In this paper, we present a way to translate the metainferences of a mixed metainferential system into formulae of an extended-language system, called its associated σ-system. To do this, the σ-system will contain new operators (one for each standard), called the σ operators, which represent the notions of "belonging to a (given) standard". We first prove, in a model-theoretic way, that these translations preserve (in)validity. That is, that a metainference is valid in the base system if and only if its (...) translation is a tautology of its corresponding σ-system. We then use these results to obtain other key advantages. Most interestingly, we provide a recipe for building unlabeled sequent calculi for σ-systems. We then exemplify this with a σ-system useful for logics of the ST family, and prove soundness and completeness for it, which indirectly gives us a calculus for the metainferences of all those mixed systems. Finally, we respond to some possible objections and show how our σ-framework can shed light on the “obeying” discussion within mixed metainferential contexts. (shrink)

The observational-theoretical distinction has played a central role in the philosophy of science from logical empiricism onwards. Developments within the discipline have shown that such a distinction hides two non-identical distinctions: observational vs. non-observational and theoretical vs. non-theoretical. Likewise, metatheoretical structuralism proposed a more sophisticated treatment of theoricity in terms of the operational dependence or independence of concepts from the theories in which they appear. Despite this remarkable sophistication, it is still generally accepted that the distinction between theoretical and non-theoretical (...) concepts coincides with the explanatory vs. non-explanatory and testing vs. non-testing distinctions. Ginnobili and Carman argued for the independence between theoricity and explanation. In this paper we will focus on testing and argue for its independence from both theoricity and explanation. To do so, we propose an account of theory testing based on the framework of metatheoretical structuralism and apply it to natural selection theory and cladistics. (shrink)

En un libro reciente McShea y Brandon defienden que la diversidad y la complejidad de la vida se explican, principalmente, por la acción de un principio que llaman “la ley evolutiva de fuerzas cero” o “ZFEL”. Tal principio actuaría de un modo implícito por detrás de muchas explicaciones de la biología, pero nunca habría sido explicitado. Asumiendo que esta idea es interesante, y que los autores en cuestión tienen razón, discutiremos el modo metateórico en que presentan dicho principio, como siendo (...) parte de la teoría de la probabilidad. Esto permite a los autores afirmar que la teoría de la probabilidad brindaría la base reductiva para toda la biología evolutiva (dado que consideran que otros principios, como el de selección natural, también serían parte de la teoría de la probabilidad). Defenderemos que, efectivamente, ZFEL no es propio de la biología únicamente, pero no por formar parte de la teoría de la probabilidad, sino por tratarse de una versión específica del principio de causa común. -/- In a recent book, McShea and Brandon argue that the observed diversity and complexity of life are explainable by a principle they call the “zero-force evolutionary law” or “ZFEL”. Although this principle would be implicit in many explanations given by biologists, it would have never been made explicit. Assuming that this idea is interesting, and that the authors are right, we will discuss the metatheoretical way in which they present said principle, as being a part of probability theory. This allows the authors to claim that probability theory provides the reductive basis for all evolutionary biology. We will defend, in accordance with them, that ZFEL is not a solely biological principle, but not because it is a part of probability theory, but rather because it is a specific version of the principle of common cause. (shrink)

Resumen -/- El objetivo de este trabajo consiste en analizar las relaciones entre los modelos de optimalidad y la selección natural. Defenderemos que esas relaciones pueden dividirse en dos tipos, en tanto hay dos tipos de explicaciones seleccionistas, que llamaremos “históricas” y “ahistóricas”. Las explicaciones históricas revelan como una población dada adquiere un rasgo que es adaptativo en ese ambiente e involucran muchas generaciones, variación, etc. Las explicaciones ahistóricas, explican por qué, en determinado momento, ciertos tipos de organismos tienen un (...) mayor éxito reproductivo que otros. Mostraremos que los modelos de optimalidad pueden jugar un rol en la determinación del explanandum de las explicaciones histórica seleccionistas, esto es, que ayudan a reconocer qué rasgos son adaptativos. Por otra parte, mostramos que los modelos de optimalidad nos permiten determinar a veces la parte del explanans de las explicaciones ahistóricas (particularmente, el concepto de fitness). -/- Abstract -/- The goal of this paper is to analyze the relations between optimality models and natural selection. We contend that these relationships can be divided into two kinds, as there are two kinds of natural selection explanations, which we call “historical” and “ahistorical”. Historical explanations reveal how a given population acquires a trait which is adaptive in its environment, and involves many generations, variations, etc. Ahistorical ones, explain why, at a given moment, certain kinds of organisms have a greater reproductive success than others. We show that optimality models can play a role in determining the explanandum of historical selectionist explanations, that is, they help us to recognize which traits are adaptive. And, on the other hand, that optimality models sometimes allow us to determine part of the explanans of ahistorical explanations (particularly, the concept of fitness). (shrink)

In this article, we provide three generators of propositional formulae for arbitrary languages, which uniformly sample three different formulae spaces. They take the same three parameters as input, namely, a desired depth, a set of atomics and a set of logical constants (with specified arities). The first generator returns formulae of exactly the given depth, using all or some of the propositional letters. The second does the same but samples up-to the given depth. The third generator outputs formulae with exactly (...) the desired depth and all the atomics in the set. To make the generators uniform (i.e. to make them return every formula in their space with the same probability), we will prove various cardinality results about those spaces. (shrink)