搜索结果：Philosophical studies

We applied computational methods to analyze references across 2,245 philosophical texts, spanning from approximately 550 BCE to 1940 AD, in order to measure patterns in how philosophical ideas have spread over time. Using natural language processing and network analysis, we mapped over 294,970 references between authors, classifying each reference into subdisciplines of philosophy based on its surrounding context. We then constructed a graph, with authors as nodes and textual references as edges, to empirically validate, visualize, and quantify intellectual lineages as they are understood within philosophical scholarship. For instance, we find that Plato and Aristotle alone account for nearly 10% of all references from authors in our dataset, suggesting that their influence may still be underestimated. As another example, we support the view that St. Thomas Aquinas served as a synthesizer between Aristotelian and Christian philosophy by analyzing the network structures of Aquinas, Aristotle, and Christian theologians. Our results are presented through an interactive visualization tool, allowing users to dynamically explore these networks, alongside a mathematical analysis of the ne

The philosophical foundations of statistics involve issues in theoretical statistics, such as goals and methods to meet these goals, and interpretation of the meaning of inference using statistics. They are related to the philosophy of science and to the philosophy of probability. We review the core and partly interrelated themes and place them in context.

We explore the issue of providing a foundational framework for Leibnizian infinitesimals in the light of modern standard and nonstandard approaches. We outline a trichotomy of ordinals, cardinals and ringinals as a historiographic tool. A ringinal is a concept of infinite number, arithmetic in nature, different from Cantor's transfinite ordinals and cardinals. The continuum is not necessarily identifiable with R; even if one seeks such an identification, infinitesimals are not ruled out. Analysis with unlimited numbers (via the predicate standard) is possible in a conservative extension of Zermelo-Fraenkel set theory and in this sense is epistemologically 'safe'. We sketch a recent theory of infinitesimal analysis that formalizes Leibnizian definitions and heuristic principles while eschewing both the axiom of choice and ultrafilters, thus challenging received philosophical views on the nature of infinitesimals.

This paper examines the intellectual legacy of Philip E. Agre by situating his work at the intersection of artificial intelligence, philosophy, and critical theory. It reconstructs Agre's proposal of a critical technical practice, according to which AI should be understood not merely as an engineering discipline but as a form of mathematized philosophy shaped by historically contingent metaphors, assumptions, and discourses. Drawing on Heideggerian phenomenology, especially the distinction between ready-to-hand and present-at-hand, Agre sought to reform AI by emphasizing interaction, embedding, indexicality, and deictic representation over traditional mentalist and representational models. The paper analyzes Agre's attempt to operationalize these ideas through computational implementations such as the Pengi system, highlighting both the philosophical ambition and the technical limitations of programming phenomenological concepts. While acknowledging Agre's success in exposing the hidden philosophical commitments of AI and enriching its conceptual vocabulary, the paper ultimately argues that his project encounters a fundamental impasse: the open and self-disclosing character of huma

This book is a philosopher's introduction to the idea that our universe is just one of many universes. I present and assess three versions of the idea: one version from philosophy, and two from physics. In short, they are: all the logically possible worlds; all the branches of the quantum state, in an Everettian interpretation of quantum theory; and all the bubbles of inflationary cosmology. For each proposal, I choose one main philosophical question to discuss in depth. They are, respectively: what is a possible world; what is chance; and what is explanation. But before treating these proposals and their associated questions, I set the stage by reviewing physics and philosophy from about 1600 to about 1900; and a final Chapter compares and contrasts the proposals.

