NCG 4.0 is the latest update of the Network of Cancer Genes, a web-based repository of systems-level properties of cancer genes. In its current version, the database collects information on 537 known (i.e. experimentally supported) and 1463 candidate (i.e. inferred using statistical methods) cancer genes. Candidate cancer genes derive from the manual revision of 67 original publications describing the mutational screening of 3460 human exomes and genomes in 23 different cancer types. For all 2000 cancer genes, duplicability, evolutionary origin, expression, (...) functional annotation, interaction network with other human proteins and with microRNAs are reported. In addition to providing a substantial update of cancer-related information, NCG 4.0 also introduces two new features. The first is the annotation of possible false-positive cancer drivers, defined as candidate cancer genes inferred from large-scale screenings whose association with cancer is likely to be spurious. The second is the description of the systems-level properties of 64 human microRNAs that are causally involved in cancer progression (oncomiRs). Owing to the manual revision of all information, NCG 4.0 constitutes a complete and reliable resource on human coding and non-coding genes whose deregulation drives cancer onset and/or progression. NCG 4.0 can also be downloaded as a free application for Android smart phones. (shrink)

In the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the ‘black box’ and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine learning tools to be (...) interpretable at the algorithmic level to make them trustworthy, as long as they meet some strict empirical desiderata. In this paper, we analyse and develop London’s position. In particular, we make two claims. First, we claim that London’s solution to the problem of trust can potentially address another problem, which is how to evaluate the reliability of ML tools in medicine for regulatory purposes. Second, we claim that to deal with this problem, we need to develop London’s views by shifting the focus from the opacity of algorithmic details to the opacity of the way in which ML tools are trained and built. We claim that to regulate AI tools and evaluate their reliability, agencies need an explanation of how ML tools have been built, which requires documenting and justifying the technical choices that practitioners have made in designing such tools. This is because different algorithmic designs may lead to different outcomes, and to the realization of different purposes. However, given that technical choices underlying algorithmic design are shaped by value-laden considerations, opening the black box of the design process means also making transparent and motivating (technical and ethical) values and preferences behind such choices. Using tools from philosophy of technology and philosophy of science, we elaborate a framework showing how an explanation of the training processes of ML tools in medicine should look like. (shrink)

In the past few years, the ethical ramifications of AI technologies have been at the center of intense debates. Considerable attention has been devoted to understanding how a morally responsible practice of data science can be promoted and which values have to shape it. In this context, ethics and moral responsibility have been mainly conceptualized as compliance to widely shared principles. However, several scholars have highlighted the limitations of such a principled approach. Drawing from microethics and the virtue theory tradition, (...) in this paper, we formulate a different approach to ethics in data science which is based on a different conception of “being ethical” and, ultimately, of what it means to promote a morally responsible data science. First, we develop the idea that, rather than only compliance, ethical decision-making consists in using certain moral abilities, which are cultivated by practicing and exercising them in the data science process. An aspect of virtue development that we discuss here is moral attention, which is the ability of data scientists to identify the ethical relevance of their own technical decisions in data science activities. Next, by elaborating on the capability approach, we define a technical act as ethically relevant when it impacts one or more of the basic human capabilities of data subjects. Therefore, rather than “applying ethics”, data scientists should cultivate ethics as a form of reflection on how technical choices and ethical impacts shape one another. Finally, we show how this microethical framework concretely works, by dissecting the ethical dimension of the technical procedures involved in data understanding and preparation of electronic health records. (shrink)

In the science and values literature, scholars have shown how science is influenced and shaped by values, often in opposition to the ‘value free’ ideal of science. In this paper, we aim to contribute to the science and values literature by showing that the relation between science and values flows not only from values into scientific practice, but also from (allegedly neutral) science to values themselves. The extant literature in the ‘science and values’ field focuses by and large on reconstructing, (...) post hoc, how values have influenced science; our reconstruction of the case studies, instead, aims to show that scientific concepts and methods _too, because of specific identifiable characteristics,_ can promote some values rather than (or at the expense of) others. We explain this bidirectional relation in analogy to debates on the normativity of technical artifacts and on feminist approaches in science, and we illustrate our claims with cases from the health sciences and machine learning. While our arguments in this paper also draw on post hoc reconstructions, we intend to show where, in the science in the making, we should engage not only with the question whether a practice is value-laden, but also how specific conceptual and methodological choices can influence values down the road. All in all, these considerations expand the ways in which philosophers can contribute to more value-aware scientific practices. (shrink)

