These are my links for December 14th through January 5th:
These are my links for September 28th from 06:58 to 16:49:
- A Mathematician’s Lament – The first thing to understand is that mathematics is an art. The difference between math and the other arts, such as music and painting, is that our culture does not recognize it as such. Everyone understands that poets, painters, and musicians create works of art, and are expressing themselves in word, image, and sound. In fact, our society is rather generous when it comes to creative expression; architects, chefs, and even television directors are considered to be working
artists. So why not mathematicians?
Part of the problem is that nobody has the faintest idea what it is that mathematicians do. The common perception seems to be that mathematicians are somehow connected with science— perhaps they help the scientists with their formulas, or feed big numbers into computers for some reason or other. There is no question that if the world had to be divided into
the “poetic dreamers” and the “rational thinkers” most people would place mathematicians in the latter category.
- Amartya Sen Shakes Up Justice Theory – The Chronicle Review – The Chronicle of Higher Education – Suppose three children—Anne, Bob, and Carla—quarrel over a flute….Intuitions clashing yet? Need something more complex to tingle your justice antennae—perhaps a puzzler from game theory? The example is Amartya Sen’s, from the Nobel-Prize-winning economist’s just-published The Idea of Justice, his magnum opus on a line of work he’s long addressed and now thoroughly re-examines: justice theory. And what a growth industry it’s been since John Rawls revived the subject with his classic, A Theory of Justice (1971), and colleague Robert Nozick made its core principles into an Emerson Hall battle with his libertarian Anarchy, State, and Utopia (1974). Since Rawls, one hardly ranks as a political theorist without a whack at the J-word. Sen’s stepping into the fray should keep things hopping, but justice theory is one subsidiary of philosophy that never really suffers a bad century.
- THE LAST DAYS OF THE POLYMATH | More Intelligent Life – People who know a lot about a lot have long been an exclusive club, but now they are an endangered species. Edward Carr tracks some down …
These are my links for September 14th through September 22nd:
- Philosophy Now | Daniel Dennett: Autobiography (Part 1) – What makes a philosopher? In the first of a two-part mini-epic, Daniel C. Dennett contemplates a life of the mind – his own. Part 1: The pre-professional years.
- Philosopher’s Annual – Our goal is to select the ten best articles published in philosophy each year—an attempt as simple to state as it is admittedly impossible to fulfill. Against a background of twenty-four volumes in hard copy, the Annual is now available entirely online.
- Revolutions: Interactive stock visualizations with R – Jeroen Ooms, who recently completed his Masters in Statistics at Utrech University, has created an outstanding web-based drag-and-drop application for visualizing financial data. With his “StockPlot” t application, you can select any stock from a number of world exchanges (including NASDAQ, DAX, FTSE), and drag it to a worksheet to see a time-series of the stock price. You can arrange up to four charts on the same worksheet for comparison purposes, and control the timeframe and appearance of each chart.
- Revolutions: Machine Learning in R, in a nutshell – Josh Reich has created a concise R script demonstrating various machine-learning techniques in R with simple, self-contained examples.
- Information Processing and Thermodynamic Entropy (Stanford Encyclopedia of Philosophy) – Are principles of information processing necessary to demonstrate the consistency of statistical mechanics? Does the physical implementation of a computational operation have a fundamental thermodynamic cost, purely by virtue of its logical properties? These two questions lie at the centre of a large body of literature concerned with the Szilard engine (a variant of the Maxwell’s demon thought experiment), Landauer’s principle (supposed to embody the fundamental principle of the thermodynamics of computation) and possible connections between the two. A variety of attempts to answer these questions have illustrated many open questions in the foundations of statistical mechanics.
- Christopher J. G. Meacham, Two Mistakes Regarding The Principal Principle | PhilPapers – This paper examines two mistakes regarding David Lewis’ Principal Principle that have appeared in the recent literature. These particular mistakes are worth looking at for several reasons: the thoughts that lead to these mistakes are natural ones, the principles that result from these mistakes are untenable, and these mistakes have led to significant misconceptions regarding the role of admissibility and time. After correcting these mistakes, the paper discusses the correct roles of time and admissibility. With these results in hand, the paper concludes by showing that one way of formulating the chance-credence relation has a distinct advantage over its rivals.
