We prove that Standardization fails in every nontrivial universe definable in the nonstandard set theory BST, and that a natural characterization of the standard universe is both consistent with and independent of BST. As a consequence we obtain a formulation of nonstandard class theory in the ∈-language.

Consider a set of shuffled observations drawn from a fixed probability distribution over some instance domain. What enables learning of inductive generalizations which proceed from such a set of observations? The scenario is worthwhile because it epistemically characterizes most of machine learning. This kind of learning from observations is also inverse and ill-posed. What reduces the non-uniqueness of its result and, thus, its problematic epistemic justification, which stems from a one-to-many relation between the observations and many learnable generalizations? The paper (...) argues that this role belongs to any complexity regularization which satisfies Norton’s Material Theory of Induction (MTI) by localizing the inductive risk to facts in the given domain. A prime example of the localization is the Lottery Ticket Hypothesis (LTH) about overparameterized neural networks. The explanation of MTI’s role in complexity regularization of neural networks is provided by analyzing the stability of Empirical Risk Minimization (ERM), an inductive rule that controls the learning process and leads to an inductive generalization on the given set of observations. In cases where ERM might become asymptotically unstable, making the justification of the generalization by uniform convergence unavailable, LTH and MTI can be used to define a local stability. A priori, overparameterized neural networks are such cases and the combination of LTH and MTI can block ERM’s trivialization caused by equalizing the strengths of its inductive support for risk minimization. We bring closer the investigation of generalization in artificial neural networks and the study of inductive inference and show the division of labor between MTI and the optimality justifications (developed by Gerhard Schurz) in machine learning. (shrink)

The paper is devoted to Nikolay Lossky who was one of the leading Russian philosophers of the first half of the twentieth century. We demonstrate the interrelationship between three aspects of Lossky’s philosophy: realism in the theory of knowledge, hierarchical personalism, and supra-naturalistic concept of evolution. We pay attention to the contemporary relevance of Lossky, and we discuss and critique his ideas in light of those of other philosophers. Lossky acknowledges that the subject interacts with being itself and that knowledge (...) is not a shadow or copy of being. His ontology is based on intuitivism and epistemological optimism. Its main categories are God, as the super-systematic principle, and monads. In Lossky’s metaphysics, the idea of abstract ideal principles of being is quite harmoniously combined with personalism. Lossky’s ontology has strong ethical underpinnings. He considers that values are rooted in being and that the evolution of the world is directed by God towards the good. Lossky elaborates a supra-naturalistic concept of evolution. He extrapolates principles of teleological determination, which are fundamental to human existence, on the underlying levels of the world as a whole. Many trends of modern thought are in tune with Lossky’s philosophy, including a return to metaphysics, panpsychism, Platonism, and the desire to unite the religious worldview with new scientific discoveries. (shrink)

This monograph presents a general theory of weakly implicative logics, a family covering a vast number of non-classical logics studied in the literature, concentrating mainly on the abstract study of the relationship between logics and their algebraic semantics. It can also serve as an introduction to algebraic logic, both propositional and first-order, with special attention paid to the role of implication, lattice and residuated connectives, and generalized disjunctions. Based on their recent work, the authors develop a powerful uniform framework for (...) the study of non-classical logics. In a self-contained and didactic style, starting from very elementary notions, they build a general theory with a substantial number of abstract results. The theory is then applied to obtain numerous results for prominent families of logics and their algebraic counterparts, in particular for superintuitionistic, modal, substructural, fuzzy, and relevant logics. The book may be of interest to a wide audience, especially students and scholars in the fields of mathematics, philosophy, computer science, or related areas, looking for an introduction to a general theory of non-classical logics and their algebraic semantics. (shrink)

What makes safety claims about general purpose AI systems such as large language models trustworthy? We show that rather than the capabilities of security tools such as alignment and red teaming procedures, it is security practices based on these tools that contributed to reconfiguring the image of AI safety and made the claims acceptable. After showing what causes the gap between the capabilities of security tools and the desired safety guarantees, we critically investigate how AI security practices attempt to fill (...) the gap and identify several shortcomings in diversity and participation. We found that these security practices are part of securitization processes aiming to support (commercial) development of general purpose AI systems whose trustworthiness can only be imperfectly tested instead of guaranteed. We conclude by offering several improvements to the current AI security practices. (shrink)

On the Notion "Type of Language" Petr Sgall It is well known that the high frequency of terminological vagueness and confusion has been a serious obstacle ...