Recently, biologists have argued that data - driven biology fosters a new scientific methodology; namely, one that is irreducible to traditional methodologies of molecular biology defined as the discovery strategies elucidated by mechanistic philosophy. Here I show how data - driven studies can be included into the traditional mechanistic approach in two respects. On the one hand, some studies provide eliminative inferential procedures to prioritize and develop mechanistic hypotheses. On the other, different studies play an exploratory role in providing useful (...) generalizations to complement the procedure of prioritization. Overall this paper aims to shed light on the structure of contemporary research in molecular biology. (shrink)

Machine learning (ML) has been praised as a tool that can advance science and knowledge in radical ways. However, it is not clear exactly how radical are the novelties that ML generates. In this article, I argue that this question can only be answered contextually, because outputs generated by ML have to be evaluated on the basis of the theory of the science to which ML is applied. In particular, I analyze the problem of novelty of ML outputs in the (...) context of molecular biology. In order to do this, I first clarify the nature of the models generated by ML. Next, I distinguish three ways in which a model can be novel (from the weakest to the strongest). Third, I dissect the way ML algorithms work and generate models in molecular biology and genomics. On these bases, I argue that ML is either a tool to identify instances of knowledge already present and codified, or to generate models that are novel in a weak sense. The notable contribution of ML to scientific discovery in the context of biology is that it can aid humans in overcoming potential bias by exploring more systematically the space of possible hypotheses implied by a theory. (shrink)

The notion of biological function is fraught with difficulties—intrinsically and irremediably so, we argue. The physiological practice of functional ascription originates from a time when organisms were thought to be designed and remained largely unchanged since. In a secularized worldview, this creates a paradox which accounts of functions as selected effect attempt to resolve. This attempt, we argue, misses its target in physiology and it brings problems of its own. Instead, we propose that a better solution to the conundrum of (...) biological functions is to abandon the notion altogether, a prospect not only less daunting than it appears, but arguably the natural continuation of the naturalisation of biology. (shrink)

In the past few years, scholars have been questioning whether the current approach in data ethics based on the higher level case studies and general principles is effective. In particular, some have been complaining that such an approach to ethics is difficult to be applied and to be taught in the context of data science. In response to these concerns, there have been discussions about how ethics should be “embedded” in the practice of data science, in the sense of showing (...) how ethical issues emerge in small technical choices made by data scientists in their day-to-day activities, and how such an approach can be used to teach data ethics. However, a precise description of how such proposals have to be theoretically conceived and could be operationalized has been lacking. In this article, we propose a full-fledged characterization of ‘embedding’ ethics, and how this can be applied especially to the problem of teaching data science ethics. Using the emerging model of ‘microethics’, we propose a way of teaching daily responsibility in digital activities that is connected to the higher level ethical challenges discussed in digital/data ethics. We ground this microethical approach into a virtue theory framework, by stressing that the goal of a microethics is to foster the cultivation of moral virtues. After delineating this approach of embedding ethics in theoretical detail, this article discusses a concrete example of how such a ‘micro-virtue ethics’ approach could be practically taught to data science students. (shrink)

We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex, the less explanatory it will be. Since machine learning increases its performances when more components are added, then it generates (...) models which are not intelligible, and hence not explanatory. (shrink)