- José Luis Bermúdez – Decision Theory and Rationality – Reviewed by Lara Buchak, UC Berkeley – Philosophical Reviews – University of Notre Dame – Decision theory is used for a variety of purposes: decision makers use it to guide their own actions, and theorists use it both normatively to assess decision makers and to predict and explain their decisions. This book investigates whether the theory can fulfill all three of these purposes. In particular, Bermúdez explores three questions that decision theory must answer under any guise: How should we understand utility and preference? How finely should we individuate the possible outcomes in a decision problem? And how should choice be constrained over time? He argues that there are no answers to these questions that allow decision theory to serve all three purposes.
These are my links for July 29th through August 16th:
- Bayes, Jeffreys, prior distributions, and the philosophy of statistics – In this brief discussion I will argue the following: (1) in thinking about prior distributions, we should go beyond Jeffreys’s principles and move toward weakly informative priors; (2) it is natural for those of us who work in social and computational sciences to favor complex models, contra Jeffreys’s preference for simplicity; and (3) a key generalization of Jeffreys’s ideas is to explicitly include model checking in the process of data analysis.
- Does Confirmation Work the Same at Every Level of Science – In the 1960s, Kuhn maintained that there is no standard higher of rationality than the assent of the relevant community. Realists have seek to evaluate the rationality of science relative to a highest standard possible—namely the truth, or approximate truth, of our best theories. Given that the realist view of rationality is controversial, it seems that a more secure reply to Kuhn should be based on a less controversial objective of science—namely, the goal of predictive accuracy. Not only does this yield a more secure reply to Kuhn, but it also provides the foundation on which any realist arguments should be based. In order to make this case, it is necessary to introduce a three-way distinction between theories, models, and predictive hypotheses, and then ask some hard questions about how the methods of science can actually achieve their goals. As one example of the success of such a program, I explain how the truth of models can sometimes lower their predictive accuracy…
- What is a statistical model? – This paper addresses two closely related questions, “What is a statistical model?” and “What is a parameter?” The notions that a model must “make sense,” and that a parameter must “have a well-defined meaning” are deeply ingrained in applied statistical work, reasonably well understood at an instinctive level, but absent from most formal theories of modelling and inference. In this paper, these concepts are defined in algebraic terms, using morphisms, functors and natural transformations. It is argued that inference on the basis of a model is not possible unless the model admits a natural extension that includes the domain for which inference is required. For example, prediction requires that the domain include all future units, subjects or time points. Although it is usually not made explicit, every sensible statistical model admits such an extension. Examples are given to show why such an extension is necessary and why a formal theory is required…
- Are You Happy? – The New York Review of Books – Chances are if someone were to ask you, right now, if you were happy, you’d say you were. Claiming that you’re happy—that is, to an interviewer who is asking you to rate your “life satisfaction” on a scale from zero to ten—appears to be nearly universal, as long as you’re not living in a war zone, on the street, or in extreme emotional or physical pain. The Maasai of Kenya, soccer moms of Scarsdale, the Amish, the Inughuit of Greenland, European businessmen—all report that they are happy. When happiness researcher Ed Diener, the past president of the International Society of Quality of Life Studies, synthesized 916 surveys of over a million people in forty-five countries, he found that, on average, people placed themselves at seven on the zero-to-ten scale…
- Should Copyright Of Academic Works Be Abolished? – The conventional rationale for copyright of written works, that copyright is needed to foster their creation, is seemingly of limited applicability to the academic domain. For in
a world without copyright of academic writing, academics would still benefit from publishing in the major way that they do now, namely, from gaining scholarly esteem.
Yet publishers would presumably have to impose fees on authors, because publishers
would not be able to profit from reader charges. If these publication fees would be borne
by academics, their incentives to publish would be reduced. But if the publication fees
would usually be paid by universities or grantors, the motive of academics to publish
would be unlikely to decrease (and could actually increase) – suggesting that ending
academic copyright would be socially desirable in view of the broad benefits of a
copyright-free world…
|
|