Despite an increasing role of machine learning in science, there is a lack of results on limits of empirical exploration aided by machine learning. In this paper, we construct one such limit by proving undecidability of learnability of state spaces of physical systems. We characterize state spaces as binary hypothesis classes of the computable Probably Approximately Correct learning framework. This leads to identifying the first limit for learnability of state spaces in the agnostic setting. Further, using the fact that finiteness (...) of the combinatorial dimension of hypothesis classes is undecidable, we derive undecidability for learnability of state spaces as well. Throughout the paper, we try to connect our formal results with modern neural networks. This allows us to bring the limits close to the current practice and make a first step in connecting scientific exploration aided by machine learning with results from learning theory. (shrink)

The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. By enabling rapid, (...) low-risk experimentation with alternative institutional designs, this approach could significantly accelerate democratic innovation. I examine the potential of GenAI-assisted research to mitigate current limitations in democratic experimentation, including the ability to simulate large-scale societal interactions and test complex institutional mechanisms. While acknowledging potential risks such as algorithmic bias, reproducibility challenges, and AI alignment issues, I argue that the benefits of synthetic data are likely to outweigh their drawbacks if implemented with proper caution. To address existing challenges, I propose a range of technical, methodological, and institutional adaptations. The paper concludes with a call for interdisciplinary collaboration in the development and implementation of GenAI-assisted methods in democracy research, highlighting their potential to bridge the gap between democratic theory and practice in an era of rapid technological change. (shrink)

[The Molinist theory of the relation of divine prescience and human freedom in late Scholasticism and current analytic philosophy].

Práce se zabývá metodologickou stránkou teorie lidského jednání, která vznikla v rámci Weberovy rozumějící sociologie a byla dále transformována ve fenomenologické sociologii Alfreda Schütze. Analýza se zaměřuje především na to, jakým způsobem se oba myslitelé pokusili vyřešit problém interpretace lidského jednání ze strany (vědeckého) pozorovatele a samotného aktéra. Ukazuje se, že Weber stojí i přes snahu zůstat v kontaktu s psychologickou realitou jednání vždy již na pozici vědeckého pozorovatele. Schütz usiluje o důkladnější založení weberiánského postoje a ukazuje, jakým způsobem stanovisko (...) nezaujatého pozorovatele vystupuje z bezprostředně ("naivně") prožívané reality přirozeného světa. Oba myslitelé diskutují otázku racionality jednání a v závěru dospívají k přesvědčení, že racionalita (chápaná jako logická konzistence a empirická efektivita jednání) není ani tak charakteristikou, která by připadala "reálnému" jednání, ale spíše normou vědeckého popisu a vědeckého postupu. Argumentace je rozvinuta v rámci úvahy o chápání racionality v současné ekonomické teorii a roli introspekce. (shrink)

Could the employment of large language models (LLMs) in place of human advisors improve the problem-solving ability of democratic assemblies? LLMs represent the most significant recent incarnation of artificial intelligence and could change the future of democratic governance. This paper assesses their potential to serve as expert advisors to democratic representatives. While LLMs promise enhanced expertise availability and accessibility, they also present specific challenges. These include hallucinations, misalignment and value imposition. After weighing LLMs’ benefits and drawbacks against human advisors, I (...) argue that time-tested democratic procedures, such as deliberation and aggregation by voting, provide safeguards that are effective against human and machine advisor shortcomings alike. Additional protective measures may include custom training for advisor LLMs or boosting representatives’ competencies in query formulation. Implementation of adversarial proceedings in which LLM advisors would debate each other and provide dissenting opinions is likely to yield further epistemic benefits. Overall, promising interventions that would mitigate the LLM risks appear feasible. Machine advisors could thus empower human decision-makers to make more autonomous, higher-quality decisions. On this basis, I defend the hypothesis that LLMs’ careful integration into policymaking could augment democracy’s ability to address today’s complex social problems. (shrink)

Drawing on current debates at the frontiers of economics, psychology, and political philosophy, this book explores the challenges that arise for liberal democracies from a confrontation between modern technologies and the bounds of human rationality. With the ongoing transition of democracy's underlying information economy into the digital space, threats of disinformation and runaway political polarization have been gaining prominence. Employing the economic approach informed by behavioral sciences' findings, the book's chief concern is how these challenges can be addressed while preserving (...) a commitment to democratic values and maximizing the epistemic benefits of democratic decision-making. The book has two key strands: it provides a systematic argument for building a behaviorally informed theory of democracy; and explores how scientific knowledge on quirks and bounds of human rationality can inform the design of resilient democratic institutions. Drawing these together, the book explores the centrality of the rationality assumption in the methodological debates surrounding behavioral sciences as exemplified by the dispute between neoclassical and behavioral economics; the role of rationality in democratic social choice; behaviorally informed paternalism as a response to the challenge of irrationality; and non-paternalistic avenues to increase the resilience of the democratic institutions towards political irrationality This book is invaluable reading for anyone interested in behavioral economics and sciences, political philosophy and the future of democracy. (shrink)

This paper addresses the ontological status of the body in the context of bodily practices in body-oriented disciplines, such as sport training, dance, and physiotherapy. Following Descartes’, Huss...