In its last round of publications in September 2012, the Encyclopedia Of DNA Elements (ENCODE) assigned a biochemical function to most of the human genome, which was taken up by the media as meaning the end of ‘Junk DNA’. This provoked a heated reaction from evolutionary biologists, who among other things claimed that ENCODE adopted a wrong and much too inclusive notion of function, making its dismissal of junk DNA merely rhetorical. We argue that this criticism rests on misunderstandings concerning (...) the nature of the ENCODE project, the relevant notion of function and the claim that most of our genome is junk. We argue that evolutionary accounts of function presuppose functions as ‘causal roles’, and that selection is but a useful proxy for relevant functions, which might well be unsuitable to biomedical research. Taking a closer look at the discovery process in which ENCODE participates, we argue that ENCODE’s strategy of biochemical signatures successfully identified activities of DNA elements with an eye towards causal roles of interest to biomedical research. We argue that ENCODE’s controversial claim of functionality should be interpreted as saying that 80 % of the genome is engaging in relevant biochemical activities and is very likely to have a causal role in phenomena deemed relevant to biomedical research. Finally, we discuss ambiguities in the meaning of junk DNA and in one of the main arguments raised for its prevalence, and we evaluate the impact of ENCODE’s results on the claim that most of our genome is junk. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...) particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

In biology—as in other scientific fields—there is a lively opposition between big and small science projects. In this commentary, I try to contextualize this opposition in the field of biomedicine, and I argue that, at least in this context, big science projects should come first.

A recent article by Herzog provides a much-needed integration of ethical and epistemological arguments in favor of explicable AI in medicine. In this short piece, I suggest a way in which its epistemological intuition of XAI as “explanatory interface” can be further developed to delineate the relation between AI tools and scientific research.

Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this article, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ are new types of material manipulations and are important not because of their explanatory force, but because of the interventionist strategies they afford. This (...) is a distinction between aspects of the same model. In molecular biology, models may be treated either as ‘models of’ or as ‘models for’. By analysing the discovery and characterization of restriction–modification systems and their exploitation for DNA cloning and mapping, I identify the differences between treating a model as a ‘model of’ or as a ‘model for’. These lie in the cognitive disposition of the modeller towards the model: a modeller will look at a model as a ‘model of’ if interested in its explanatory force, or as a ‘model for’ if interested in the material manipulations it can possibly afford. (shrink)

We claim that in contemporary studies in molecular biology and biomedicine, the nature of ‘manipulation’ and ‘intervention’ has changed. Traditionally, molecular biology and molecular studies in medicine are considered experimental sciences, whereas experiments take the form of material manipulation and intervention. On the contrary “big science” projects in biology focus on the practice of data mining of biological databases. We argue that the practice of data mining is a form of intervention although it does not require material manipulation. We also (...) suggest that material manipulation, although still present in in the practice of data mining, fulfill a different epistemic role. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...) particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

In the last decade, Systems Biology has emerged as a conceptual and explanatory alternative to reductionist-based approaches in molecular biology. However, the foundations of this new discipline need to be fleshed out more carefully. In this paper, we claim that a relational ontology is a necessary tool to ground both the conceptual and explanatory aspects of Systems Biology. A relational ontology holds that relations are prior—both conceptually and explanatory—to entities, and that in the biological realm entities are defined primarily by (...) the context they are embedded within—and hence by the web of relations they are part of. (shrink)

The applications of machine learning and deep learning to the natural sciences has fostered the idea that the automated nature of algorithmic analysis will gradually dispense human beings from scientific work. In this paper, I will show that this view is problematic, at least when ML is applied to biology. In particular, I will claim that ML is not independent of human beings and cannot form the basis of automated science. Computer scientists conceive their work as being a case of (...) Aristotle’s poiesis perfected by techne, which can be reduced to a number of straightforward rules and technical knowledge. I will show a number of concrete cases where at each level of computational analysis, more is required to ML than just poiesis and techne, and that the work of ML practitioners in biology needs also the cultivation of something analogous to phronesis, which cannot be automated. But even if we knew how to frame phronesis into rules, still this virtue is deeply entrenched in our biological constitution, which computers lack. Whether computers can fully perform scientific practice independently of humans is an ill-posed question. (shrink)

It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. Using healthcare as an example, (...) attention to sociocultural conversion factors relevant to health can help in navigating technical choices that require broader considerations of the sociotechnical system, such as metric tradeoffs in model validation, resulting in better ethical and technical choices. This approach promotes awareness of the ethical dimension of technical choices by data scientists and others, and that can foster the cultivation of 'ethical skills' as integral to data science. (shrink)

