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Michael Dummett and Storrs McCall have claimed that time travel scenarios in which an artist copies an artwork from a reproduction of it that has been sent from the future introduce a causal loop of a new kind: one involving artistic value. They have suggested that this poses a hitherto unacknowledged challenge to time travel theories. I argue that their conclusion depends on some unstated essentialist assumptions about metaphysics of art and the status of representations. By relaxing these assumptions, I show that Dummett and McCall’s scenarios contain no causal loop involving artistic value, and thus pose no new problem for time travel theories.

International Studies in the Philosophy of Science (ISPS) reports the following manuscript handling figures for 2016. We received 35 unsolicited article manuscripts. The editor rejected three of th...

Broaching the topic of the relation between art and science 500 years after the Renaissance plunges us into a complex set of tangled congruities and incongruities in flux. Some writers portray the Renaissance as a golden age in which art and science were united or integrated (Crombie in Hist Sci 18:233–246, 1980). Some go on to lament the fragmentation of areas of human endeavour since the Renaissance, and work to recover hidden unities of purpose and of method between art and science.

The 2015 manuscript handling figures for International Studies in the Philosophy of Science (ISPS) were as follows. We received 45 unsolicited article manuscripts. The editor rejected seven of thes...

This article offers a critical review of past attempts and possible methods to test philosophical models of science against evidence from history of science. Drawing on methodological debates in social science, I distinguish between quantitative and qualitative approaches. I show that both have their uses in history and philosophy of science, but that many writers in this domain have misunderstood and misapplied these approaches, and especially the method of case studies. To test scientific realism, for example, quantitative methods are more effective than case studies. I suggest that greater methodological clarity would enable the project of integrated history and philosophy of science to make renewed progress.

In this article, I discuss calls for access to empirical data within controversies about climate science, as revealed and highlighted by the publication of the e-mail correspondence involving scientists at the Climatic Research Unit at the University of East Anglia in 2009. I identify several arguments advanced for and against the sharing of scientific data. My conclusions are that, whereas transparency in science is to be valued, appeals to an unproblematic category of ‘empirical data’ in climate science do not reflect the complexities of scientific practice in this field.

International Studies in the Philosophy of Science (ISPS) received 25 unsolicited article manuscripts in 2013. The editor rejected 3 manuscripts without seeking further advice, on the grounds that ...

This article defends the following claims. First, for patterns exhibited in empirical data, there is no criterion on which to demarcate patterns that are physically significant and patterns that are not physically significant. I call a pattern physically significant if it corresponds to a structure in the world. Second, all patterns must be regarded as physically significant. Third, distinct patterns must be regarded as providing evidence for distinct structures in the world. Fourth, in consequence, the world must be conceived as showing all possible structures.

Scientists use several rhetorical strategies to heighten the objectivity and credibility of their findings. Examples are the rhetoric of effort, which involves emphasizing the amount of effort expended in research, and the rhetoric of self-effacement, which involves suggesting that the facts have manifested themselves without input from the researcher. In this article, I present a further, hitherto unrecognized rhetorical strategy that scientists use for the same aims: the rhetoric of effortlessness, which consists in conveying the impression that establishing a result has cost the investigator little effort. This rhetorical strategy heightens the credibility of individual scientific findings, raises the reputation of individual scientists, and propagates an attractive view of science as a whole. I outline the epistemology underpinning the rhetoric of effortlessness and give examples of its use in modern science.

Several quantitative techniques for choosing among data models are available. Among these are techniques based on algorithmic information theory, minimum description length theory, and the Akaike information criterion. All these techniques are designed to identify a single model of a data set as being the closest to the truth. I argue, using examples, that many data sets in science show multiple patterns, providing evidence for multiple phenomena. For any such data set, there is more than one data model that must be considered close to the truth. I conclude that, since the established techniques for choosing among data models are unequipped to handle these cases, they cannot be regarded as adequate.

This discussion note responds to objections by Twardy, Gardner, and Dowe to my earlier claim that empirical data sets are algorithmically incompressible. Twardy, Gardner, and Dowe hold that many empirical data sets are compressible by Minimum Message Length technique and offer this as evidence that these data sets are algorithmically compressible. I reply that the compression achieved by Minimum Message Length technique is different from algorithmic compression. I conclude that Twardy, Gardner, and Dowe fail to establish that empirical data sets are algorithmically compressible.

Thought experiment acquires evidential significance only on particular metaphysical assumptions. These include the thesis that science aims at uncovering “phenomena”—universal and stable modes in which the world is articulated—and the thesis that phenomena are revealed imperfectly in actual occurrences. Only on these Platonically inspired assumptions does it make sense to bypass experience of actual occurrences and perform thought experiments. These assumptions are taken to hold in classical physics and other disciplines, but not in sciences that emphasize variety and contingency, such as Aristotelian natural philosophy and some forms of historiography. This explains why thought experiments carry weight in the former but not the latter disciplines.

I argue that, contrary to thestandard view, the Newtonian universe containsno contingency. I do this by arguing (i) thatno contingency is introduced into the Newtonianuniverse by the initial conditions of physicalsystems in the universe, and (ii) that theclaim that the Newtonian universe as a wholehas contingent properties leads to incoherence.This result suggests that Newtonian physics iseither inconsistent or incomplete, since thelaws of Newtonian physics are too weak todetermine all the properties of the Newtonianuniverse uniquely.

According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers of information about the world. Since, on algorithmic information theory, a string is algorithmically random just if it is incompressible, the thesis entails that empirical data sets consist of algorithmically random strings of digits. Rather than constituting compressions of empirical data, scientific laws and theories pick out patterns that data sets exhibit with a certain noise.

Murray Gell-Mann has proposed the concept of effective complexity as a measure of information content. The effective complexity of a string of digits is defined as the algorithmic complexity of the regular component of the string. This paper argues that the effective complexity of a given string is not uniquely determined. The effective complexity of a string admitting a physical interpretation, such as an empirical data set, depends on the cognitive and practical interests of investigators. The effective complexity of a string as a purely formal construct, lacking a physical interpretation, is either close to zero, or equal to the string's algorithmic complexity, or arbitrary, depending on the auxiliary criterion chosen to pick out the regular component of the string. Because of this flaw, the concept of effective complexity is unsuitable as a measure of information content.

This introduction to the special issue on "Aesthetics of Science" reviews recent philosophical research on aesthetic aspects of science. Topics represented in this research include the aesthetic properties of scientific images, theories, and experiments; the relation of science and art; the role of aesthetic criteria in scientific practice and their effect on the development of science; aesthetic aspects of mathematics; the contrast between a classic and a Romantic aesthetic; and the relation between emotion, cognition, and rationality.

International Studies in the Philosophy of Science (ISPS) received 48 unsolicited article manuscripts in 2007. The editor rejected 7 of these without consulting referees, on the grounds that they f...