhilMath Archive: preprint server specifically for philosophy of mathematics

From the post:

PhilSci-Archive is pleased to announce the launch of the PhilMath-Archive, http://philsci-archive.pitt.edu/philmath.html a preprint server specifically for the philosophy of mathematics. The PhilMath-Archive is offered as a free service to the philosophy of mathematics community. Like the PhilSci-Archive, its goal is to promote communication in the field by the rapid dissemination of new work. We aim to provide an accessible repository in which scholarly articles and monographs can find a permanent home. Works posted here can be linked to from across the web and freely viewed without the need for a user account.

PhilMath-Archive invites submissions in all areas of philosophy of mathematics, including general philosophy of mathematics, history of mathematics, history of philosophy of mathematics, history and philosophy of mathematics, philosophy of mathematical practice, philosophy and mathematics education, mathematical applicability, mathematical logic and foundations of mathematics.

For your reference, the PhilSci-Archive.

Enjoy!

Big Data – The New Science of Complexity

Friday, December 26th, 2014

Big Data – The New Science of Complexity by Wolfgang Pietsch.

Abstract:

Data-intensive techniques, now widely referred to as ‘big data’, allow for novel ways to address complexity in science. I assess their impact on the scientific method. First, big-data science is distinguished from other scientific uses of information technologies, in particular from computer simulations. Then, I sketch the complex and contextual nature of the laws established by data-intensive methods and relate them to a specific concept of causality, thereby dispelling the popular myth that big data is only concerned with correlations. The modeling in data-intensive science is characterized as ‘horizontal’—lacking the hierarchical, nested structure familiar from more conventional approaches. The significance of the transition from hierarchical to horizontal modeling is underlined by a concurrent paradigm shift in statistics from parametric to non-parametric methods.

A serious investigation of the “science” of big data, which I noted was needed in: Underhyped – Big Data as an Advance in the Scientific Method.

From the conclusion:

The knowledge established by big-data methods will consist in a large number of causal laws that generally involve numerous parameters and that are highly context-specific, i.e. instantiated only in a small number of cases. The complexity of these laws and the lack of a hierarchy into which they could be integrated prevent a deeper understanding, while allowing for predictions and interventions. Almost certainly, we will experience the rise of entire sciences that cannot leave the computers and do not fit into textbooks.

This essay and the references therein are a good vantage point from which to observe the development of a new science and its philosophy of science.

Cause And Effect:…

Tuesday, December 23rd, 2014

Cause And Effect: The Revolutionary New Statistical Test That Can Tease Them Apart

From the post:

…But in the last few years, statisticians have begun to explore a number of ways to solve this problem. They say that in certain circumstances it is indeed possible to determine cause and effect based only on the observational data.

At first sight, that sounds like a dangerous statement. But today Joris Mooij at the University of Amsterdam in the Netherlands and a few pals, show just how effective this new approach can be by applying it to a wide range of real and synthetic datasets. Their remarkable conclusion is that it is indeed possible to separate cause and effect in this way.

Mooij and co confine themselves to the simple case of data associated with two variables, X and Y. A real-life example might be a set of data of measured wind speed, X, and another set showing the rotational speed of a wind turbine, Y.

These datasets are clearly correlated. But which is the cause and which the effect? Without access to a controlled experiment, it is easy to imagine that it is impossible to tell.

The basis of the new approach is to assume that the relationship between X and Y is not symmetrical. In particular, they say that in any set of measurements there will always be noise from various cause. The key assumption is that the pattern of noise in the cause will be different to the pattern of noise in the effect. That’s because any noise in X can have an influence on Y but not vice versa.

At some eighty-three (83) pages, this is going to take a while to digest. One of the reasons for mentioning it as a couple of holidays approach in many places.

I don’t think the authors are using “cause and effect” in the same sense as Hume and Ayer but that remains to be seen. Just skimming the first few pages, this is going to be an interesting read.

The post is based on:

Distinguishing cause from effect using observational data: methods and benchmarks by Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schöt;lkopf.

Abstract:

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y . This was often considered to be impossible. Nevertheless, several approaches for addressing this bivariate causal discovery problem were proposed recently. In this paper, we present the benchmark data set CauseEffectPairs that consists of 88 different “cause-effect pairs” selected from 31 datasets from various domains. We evaluated the performance of several bivariate causal discovery methods on these real-world benchmark data and on artificially simulated data. Our empirical results provide evidence that additive-noise methods are indeed able to distinguish cause from effect using only purely observational data. In addition, we prove consistency of the additive-noise method proposed by Hoyer et al. (2009).

Thoughts and comments welcome!

Positions in the philosophy of science

Tuesday, November 18th, 2014

positions in philosophy of science

If you want to start a debate among faculty this holiday season, print this graphic out and leave it laying around with one or two local names penciled in.

For example, I would not list naive realism as a “philosophy of science” as much as an error, taken for a “philosophy of science.” 😉

Enjoy!

I first saw this as Positions in the philosophy of science by Chris Blattman.

To Know, but Not Understand: David Weinberger on Science and Big Data

Wednesday, January 4th, 2012

To Know, but Not Understand: David Weinberger on Science and Big Data

From the introduction:

In an edited excerpt from his new book, Too Big to Know, David Weinberger explains how the massive amounts of data necessary to deal with complex phenomena exceed any single brain’s ability to grasp, yet networked science rolls on.

Well, it is a highly entertaining excerpt, with passages like:

For example, the biological system of an organism is complex beyond imagining. Even the simplest element of life, a cell, is itself a system. A new science called systems biology studies the ways in which external stimuli send signals across the cell membrane. Some stimuli provoke relatively simple responses, but others cause cascades of reactions. These signals cannot be understood in isolation from one another. The overall picture of interactions even of a single cell is more than a human being made out of those cells can understand. In 2002, when Hiroaki Kitano wrote a cover story on systems biology for Science magazine — a formal recognition of the growing importance of this young field — he said: “The major reason it is gaining renewed interest today is that progress in molecular biology … enables us to collect comprehensive datasets on system performance and gain information on the underlying molecules.” Of course, the only reason we’re able to collect comprehensive datasets is that computers have gotten so big and powerful. Systems biology simply was not possible in the Age of Books.

Weinberger slips twix and tween philosophy of science, epistemology, various aspects of biology and computational science. Not to mention with the odd bald faced assertion such as: “…the biological system of an organism is complex beyond imagining.” At one time that could have been said about the atom. I think some progress has been made on understanding that last item, or so physicists claim.

Don’t get me wrong, I have a copy on order and look forward to reading it.

But, no single reader will be able to discover all the factual errors and leaps of logic in Too Big to Know. Perhaps a website or wiki, Too Big to Correct?