I diagnose the current debate between epistemological and ontological emergentism as a Kantian antinomy, which has reasonable but irreconcilable thesis and antithesis. Kantian antinomies have recently returned to contemporary philosophy in part through the work of Luciano Floridi, and the method of levels of abstraction. I use a thought experiment concerning a computer simulation to show how to resolve the epistemological/ontological antinomy about emergence. I also use emergentism and simulations in artificial life to illuminate both levels of abstraction and theoretical (...) challenge for building intelligent agents. (shrink)

In this chapter, we propose a non-traditional RCR training in data science that is grounded in a virtue theory framework. First, we delineate the approach in more theoretical detail by discussing how the goal of RCR training is to foster the cultivation of certain moral abilities. We specify the nature of these ‘abilities’: while the ideal is the cultivation of virtues, the limited space allowed by RCR modules can only facilitate the cultivation of superficial abilities or proto-virtues, which help students (...) to familiarize themselves with moral and political issues in the data science environment. Third, we operationalize our approach by stressing that (proto-)virtue acquisition (like skill acquisition) occurs through the technical and social tasks of daily data science activities, where these repetitive tasks provide the opportunities to develop (proto-)virtue capacity and to support the development of ethically robust data systems. Finally, we discuss a concrete example of implementing this approach. In particular, we describe how this method is applied to teach data ethics to students participating in the CODATA-RDA Data Science Summer Schools. (shrink)

In the past few years, calls for integrating ethics modules in engineering curricula have multiplied. Despite this positive trend, a number of issues with these ‘embedded’ programs remains. First, learning goals are underspecified. A second limitation is the conflation of different dimensions under the same banner, in particular confusion between ethics curricula geared towards addressing the ethics of individual conduct and curricula geared towards addressing ethics at the societal level. In this article, we propose a tripartite framework to overcome these (...) difficulties. Our framework analytically decomposes an ethics module into three dimensions. First, there is the ethical dimension, which pertains to the learning goals. Second, there is the moral dimension, which addresses the moral relevance of engineers’ conduct. Finally, there is the political dimension, which scales up issues of moral relevance at the civic level. All in all, our framework has two advantages. First, it provides analytic clarity, i.e. it enables course instructors to locate ethical dilemmas in either the moral or political realm and to make use of the tools and resources from moral and/or political philosophy. Second, it depicts a comprehensive ethical training, which enables students to both reason about moral issues in the abstract, and to socially contextualize potential solutions. (shrink)

Making public policy choices based on available scientific evidence is an ideal condition for any policy making. However, the mechanisms governing these scenarios are complex, non-linear, and, alongside the medical-health and epidemiological issues, involve socio-economic, political, communicative, informational, ethical and epistemological aspects. In this article we analyze the role of scientific evidence when implementing political decisions that strictly depend on it, as in the case of the COVID-19 pandemic. In carrying out this analysis, we will focus above all on the (...) Italian case. This, on the one hand, precisely because Italy led the way regarding the containment policies of the pandemic. Secondly, the government's action was immediately criticized in various respects. Some were calling into question not only the cumbersome political mechanisms, but also suggesting a scarce ability to take scientific evidence into account. On other fronts, there are those who have criticized Italy for its blind and uncritical faith in science and for the paternalism of its decisions. This debate therefore offers the possibility of dealing with some aspects concerning scientific results and their implementation at the political level from the point of view of a political philosophy of science, roughly in the spirit suggested by John Dupré. (shrink)

In the last decade, robustness has been extensively mentioned and discussed in biology as well as in the philosophy of the life sciences. Nevertheless, from both fields, someone has affirmed that this debate has resulted in more semantic confusion than in semantic clearness. Starting from this claim, we wish to offer a sort of prima facie map of the different usages of the term. In this manner we would intend to predispose a sort of “semantic platform” which could be exploited (...) by those who wish to discuss or simply use it. We do this by starting from a core distinction between the robustness of representations, which is a philosophy of science issue, and the representations of robustness, which instead pertains to science. We illustrate our proposal with examples from biology, physics and mathematics. (shrink)

It is widely held that all lies are assertions: the traditional definition of lying entails that, in order to lie, speakers have to assert something they believe to be false. It is also widely held that assertion contrasts with presupposition and, in particular, that one cannot assert something by presupposing it. Together, these views imply that speakers cannot lie with presuppositions—a view that Andreas Stokke has recently explicitly defended. The aim of this paper is to argue that speakers can lie (...) with presuppositions, and to discuss some of the implications this outcome has for current research on lying, assertion and presupposition. (shrink)

Comprehensive in scope and research, this book will be a crucial resource for researchers in the medical sciences, as well as teachers and students alike.

