Parfit’s Repugnant Conclusion argues, against intuition, that for any world A, another world Z with higher population and minimal well-being is better. That intuition is incorrect because the argument has not considered resources that support well-being. Z must have many more resources supporting well-being than A does. Z is repugnant because it spreads those resources among too many people; another world with Z’s resources and fewer people, if available, would be far superior. But Z is still better than A; it is worth accepting its very large population to get the resources needed to support their well-being.

I offer an analysis of the concept of scientific innovation. When research is innovated, highly novel and useful elements of investigation begin to spread through a scientific community, resulting from a process which is neither due to blind chance nor to necessity, but to a minimal use of rationality. This, however, leads to tension between two claims: (1) scientific innovation can be explained rationally; (2) no existing account of rationality explains scientific innovation. There are good reasons to maintain (1) and (2), but it is difficult for both claims to be accepted simultaneously by a rational subject. In particular, I argue that neither standard nor bounded theories of rationality can deliver a satisfactory explanation of scientific innovations.

During the 19th century, evolutionary models of innovation followed a famous thesis of continuity, according to which methods and explanatory patterns of biology should have an important say in the social sciences. In the 20th century, this thesis was considered unacceptable as part of the sharp separation of biology from the social sciences. Recent advances in the biological sciences suggest a way in which a version of the thesis of continuity can be reinstated, to suggest new ways of explaining innovation in the social sciences. Key kinds of innovation can be explained in terms of the evolution of robust complex systems, interpreted as processes of path creation.

There is a rough, long-term tradeoff between rate of innovation and degree of strong realism in scientific practice, a point reflected in historically changing conceptions of method as they retreat from epistemological foundationism to a highly fallibilistic, modeling perspective. The successively more liberal, innovation-stimulating methods open up to investigation deep theoretical domains at the cost, in many cases, of moving away from strong realism as a likely outcome of research. The crowbar model of method highlights this tension, expressed as the crowbar compromise and the crowbar fallacy. The tools-to-theories heuristic, described and evaluated by Gigerenzer and colleagues, can be regarded as an attempt by some scientific realists to overcome this compromise. Instead, it is an instance of it. Nonetheless, in successful applications the crowbar model implies a modest, instrumental (nonrepresentational) realism.

Este artículo parte del debate en filosofía de la ciencia entre la tradición teórica y la experimental, y muestra su relación con el estudio de los procesos de innovación e invención en ciencia, cruzando así los planteamientos de análisis más teóricos de la filosofía de la ciencia con cuestiones más relacionadas con la filosofía de la tecnología y la ciencia aplicada. De esta manera, analizamos la interrelación entre experimento y teoría en los procesos de invención e innovación y conectamos los campos de la ciencia teórica y la aplicada, mostrando la continuidad entre ambas. Así, podemos mostrar también cómo en ciencia hay siempre dependencia mutua de teoría y experimentación, y cómo esa dependencia es extrapolable también a los procesos de innovación e invención.Tomando como punto de partida el debate en torno a las tradiciones teóricas y experimentales, veremos hasta qué punto los argumentos que cuestionan las tradiciones teóricas y apuestan por las tradiciones experimentales encajan con los fenómenos de invención e innovación. El caso que vamos a tomar como referencia para aplicar este análisis es el de «aprendizaje automático», como una rama de los algoritmos computacionales diseñados para emular la inteligencia humana aprendiendo del entorno. Este campo es relevante pues, a pesar de su naturaleza eminentemente teórica —en substancia es matemática aplicada—, presenta toda una serie de características que lo hacen muy afín al análisis desde las tradiciones experimentales.

In order to show how technological innovation and scientific innovation are linked in the course of research in human science, I present an account of a series of innovations made in our laboratory (Distal Glove – Tactos system – Intertact server – Dialtact module). We will see how research on the technical constitution of cognitive and perceptual activities can be associated with a process of innovation. The technical devices present at each stage carry an interpretative framework that prepares the following stages. Devices which were initially developed for the purposes of performing experiments contributed both to scientific inventions and to developments with a practical and social finality.

