European Journal for Philosophy of Science

To teach the ethics of science to science majors, I follow several teachers in the literature who recommend “persona” writing, or the student construction of dialogues between ethical thinkers of interest. To engage science majors in particular, and especially those new to academic philosophy, I recommend constructing persona dialogues from Henri Poincaré’s essay, “Ethics and Science” (1963/1913), and the non-theological third chapter of Pope Francis’s (2015) encyclical on the environment, Laudato si. This pairing of interlocutors offers two advantages. The first is that science students are likely to recognize both names, since Poincaré appears in undergraduate mathematics and physics textbooks, and because Francis is an environmentalist celebrity. Hence students (in my experience) show more interest in these figures than in other philosophers. The second advantage is that the third chapter of Laudato si reads like an implicit rebuttal of Poincaré’s essay in many respects, and so contriving a dialogue between those authors both facilitates classroom discussion, and deserves attention from professional ethicists in its own right. In this paper, I present my own contrived dialogue between Francis and Poincaré, not for assigning to students as a reading, but as a template for an effective assignment product, and as a crib sheet for educators to preview the richly antiparallel themes between the two works.

It is well-known that the global structure of every space-time model for relativistic cosmology is observationally underdetermined. In order to alleviate the severity of this underdetermination, it has been proposed that we adopt the Cosmological Principle because the Principle restricts our attention to a distinguished class of space-time models (spatially homogeneous and isotropic models). I argue that, even assuming the Cosmological Principle, the topology of space remains observationally underdetermined. Nonetheless, I argue that we can muster reasons to prefer various topological properties over others. In particular, I favor the adoption of multiply connected universe models on grounds of (i) simplicity, (ii) Machian considerations, and (iii) explanatory power. We are able to appeal to such grounds because multiply connected topologies open up the possibility of finite universe models (consistent with our best data), which in turn avoid thorny issues concerning the postulation of an actually infinite universe.

Classic conceptions of model simplicity for machine learning are mainly based on the analysis of the structure of the model. Bayesian, Frequentist, information theoretic and expressive power concepts are the best known of them, which are reviewed in this work, along with their underlying assumptions and weaknesses. These approaches were developed before the advent of the Big Data deluge, which has overturned the importance of structural simplicity. The computational simplicity concept is presented, and it is argued that it is more encompassing and closer to actual machine learning practices than the classic ones. In order to process the huge datasets which are commonplace nowadays, the computational complexity of the learning algorithm is the decisive factor to assess the viability of a machine learning strategy, while the classic accounts of simplicity play a surrogate role. Some of the desirable features of computational simplicity derive from its reliance on the learning system concept, which integrates key aspects of machine learning that are ignored by the classic concepts. Moreover, computational simplicity is directly associated with energy efficiency. In particular, the question of whether the maximum possibly achievable predictive accuracy should be attained, no matter the economic cost of the associated energy consumption pattern, is considered.

In his seminal Inference to the Best Explanation, Peter Lipton adopted a causal view of explanation and a broadly Millian view of how causal knowledge is obtained. This made his account vulnerable to critics who charged that Inference to the Best Explanation is merely a dressed-up version of Mill’s methods, which in the critics’ view do the real inductive work. Lipton advanced two arguments to protect Inference to the Best Explanation against this line of criticism: the problem of multiple differences and the problem of inferred differences. Lipton claimed that these two problems show Mill’s method of difference to be largely unworkable unless it is embedded in an explanationist framework. Here I consider both arguments as well as the best Millian defense against them. Since the existing Millian defense is only partially successful, I will develop a new and improved account. As an integral part of the argument, I show that my solutions to the problems of multiple and inferred differences offer new insight into Lipton’s main case study: Ignaz Semmelweis’s discovery of the cause of childbed fever. I conclude that the method of difference can overcome Lipton’s challenges outside an explanationist framework.

Non-epistemic values play important roles in classificatory practice, such that philosophical accounts of kinds and classification should be able to accommodate them. Available accounts fail to do so, however. Our aim is to fill this lacuna by showing how non-epistemic values feature in scientific classification, and how they can be incorporated into a philosophical theory of classification and kinds. To achieve this, we present a novel account of kinds and classification (the Grounded Functionality Account), discuss examples from biological classification where non-epistemic values play decisive roles, and show how this account accommodates the role of non-epistemic values.

The work of Gaston Bachelard is known for two crucial concepts, that of the epistemological rupture and that of phenomenotechnique. A crucial question is, however, how these two concepts relate to one another. Are they in fact essentially connected or must they be seen as two separate elements of Bachelard’s thinking? This paper aims to analyse the relation between these two Bachelardian moments and the significance of the concept of phenomenotechnique for today. This will be done by examining certain historical uses of the concepts of Bachelard have been used from the 1960s on. From this historical perspective, one gets the impression that these two concepts are relatively independent from each other. The Althusserian school has exclusively focused on the concept of ‘epistemological break’, while scholars from Science & Technology Studies (STS), such as Bruno Latour, seem to have only taken up the concept of phenomenotechnique. It in fact leads to two different models of how to think about science, namely the model of purification and the model of proliferation. The former starts from the idea that sciences are rational to the extent that they are purified and free from (epistemological) obstacles. Scientific objectivity, within this later model, is not achieved by eradicating all intermediaries, obstacles and distortions, but rather exactly by introducing as many relevant technical mediators as possible. Finally, such a strong distinction will be criticized and the argument will be made that both in Bachelard’s and Latour's thought both concepts are combined. This leads to a janus-headed view on science, where both the element of purification (the epistemological break) and the element of proliferation (phenomenotechnique) are combined.