Because rate of force development is an emerging outcome measure for the assessment of neuromuscular function in unfatigued conditions, and it represents a valid alternative/complement to the classical evaluation of pure maximal strength, this scoping review aimed to map the available evidence regarding RFD as an indicator of neuromuscular fatigue. Thus, following a general overview of the main studies published on this topic, we arbitrarily compared the amount of neuromuscular fatigue between the “gold standard” measure and peak, early and late (...) RFD. Seventy full-text articles were included in the review. The most-common fatiguing exercises were resistance exercises, endurance exercises/locomotor activities, isokinetic contractions, and simulated/real sport situations. The most widely tested tasks were knee extension and plantar flexion. The reason for evaluating RFD was lacking in 36% of the studies. On average, the amount of fatigue for MVF was comparable to late RFD but lower compared to both peak RFD and early RFD. Even if the rationale for evaluating RFD in the fatigued state was often lacking and the specificity between test task and fatiguing exercise characteristics was not always respected in the included studies, RFD seems to be a valid indicator of neuromuscular fatigue. Based on our arbitrary analyses, peak RFD and early phase RFD appear even to be more sensitive to quantify neuromuscular fatigue than MVF and late phase RFD. (shrink)

The distinction between lying and mere misleading is commonly tied to the distinction between saying and conversationally implicating. Many definitions of lying are based on the idea that liars say something they believe to be false, while misleaders put forward a believed-false conversational implicature. The aim of this paper is to motivate, spell out, and defend an alternative approach, on which lying and misleading differ in terms of commitment: liars, but not misleaders, commit themselves to something they believe to be (...) false. This approach entails that lying and misleading involve speech-acts of different force. While lying requires the committal speech-act of asserting, misleading involves the non-committal speech-act of suggesting. The approach leads to a broader definition of lying that can account for lies that are told while speaking non-literally or with the help of presuppositions, and it allows for a parallel definition of misleading, which so far is lacking in the debate. (shrink)

We analyze recent criticisms of the use of hyperreal probabilities as expressed by Pruss, Easwaran, Parker, and Williamson. We show that the alleged arbitrariness of hyperreal fields can be avoided by working in the Kanovei–Shelah model or in saturated models. We argue that some of the objections to hyperreal probabilities arise from hidden biases that favor Archimedean models. We discuss the advantage of the hyperreals over transferless fields with infinitesimals. In Paper II we analyze two underdetermination theorems by Pruss and (...) show that they hinge upon parasitic external hyperreal-valued measures, whereas internal hyperfinite measures are not underdetermined. (shrink)

To understand the endogenous components of the event-related brain potential (ERP), we must use data about the components' antecedent conditions to form hypotheses about the information-processing function of the underlying brain activity. These hypotheses, in turn, generate testable predictions about the consequences of the component. We review the application of this approach to the analysis of the P300 component. The amplitude of the P300 is controlled multiplicatively by the subjective probability and the task relevance of the eliciting events, whereas its (...) latency depends on the duration of stimulus evaluation. These and other factors suggest that the P300 is a manifestation of activity occurring whenever one's model of the environment must be revised. Tests of three predictions based on this “context updating” model are reviewed. Verleger's critique is based on a misconstrual of the model as well as a partial and misleading reading of the relevant literature. (shrink)