The present paper shows how statistical learning theory and machine learning models can be used to enhance understanding of AI-related epistemological issues regarding inductive reasoning and reliability of generalisations. Towards this aim, the paper proceeds as follows. First, it expounds Price’s dual image of representation in terms of the notions of e-representations and i-representations that constitute subject naturalism. For Price, this is not a strictly anti-representationalist position but rather a dualist one (e- and i-representations). Second, the paper links this debate (...) with machine learning in terms of statistical learning theory becoming more viable epistemological tool when it abandons the perspective of object naturalism. The paper then argues that machine learning grounds a form of knowing that can be understood in terms of e- and i-representation learning. Third, this synthesis shows a way of analysing inductive reasoning in terms of reliability of generalisations stemming from a structure of e- and i-representations. In the age of Artificial Intelligence, connecting Price’s dual view of representation with Deep Learning provides an epistemological way forward and even perhaps an approach to how knowing is possible. (shrink)

Shows how different forms of skepticism can lead to remarkably different moral and political implications.

Despite their flaws, large language models (LLMs) deserve a fair chance to prove their mettle against human experts, who are often plagued with biases, conflicts of interest, and other frailties. For epistemically unprivileged laypeople struggling to access expert knowledge, the accessibility advantages of LLMs could prove crucial. While complaints about LLMs' inconsistencies and arguments for human superiority are often justified (for now), they distract from the urgent need to prepare for the likely scenario of LLMs' continued ascent. Experimentation with both (...) the capabilities and institutional architecture of LLMs is necessary. Neither tech-bashing nor excessive gatekeeping will suffice. As LLMs are here to stay and they keep improving, it is high time we started thinking about how to navigate the impending wave of their proliferation. (shrink)

This article reviews recent findings indicating some common brain processes during dissociative states and dreaming with the aim to outline a perspective that neural chaotic states during dreaming can be closely related to dissociative states that may manifest in dreams scenery. These data are in agreement with various clinical findings that dissociated states can be projected into the “dream scenery” in REM sleep periods and dreams may represent their specific interactions that may uncover unusual psychological potential of creativity in psychotherapy, (...) art and scientific discoveries. (shrink)

Current thinking suggests that dissociation could be a significant comorbid diagnosis in a proportion of schizophrenic patients with a history of trauma. This potentially may explain the term “schizophrenia” in its original definition by Bleuler, as influenced by his clinical experience and personal view. Additionally, recent findings suggest a partial overlap between dissociative symptoms and the positive symptoms of schizophrenia, which could be explained by inhibitory deficits. In this context, the process of dissociation could serve as an important conceptual framework (...) for understanding schizophrenia, which is supported by current neuroimaging studies and research of corollary discharges. These data indicate that the original conception of “split mind” may be relevant in an updated context. Finally, recent data suggest that the phenomenal aspects of dissociation and conscious disintegration could be related to underlying disruptions of connectivity patterns and neural integration. (shrink)

Dave Ripley has recently argued against the plausibility of multiset consequence relations and of contraction-free approaches to paradox. For Ripley, who endorses a nontransitive theory, the best arguments that buttress transitivity also push for contraction—whence it is wiser for the substructural logician to go nontransitive from the start. One of Ripley’s allegations is especially insidious, since it assumes the form of a trivialisation result: it is shown that if a multiset consequence relation can be associated to a closure operator in (...) the expected way, then it necessarily contracts. We counter Ripley’s objection by presenting an approach to multiset consequence that escapes this trap. This approach is multiple-conclusioned in a heterodox way, for multiple succedents are given a conjunctive, rather than a disjunctive reading. Finally, we address a further objection by French and Ripley to the effect that the informational interpretation of sequents in linear logic does not motivate cut. (shrink)

Later works of C.G. Jung contain comprehensive descriptions of the relationship between psychological and physical research. These considerations described in Jung’s works and in his correspondence with Wolfgang Pauli represent interesting philosophical ideas that are related to interpretation of psychological data. The so-called “collective unconscious” studied by Jung in analysis of dream material, mythology, psychopathological symptoms, and several cultural manifestations led him to postulate complementarity and unity of scientific principles, and to define the psyche as complementary to physical reality. Likewise (...) recent neuroscientific studies and physical analyses on the role of the observer in physical reality led to the study of “quantum consciousness.” This review compares the philosophical postulates by Roger Penrose with Jung’s and Pauli’s studies, and suggests novel links of these concepts to recent findings of chaos theory in the brain. (shrink)