With the rise of generative AI (GenAI), Large Language Models are increasingly employed for code generation, becoming active co-authors alongside human programmers. Focusing specifically on this application domain, this paper articulates distinct ``Architectures of Error'' to ground an epistemic distinction between human and machine code generation. Examined through their shared vulnerability to error, this distinction reveals fundamentally different causal origins: human-cognitive versus artificial-stochastic. To develop this framework and substantiate the distinction, the analysis draws critically upon Dennett's mechanistic functionalism and Rescher's methodological pragmatism. I argue that a systematic differentiation of these error profiles raises critical philosophical questions concerning semantic coherence, security robustness, epistemic limits, and control mechanisms in human-AI collaborative software development. The paper also utilizes Floridi's levels of abstraction to provide a nuanced understanding of how these error dimensions interact and may evolve with technological advancements. This analysis aims to offer philosophers a structured framework for understanding GenA

Can humans and artificial intelligences share concepts and communicate? 'Making AI Intelligible' shows that philosophical work on the metaphysics of meaning can help answer these questions. Herman Cappelen and Josh Dever use the externalist tradition in philosophy to create models of how AIs and humans can understand each other. In doing so, they illustrate ways in which that philosophical tradition can be improved. The questions addressed in the book are not only theoretically interesting, but the answers have pressing practical implications. Many important decisions about human life are now influenced by AI. In giving that power to AI, we presuppose that AIs can track features of the world that we care about (for example, creditworthiness, recidivism, cancer, and combatants). If AIs can share our concepts, that will go some way towards justifying this reliance on AI. This ground-breaking study offers insight into how to take some first steps towards achieving Interpretable AI.

Silicon samples are increasingly used as a low-cost substitute for human panels and have been shown to reproduce aggregate human opinion with high fidelity. We show that, in the alignment-relevant domain of philosophy, silicon samples systematically collapse heterogeneity. Using data from $N = {277}$ professional philosophers drawn from PhilPeople profiles, we evaluate seven proprietary and open-source large language models on their ability to replicate individual philosophical positions and to preserve cross-question correlation structures across philosophical domains. We find that language models substantially over-correlate philosophical judgments, producing artificial consensus across domains. This collapse is associated in part with specialist effects, whereby models implicitly assume that domain specialists hold highly similar philosophical views. We assess the robustness of these findings by studying the impact of DPO fine-tuning and by validating results against the full PhilPapers 2020 Survey ($N = {1785}$). We conclude by discussing implications for alignment, evaluation, and the use of silicon samples as substitutes for human judgment. The code of this project can be fou

In the present paper, we outline and expound the fundamental and novel qualitative-cum-philosophical premises, principles, ideas, concepts, constructions and results that originate from our ongoing research project of applying the conceptual panoply and the technical machinery of Abstract Differential Geometry (ADG) to various persistently outstanding issues in Quantum Gravity (QG) research. At the end of the paper, we discuss the potential philosophical repercussions of two possible future research routes that the main stream of our applications of ADG to QG may bifurcate towards in view of three independent, but overlapping, research papers that are currently under development.

This paper leverages various philosophical and ontological frameworks to explore the concept of embodied artificial general intelligence (AGI), its relationship to human consciousness, and the key role of the metaverse in facilitating this relationship. Several theoretical frameworks underpin this exploration, such as embodied cognition, Michael Levin's computational boundary of a "Self," and Donald D. Hoffman's Interface Theory of Perception, which lead to considering human perceived outer reality as a symbolic representation of alternate inner states of being, and where AGI could embody a different form of consciousness with a larger computational boundary. The paper further discusses the necessary architecture for the emergence of an embodied AGI, how to calibrate an AGI's symbolic interface, and the key role played by the Metaverse, decentralized systems and open-source blockchain technology. The paper concludes by emphasizing the importance of achieving a certain degree of harmony in human relations and recognizing the interconnectedness of humanity at a global level, as key prerequisites for the emergence of a stable embodied AGI.