Dieses Buch bietet die erste systematische Interpretation von Husserls Ideen für eine reine Phänomenologie und phänomenologische Philosophie anhand der neuen kritischen Edition von Ideen II. Es ermöglicht eine phänomenologische Auslegung des allgemein-metaphysischen Problems, wie physische, mentale und soziale Tatsachen zusammenhängen. Das Buch diskutiert und interpretiert detailliert einige von Husserls zentralen Konzeptionen und zeigt die Konsequenzen seines Denkansatzes und seiner Theorieentwicklung. Natur und Gemeingeist sind Husserl zufolge die Grundbegriffe der naturalistischen und der personalistischen Einstellungen und dienen als Leitfaden der Unterscheidung zwischen (...) Natur- und Geisteswissenschaften. In der kritischen Auseinandersetzung mit diesem wissenschaftstheoretischen Dualismus führt Husserl den Habitus-Begriff methodisch ein, um das Verhältnis von Natur- und Sozialontologie aus der konkreten Erfahrung heraus phänomenologisch neu zu deuten, womit der spätere, anti-dualistische Weg der Lebensweltphänomenologie vorbereitet wird. In Husserls Studien zur Regionalontologie des Gemeingeistes rückt das konkrete Subjekt in den Vordergrund der Intentionalitätsanalyse, indem die sinntragenden Elemente der Inaktualitität auf Habitualisierungsprozesse und die Intersubjektivität auf Sozialisierungsstufen zurückgeführt werden. Dank der durch den Habitus-Begriff ermöglichten klaren Unterscheidung zwischen konstituierender Aktualität und konstitutiver Relevanz des inaktuellen Horizonts kann Husserls Philosophie des Geistes als individualistisch und holistisch zugleich gelten. Dieser ontologischen Position entspricht auch Husserls sozialepistemologische Ansicht, dass sich Wissenschaften erst im Rahmen idealisierter Sozialstrukturen entfalten können. Durch diese idealisierenden Operationen wird die Konstitution der Objektivität möglich, welche die Wissenschaften anstreben. Deren Rationalität ist deshalb in ihren konkreten und idealisierten Sozialitätsstufen und Habitualitäten zu befragen. (shrink)

Drawing on evolutionary epistemology, process ontology, and a social-cognition approach, this book suggests cognitive evolution, an evolutionary-constructivist social and normative theory of change and stability of international social orders. It argues that practices and their background knowledge survive preferentially, communities of practice serve as their vehicle, and social orders evolve. As an evolutionary theory of world ordering, which does not borrow from the natural sciences, it explains why certain configurations of practices organize and govern social orders epistemically and normatively, and (...) why and how these configurations evolve from one social order to another. Suggesting a multiple and overlapping international social orders' approach, the book uses three running cases of contested orders - Europe's contemporary social order, the cyberspace order, and the corporate order - to illustrate the theory. Based on the concepts of common humanity and epistemological security, the author also submits a normative theory of better practices and of bounded progress. (shrink)

ABSTRACTA probability model is underdetermined when there is no rational reason to assign a particular infinitesimal value as the probability of single events. Pruss claims that hyperreal probabilities are underdetermined. The claim is based upon external hyperreal-valued measures. We show that internal hyperfinite measures are not underdetermined. The importance of internality stems from the fact that Robinson’s transfer principle only applies to internal entities. We also evaluate the claim that transferless ordered fields may have advantages over hyperreals in probabilistic modeling. (...) We show that probabilities developed over such fields are less expressive than hyperreal probabilities. (shrink)

In Emanuel Adler's distinctive constructivist approach to international relations theory, international practices evolve in tandem with collective knowledge of the material and social worlds. This book - comprising a selection of his journal publications, a new introduction and three previously unpublished articles - points IR constructivism in a novel direction, characterized as 'communitarian'. Adler's synthesis does not herald the end of the nation-state; nor does it suggest that agency is unimportant in international life. Rather, it argues that what mediates between (...) individual and state agency and social structures are communities of practice, which are the wellspring and repositories of collective meanings and social practices. The concept of communities of practice casts new light on epistemic communities and security communities, helping to explain why certain ideas congeal into human practices and others do not, and which social mechanisms can facilitate the emergence of normatively better communities. (shrink)

