Saúl Pérez-González

Adaptive optics play a crucial role in acquiring high-quality images in ground-based telescopes. The primary challenge lies in mitigating the impact of atmospheric turbulence on captured light, which induces aberrations in the wavefront, resulting in distorted images. Over the past decade, several studies have been carried out exploring the application of predictive models to this technology, aiming to increase the efficiency of the process, reduce costs as well as improve the response time of the system. In particular, neural networks have been proven to be a powerful tool toward this end. This study assesses the potential of novel training and data processing techniques, while also evaluating the influence of specific parameters such as input data length and system frequency. In this study, the ideal training parameters are first analyzed through several low-complexity models, followed by the integration of more sophisticated neural networks. The feasibility of predicting slopes from sensor images has been demonstrated, validating the use of image-based sensor data. These results also open the path for exploring multi-scale sensors, like pyramid sensors, which could further improve predictions by capturing data at different spatial resolutions.

In contemporary biology and philosophy of biology, there remains a lack of consensus regarding the nature of natural selection. Over recent decades, a wide variety of interpretations and approaches to natural selection have been proposed. In a recent paper, Wei (Citation2024, “Natural Selection, Mechanism and Phenomenon.” International Studies in the Philosophy of Science 37:37–50.) critically examines current mechanistic accounts of natural selection. For this purpose, he focuses on a particular aspect of the mechanistic approach: a mechanism is always a mechanism for a phenomenon. Wei introduces two criteria that the phenomenon assigned to natural selection should fulfil and, on this basis, evaluates phenomena previously considered. Contrary to Pérez-González and Luque (Citation2019, “Evolutionary Causes as Mechanisms: A Critical Analysis.” History and Philosophy of the Life Sciences 41:13.), Wei argues that the increase in the frequency of fitter traits cannot be considered the phenomenon for which natural selection is responsible. The aim of this paper is to analyse and engage with Wei’s critique. We contend that his arguments face significant challenges and do not substantially weaken the assignment of increasing the frequency of fitter traits.

Science communication is a broad field and involves very diverse activities. This paper aims to illuminate and partly systematise the diversity of science communication. We focus on three important dimensions: size of the audience, frequency of interaction, and decision-making relevance. Based on them, we introduce a three-dimensional space of science communication wherein particular scenarios can be located. We argue that relevant challenges for science communication are particularly associated with certain areas of this space. Based on the proposed framework, we also address potential strategies and developments in science communication.

In the last decades, the evidence-based approach has become dominant in the biomedical sciences. Its influence has extended from clinical practice to other areas such as drug regulation and public health. Nonetheless, given the limitations of the evidence-based framework, a more pluralist approach towards evidence has been demanded. In this sense, evidential pluralism holds that evidence of mechanisms should complement statistical evidence. This paper aims to explore and discuss the prospects of evidential pluralism in public health. First, we will analyse the evidential standards of the International Agency for Research on Cancer. Those standards are argued to be in line with the pluralist approach. Second, we will consider the main challenges for gathering evidence of mechanisms in public health. For that purpose, diverse types of evidence of mechanisms will be identified. And third, we will assess the role of agent-based models as mechanistic studies. Based on a case study, we will discuss whether agent-based models can provide evidence of mechanisms and mitigate the abovementioned challenges.

The aim of this paper is to shed light on and critically evaluate the potential impact of evidential pluralism in the realm of pharmaceutical regulation. In the complex landscape of drug evaluation and approval, the role of evidence is pivotal. Firstly, we delve into the role of evidence of mechanisms within drug regulation, with a particular focus on the Accelerated Approval pathway administered by the US Food and Drug Administration. Our analysis reveals that this program, in practice, closely resembles a pluralistic approach to evidence. Secondly, we undertake a thorough examination of the reliability of evidential pluralism within the context of drug regulation. We dissect and discuss the open-cases approach recently put forth by Sung and Holman. While acknowledging its potential merits, we also identify and articulate significant limitations inherent in this approach. Finally, we propose an alternative empirical approach that centres on the real-world outcomes of regulatory programs.

Following Goldman’s seminal work, most contemporary philosophical contributions on the novice-expert relation have adopted a normative, expert-focused approach. In this paper, we aim to shift the focus of the philosophical analysis towards the characteristics of the novices, and how they might determine the choices that experts make. On the bases of recent empirical evidence from social psychology, we discuss how novices evaluate the messages that they receive and distinguish diverse kinds of novices according to their competence in message assessment. Building on that analysis, we discuss the difficulties of approaches to expertise that focus only on the standpoint of novices or assume novices are homogeneous. In our analysis, we introduce the standpoint of experts, and we pay special attention to the heterogeneity of novices. This approach allows us to identify and address the difficulties faced by experts in the context of science communication. In the last part of the paper, we characterise and discuss the problem of experts when choosing a strategy in the issuing of a public campaign to advise or inform certain populations of novices.

Extrapolation of causal claims from study populations to other populations of interest is a problematic issue. The standard approach in experimental research, which prioritises randomized controlled trials and statistical evidence, is not devoid of difficulties. Granted that, it has been defended that evidence of mechanisms is indispensable for causal extrapolation. We argue, contrarily, that this sort of evidence is not indispensable. Nonetheless, we also think that occasionally it may be helpful. In order to clarify its relevance, we introduce a distinction between a positive and a negative role of evidence of mechanisms. Our conclusion is that the former is highly questionable, but the latter may be a trustworthy resource for causal extrapolation.

Explanation is one of the main aims of science. Scientists frequently seek to explain scientific phenomena. This paper addresses the relationship between scientific explanation and science denialism. In it, explanatory wars are introduced. An explanatory war is a situation in which the standard scientific explanation of a phenomenon is systematically denied by a group of people. It is argued that the mechanistic account of scientific explanation is helpful in order to face this kind of science denialism. Mechanistic explanations are resistant to the arguments usually raised by denialists. The relevant role of mechanistic explanations is illustrated by the case of tobacco disease denialism during the second half of twentieth century.

Epidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials—some real examples are invoked—we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we make a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked.

The past two decades have witnessed an increase in interest in social mechanisms and mechanistic explanations of social macro-phenomena. This paper addresses the question of what the components of social mechanisms in mechanistic explanations of social macro-phenomena must be. Analytical sociology’s initial position and the main new proposals by analytical sociologists are discussed. It is argued that all of them are faced with outstanding difficulties. Subsequently, a minimal requirement regarding the components of social mechanisms is introduced. It is held that a component of a social mechanism in a mechanistic explanation of a social macro-phenomenon must not have the explanandum phenomenon as a part of it.

In this paper, we address the question whether a mechanistic approach can account for evolutionary causes. The last decade has seen a major attempt to account for natural selection as a mechanism. Nevertheless, we stress the relevance of broadening the debate by including the other evolutionary causes inside the mechanistic approach, in order to be a legitimate conceptual framework on the same footing as other approaches to evolutionary theory. We analyse the current debate on natural selection as a mechanism, and extend it to the rest of the evolutionary causes. We focus on three approaches that we call the stochastic view, the functional view, and the minimalist view. We argue that all of them are unable to account for evolutionary causes as mechanisms. It is concluded that the current mechanistic proposals cannot be accepted as a common framework for evolutionary causes. Finally, we outline some guidelines and requirements that any mechanistic proposal should meet in order to be applied to evolutionary theory.