This paper presents a phenomenological analysis of shame in social networks. Initially, I examine Sartre’s (1956) account of the look and shame along with Dolezal’s (2017) reinterpretation. I then explore how shame is negotiated in online interactions arguing that, in social networking systems (SNSs), shame is banned. Since subjects are constantly visible when posting content, they tend to share material that minimizes the risk of shame’s thunderstruck. Yet, this shameless self-presentation raises complex phenomenological intricacies regarding personal identity and self-identification: (...) in SNSs individuals strive for self-completion in posts, attempting to solve their otherwise irresolvable self-individuation process. (shrink)

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in (...) order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. (shrink)

Neural networks and deep learning have significantly advanced the field of artificial intelligence, offering solutions to complex problems such as image recognition, natural language processing, and decision-making tasks. This paper explores the principles of neural networks, particularly deep learning models, their evolution, applications, challenges, and potential future directions. A comprehensive analysis of key algorithms, architectures, and advancements is provided, with an emphasis on the practical implications of deep learning in various domains. By understanding the foundations of neural (...) class='Hi'>networks, we can better address issues of scalability, interpretability, and computational efficiency. (shrink)

Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates theorizing (...) about causality. Together, hierarchy and causality theories explain how biorealities form and diversify with time. This paper analyzes how Applied EE redefines both hierarchy and causality theories in the light of the recent explosion of network approaches to causal reasoning associated with studies on reticulate and macroevolution. Causality theories have often been framed from within a rigid, ladder-like hierarchy theory where the rungs of the ladder represent the different levels, and the elements on the rungs represent the evolving units. Causality then is either defined reductionistically as an upward movement along the strands of a singular hierarchy, or holistically as a downward movement along that same hierarchy. Upward causation theories thereby analyze causal processes in time, i.e. over the course of natural history or phylogenetically, as Darwin and the founders of the Modern Synthesis intended. Downward causation theories analyze causal processes in space, ontogenetically or ecologically, as the current eco-evo-devo schools are evidencing. This work demonstrates how macroevolution and reticulate evolution theories add to the complexity by examining reticulate causal processes in space–time, and the interactional hierarchies that such studies bring forth introduce a new form of causation that is here called reticulate causation. Reticulate causation occurs between units and levels belonging to different as well as to the same ontological hierarchies. This article concludes that beyond recognizing the existence of multiple units, levels, and mechanisms or processes of evolution, also the existence of multiple kinds of evolutionary causation as well as the existence of multiple evolutionary hierarchies needs to be acknowledged. This furthermore implies that evolution is a pluralistic process divisible into different kinds. (shrink)

Neural network training frequently exhibits instability and collapse phenomena. Common examples include exploding gradients, vanishing gradients, mode collapse in generative models, unstable loss oscillations, and representation collapse. These behaviours are typically treated as separate optimisation problems arising from algorithm design or numerical instability. This paper presents a structural interpretation of neural network collapse modes using the Paton System framework. Within this interpretation, collapse phenomena are understood as manifestations of inadmissible recursive updates within parameter space. When training updates remain within an (...) admissible corridor of constraint compatibility, learning converges and stable representations emerge. When recursive updates exceed admissible limits, collapse modes appear. This interpretation unifies several well-known neural network training failures as structural inadmissibility events within recursive learning systems. The analysis demonstrates how continuation and collapse in neural network training can be understood through admissibility conditions governing recursive systems. Within the Paton System architecture, neural network collapse modes represent a Tier-7 domain instantiation within computational systems. (shrink)

Moral Outrage Networks: The Sociology of Digital Anger examines how anger has become one of the dominant organising forces of contemporary moral and political life. Rather than treating outrage as an emotional excess, a media pathology, or a democratic failure, the book argues that moral anger is a structural condition of morality itself. Wherever moral boundaries exist, anger emerges as the mechanism through which violations are detected, communicated, and sanctioned. In digital societies, this function has been absorbed into networked (...) communication systems that amplify, stabilise, and monetise outrage. -/- The book develops the concept of moral outrage networks to describe how platforms, institutions, and publics co-produce continuous cycles of moral accusation and response. Drawing on moral psychology, sociology, media theory, and political philosophy, it shows how moral disagreement increasingly takes the form of permanent indignation rather than deliberation. Moral outrage networks do not merely reflect conflict over values; they actively organise political identities, attention economies, and symbolic power. -/- Through a genealogy of anger across historical media regimes, the book traces how moral emotions were once embedded in local communities and institutions, and how digital infrastructures have transformed anger into an always-on, scalable resource. It analyses algorithmic amplification, outrage exploitation, and contemporary propaganda models, demonstrating how moral emotions are operationalised within platform capitalism. -/- The final sections confront the ethical paradox at the heart of digital moral life. If anger is necessary for morality to exist at all, how can it be restrained without undermining moral vigilance itself? The book does not offer prescriptions for eliminating outrage, but instead clarifies the conditions under which moral anger retains its critical function, and the conditions under which it becomes self-reinforcing, corrosive, or nihilistic. In doing so, it provides a sociological framework for understanding polarisation, media distrust, and the transformation of moral authority in networked societies. (shrink)

This chapter introduces empirical network analysis (ENA) as a toolbox to complement other methods in philosophy of science. It aims to provide a hands-on introduction to ENA for philosophers of science and to discuss the usefulness of ENA for addressing questions of interest to philosophy of science. We accompany our account by an in-depth consideration of two examples of ENA to reflect not only on the potentials but also on the challenges of ENA. The chapter concludes by outlining skills required (...) for concrete applications and pointing to several resources that can be consulted to acquire those skills. (shrink)

This paper aims to propose a relational approach to the study of cognition that can offer a perspective on the cognitive behaviours of plants – sessile organisms without a nervous system – when considered in the reciprocal interrogation of philosophy and the cognitive and ecological sciences. When leveraging the inspiring, clarifying, and occasionally heuristic potential of different epistemic tools, plant cognition can be understood as the result of processes constantly shaped by multiple co-constructive relationships between organisms and their ecological niches. (...) Organisms and niches are conceivable as dense multi-functional systems of resources and information interchange. The concepts of network and ramification are fruitful keys to frame forms of dynamic relationships between elements. In their alternatively iconical, metaphorical, and conceptual-modelling potentialities, networks and ramifications have been used to identify different types of relationships and transmitted information. The explanatory and heuristic scope of these two concepts needs to be further investigated when linked to cognitive aspects, especially with regard the concept of ramification. Looking at the plant world, whilst much has been written about networks, relatively little has been said explicitly about ramifications (branching capacity, branching characteristics and habits) and their relationship with aspects related to plant behaviour and cognition. (shrink)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading to novel capabilities (...) of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (shrink)