In this paper we show how some reasoning, though fallacious, can appear to be attractive and useful for beings-like-us. Although they do not provide conclusive evidence to support or reject a certain claim the way scientific statements do, they tell us something interesting about how humans build up their arguments and reasons. First of all, we will consider and investigate three main types of fallacies: argumentum ad hominem, argumentum ad verecundiam, and argumentum ad populum. These three fallacies are traditionally considered (...) as examples of a broader category called ignoratio elenchi. Secondly, we show how people who commit these fallacies rely on information about other human beings in their reasoning. That is, they do not follow certain logical procedures that eventually lead them to correct conclusions. But they simply make use of others as social characters. For example, being an authority, being an expert, being part of a class, etc., become the substitutes for more direct evidence to support a certain claim or to make an argument more appealing. (shrink)

Dieses Buch bietet die erste systematische Interpretation von Husserls Ideen für eine reine Phänomenologie und phänomenologische Philosophie anhand der neuen kritischen Edition von Ideen II. Es ermöglicht eine phänomenologische Auslegung des allgemein-metaphysischen Problems, wie physische, mentale und soziale Tatsachen zusammenhängen. Das Buch diskutiert und interpretiert detailliert einige von Husserls zentralen Konzeptionen und zeigt die Konsequenzen seines Denkansatzes und seiner Theorieentwicklung. Natur und Gemeingeist sind Husserl zufolge die Grundbegriffe der naturalistischen und der personalistischen Einstellungen und dienen als Leitfaden der Unterscheidung zwischen (...) Natur- und Geisteswissenschaften. In der kritischen Auseinandersetzung mit diesem wissenschaftstheoretischen Dualismus führt Husserl den Habitus-Begriff methodisch ein, um das Verhältnis von Natur- und Sozialontologie aus der konkreten Erfahrung heraus phänomenologisch neu zu deuten, womit der spätere, anti-dualistische Weg der Lebensweltphänomenologie vorbereitet wird. In Husserls Studien zur Regionalontologie des Gemeingeistes rückt das konkrete Subjekt in den Vordergrund der Intentionalitätsanalyse, indem die sinntragenden Elemente der Inaktualitität auf Habitualisierungsprozesse und die Intersubjektivität auf Sozialisierungsstufen zurückgeführt werden. Dank der durch den Habitus-Begriff ermöglichten klaren Unterscheidung zwischen konstituierender Aktualität und konstitutiver Relevanz des inaktuellen Horizonts kann Husserls Philosophie des Geistes als individualistisch und holistisch zugleich gelten. Dieser ontologischen Position entspricht auch Husserls sozialepistemologische Ansicht, dass sich Wissenschaften erst im Rahmen idealisierter Sozialstrukturen entfalten können. Durch diese idealisierenden Operationen wird die Konstitution der Objektivität möglich, welche die Wissenschaften anstreben. Deren Rationalität ist deshalb in ihren konkreten und idealisierten Sozialitätsstufen und Habitualitäten zu befragen. (shrink)

In this paper I will discuss some issues related to a recent trend in experimental philosophy (or x-phi), and try to show the reasons of its late (and scarce) involvement with aesthetics, compared to other areas of philosophical investigation. In order to do this, it is first necessary to ask how an autonomous experimental philosophy of aesthetics could be related to the long-standing tradition of psychological experimental aesthetics. After distinguishing between a “narrow” and a “broad” approach of experimental philosophy, I (...) will then make a distinction between topics in aesthetics pertaining to perceptual and cognitive processes, and traditional issues involved in the analysis of general and culturally-laden concepts. The narrow program of experimental philosophy, focused on the investigation of folk intuitions, is particularly effective only when two general conditions are met: the use of hypothetical scenarios (testing of thought experiments) and the heuristic role of folk intuitions in drawing philosophically relevant conclusions. I will argue that, when aesthetics is concerned, these requirements are not easily met. These difficulties notwithstanding, I will support a pluralistic view where aesthetics is revealed as an instructive example of how experimental approaches and traditional “armchair” philosophy integrate, and enrich each other. (shrink)

Many recent definitions of lying are based on the notion of what is said. This paper argues that says-based definitions of lying cannot account for lies involving non-literal speech, such as metaphor, hyperbole, loose use or irony. It proposes that lies should instead be defined in terms of assertion, where what is asserted need not coincide with what is said. And it points to possible implications this outcome might have for the ethics of lying.