We offer a novel argument for one-boxing in Newcomb’s Problem. The intentional states of a rational person are psychologically coherent across time, and rational decisions are made against this backdrop. We compare this coherence constraint with a golf swing, which to be effective must include a follow-through after the ball is in flight. Decisions, like golf swings, are extended processes, and their coherence with other psychological states of a player in the Newcomb scenario links her choice with the way she is predicted in a common cause structure. As a result, the standard argument for two-boxing is mistaken.

Desde una lectura organicista mostramos que la concepción de la ontogenia que desarrolla Jan Swammerdam a través de su idea de la metamorfosis nos permite revelar la existencia de una continuidad entre el epigenetismo de W. Harvey y el preformacionismo de G.W. Leibniz. La concepción moderna de la epigénesis (Harvey) y la preformación (Leibniz) no implica tanto un posicionamiento antagónico respecto al problema del origen del embrión cuanto un esfuerzo por desarrollar un mismo modelo de ontogenia frente a las alternativas mecanicistas o vitalistas. En línea con esta lectura, hacemos una valoración crítica de las diferentes interpretaciones preformacionistas de Swammerdam.

El DSM, elaborado por la Asociación de Psiquiatría Americana (APA), es la clasificación de los trastornos mentales más relevante del ámbito académico y clínico. Se trata de un manual que ha ido cambiando con su contexto, pero sus modificaciones no siempre han respondido a avances en el conocimiento científico. El repaso histórico de sus sucesivas ediciones muestra su naturaleza sociopolítica, y que factores de tipo ideológico o político han tenido gran relevancia en su configuración. Estos factores ayudan a explicar las propiedades de la psiquiatría actual que el DSM-5 representa, y a comprender las fuertes críticas que se le dirigen.

I argue that the aim of belief and the aim of science are both knowledge. The ‘aim of belief’ is to be identified with the product of a properly functioning cognitive system. Science is an institution that is the social, functional analogue of a cognitive system, and its aim is the same as that of belief. In both cases it is knowledge rather than true belief that is the product of proper functioning.

The aim of the paper is to clarify the concept of scientific ignorance: what is it, what are its sources, and when is it epistemically detrimental to science. While some sources of scientific ignorance come inevitably with the process of knowledge acquisition, others are deliberately created. The former includes selection processes, inductive reasoning, and cognitive biases, while the latter includes scientific fraud. Another important source of scientific ignorance appears when scientists introduce methodological biases through micro-decisions in the research process. I provide three examples from medical research to illustrate this point. I argue further that methodological biases present a challenge, in so far as they are no easily classifiable as deliberate: they might also be the result of entrenched research practices within a scientific community. Strategies to identify and prevent methodological biases in research should take into account such difference.

With increasing publication and data production, scientific knowledge presents not simply an achievement but also a challenge. Scientific publications and data are increasingly treated as resources that need to be digitally ‘managed.’ This gives rise to scientific Knowledge Management (KM): second-order scientific work aiming to systematically collect, take care of and mobilise first-hand disciplinary knowledge and data in order to provide new first-order scientific knowledge. We follow the work of Leonelli (2014, 2016), Efstathiou (2012, 2016) and Hislop (2013) in our analysis of the use of KM in semantic systems biology. Through an empirical philosophical account of KM-enabled biological research, we argue that KM helps produce new first-order biological knowledge that did not exist before, and which could not have been produced by traditional means. KM work is enabled by conceiving of ‘knowledge’ as an object for computational science: as explicated in the text of biological articles and computable via appropriate data and metadata. However, these founded knowledge concepts enabling computational KM risk focusing on only computationally tractable data as knowledge, underestimating practice-based knowing and its significance in ensuring the validity of ‘manageable’ knowledge as knowledge.

Contemporary interdisciplinary research is often described as bringing some important changes in the structure and aims of the scientific enterprise. Sometimes, it is even characterized as a sort of Kuhnian scientific revolution. In this paper, the analogy between interdisciplinarity and scientific revolutions will be analysed. It will be suggested that the way in which interdisciplinarity is promoted looks similar to how new paradigms were described and defended in some episodes of revolutionary scientific change. However, contrary to what happens during some scientific revolutions, the rhetoric with which interdisciplinarity is promoted does not seem to be accompanied by a strong agreement about what interdisciplinarity actually is. In the end, contemporary interdisciplinarity could be defined as being in a ‘pre-paradigmatic’ phase, with the very talk promoting interdisciplinarity being a possible obstacle to its maturity.