The Czech philosopher Jan Patocka, who studied with Husserl and Heidegger, is widely recognized as the most influential thinker to come from postwar Eastern Europe. Refusing to join the Communist party after World War II, he was banned from academia and publication for the rest of his life, except for a brief time following the liberalizations of the Prague spring of 1968. Joining Vaclav Havel and Jiri Hajek as a spokesman for the Chart 77 human-rights declaration of 1977, Patocka was (...) harassed by authorities, arrested, and finally died of a heart attack during prolonged interrogation. _Plato and Europe_, arguably Patocka's most important book, consists of a series of lectures delivered in the homes of friends after his last banishment from the academy just three years before his death. Here, he presents his most mature ideas about the history of Western philosophy, arguing that the idea of the care of the soul is fundamental to the philosophical tradition beginning with the Greeks. Explaining how the care of the soul is elaborated as the problem of how human beings may make their world one of truth and justice, Patocka develops this thesis through a treatment of Plato, Democritus, and Aristotle, showing how considerations about the soul are of central importance in their writings. He demonstrates in vivid fashion how this idea forms the spiritual heritage of Europe. (shrink)

L'auteur a essayé de démontrer le positionnement de Jean-Paul Sartre sous la bannière de l'ontologie dès sa création de jeunesse, sans laquelle il est difficile de comprendre la place du philosophe français dans l'histoire de la phénoménologie. La nouvelle formule créée par Jean-Paul Sartre, à savoir "l'ontologie phénoménologique" est une forme spécifique de l'ontologie moderne de l'humain comprenant également des questions fondamentales caractéristiques de n'importe quelle pensée philosophique. u u.

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet (...) the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The paper asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach ‘elsewheres’ in space and time or deploy ML models in non-benign environments. The paper argues that the only viable version of the contract can be based on optimality (instead of on reliability which cannot be justified without circularity) and aligns this position with Schurz’s optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (‘elsewheres’ and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full. (shrink)

The starting point of this work is the gap between two distinct traditions in information engineering: knowledge representation and data - driven modelling. The first tradition emphasizes logic as a tool for representing beliefs held by an agent. The second tradition claims that the main source of knowledge is made of observed data, and generally does not use logic as a modelling tool. However, the emergence of fuzzy logic has blurred the boundaries between these two traditions by putting forward fuzzy (...) rules as a Janus-faced tool that may represent knowledge, as well as approximate non-linear functions representing data. This paper lays bare logical foundations of data - driven reasoning whereby a set of formulas is understood as a set of observed facts rather than a set of beliefs. Several representation frameworks are considered from this point of view: classical logic, possibility theory, belief functions, epistemic logic, fuzzy rule-based systems. Mamdani's fuzzy rules are recovered as belonging to the data - driven view. In possibility theory a third set-function, different from possibility and necessity plays a key role in the data - driven view, and corresponds to a particular modality in epistemic logic. A bi-modal logic system is presented which handles both beliefs and observations, and for which a completeness theorem is given. Lastly, our results may shed new light in deontic logic and allow for a distinction between explicit and implicit permission that standard deontic modal logics do not often emphasize. (shrink)

Many democratic countries have failed to stand up to the challenge presented by the COVID-19 pandemic. I argue that the collective response to the pandemic has been incapacitated by an ‘epistemic crisis’, (i.e., a breakdown in the social division of epistemic labor) that led to a failure of citizens’ beliefs to converge towards a shared perception of the situation. Neither a paucity of relevant expert knowledge nor democratic citizens’ irrationality is required for the crisis to emerge. In particular, I highlight (...) three obstacles capable of preventing relevant expert testimony from gaining democratic legitimacy necessary for an effective policy response: 1) the proper domain of expertise is uncertain; 2) regular citizens’ meta-expertise – (i.e., ability to discriminate among the putative experts based on their relative epistemic merit) – is not efficacious enough to trigger truth-convergence of the public opinion; 3) the prominent sources of third-party meta-expertise lack credibility due to their conflicts of interest. Since the first two problems appear irresolvable, I propose that the search for a path towards preclusion of future epistemic crises concentrates on upgrading the institutional fundament for meta-expertise provision. Social epistemology plays a vital role in this search, but its close cooperation with other disciplines is a must. (shrink)