Empirical studies form an integral part of visualization research. Not only can they facilitate the evaluation of various designs, techniques, systems, and practices in visualization, but they can also enable the discovery of the causalities explaining why and how visualization works. This state-of-the-art report focuses on controlled and semi-controlled empirical studies conducted in laboratories and crowd-sourcing environments. In particular, the survey provides a taxonomic analysis of over 129 empirical studies in the visualization literature. It juxtaposes these studies with topic developments between 1978 and 2017 in psychology, where controlled empirical studies have played a predominant role in research. To help appreciate this broad context, the paper provides two case studies in detail, where specific visualization-related topics were examined in the discipline of psychology as well as the field of visualization. Following a brief discussion on some latest developments in psychology, it outlines challenges and opportunities in making new discoveries about visualization through empirical studies.

Large language models like GPT-4 have achieved remarkable proficiency in a broad spectrum of language-based tasks, some of which are traditionally associated with hallmarks of human intelligence. This has prompted ongoing disagreements about the extent to which we can meaningfully ascribe any kind of linguistic or cognitive competence to language models. Such questions have deep philosophical roots, echoing longstanding debates about the status of artificial neural networks as cognitive models. This article -- the first part of two companion papers -- serves both as a primer on language models for philosophers, and as an opinionated survey of their significance in relation to classic debates in the philosophy cognitive science, artificial intelligence, and linguistics. We cover topics such as compositionality, language acquisition, semantic competence, grounding, world models, and the transmission of cultural knowledge. We argue that the success of language models challenges several long-held assumptions about artificial neural networks. However, we also highlight the need for further empirical investigation to better understand their internal mechanisms. This sets the stage for th

Ethnomethodological fieldwork has long been acknowledged as a potentially valuable way of informing the design of technology. However, there is relatively little methodological support for this activity, particularly in relation to the systematic approaches to development advocated in mainstream software and requirements engineering. This thesis focuses on the use of ethnomethodological fieldwork for the engineering of software requirements. Firstly, it proposes an approach, dubbed "Model Guided Fieldwork," to support a fieldworker in making observations that may contribute to a technological development process. It does this by supplementing the normal debriefing sessions that a fieldworker and a technologist might have, with a lightweight iterative system modelling exercise, in such a way that the fieldwork and modelling can be mutually guiding. Secondly, the thesis presents an application of this approach in a high-profile e-Science project. This case study provides an opportunity to examine the relationship between ethnomethodological ethnography and requirements engineering empirically. Thirdly, the thesis addresses a number of theoretical and philosophical concerns relating t

In this paper, I aim to articulate and investigate the philosophical implications and inherent symbolism surrounding the mathematical properties of Ouroboros spaces and their respective functions. Initially, I provide a brief historical background explaining how the symbol of the Ouroboros has been used and how it continues to be used as a term in mathematics. I then describe the philosophical symbolism and symbolic significance of the mathematical properties of the Ouroboros spaces and their functions, while offering an explanation as to why these concepts feel philosophically natural and intuitive. Following this discussion, I prove an aesthetically significant theorem that showcases the philosophical significance of the real Ouroboros functions. In closing, I articulate the interrelated, philosophical nature of these mathematical concepts, and describe how they impact other scientific fields both practically and philosophically.

Some physicists believe that superselection rules should be implemented to get rid of inconsistencies when a theory is framed in terms of a new mathematical formulation, whilst others think that this new formulation should be modified instead of implementing those rules, at the expense of introducing additional mathematical structure. The outcome, however, is that we are still uncertain whether these rules should be implemented and how they should be interpreted and assessed from the philosophical point of view. Based on a detailed examination of the group-theoretic reformulation of (relativistic and non-relativistic) quantum mechanics that prompts physicists to impose superselection rules, I shall argue that the implementation of these rules involves serious heuristic and epistemological concerns. Considering this argument, I shall conclude that there are suitable philosophical reasons to claim that the implementation of superselection rules should be rejected and that there are certain circumstances when the formulation of a theory should be modified.

This paper establishes a connection between the fields of machine learning (ML) and philosophy concerning the phenomenon of behaving neutrally. It investigates a specific class of ML systems capable of delivering a neutral response to a given task, referred to as abstaining machine learning systems, that has not yet been studied from a philosophical perspective. The paper introduces and explains various abstaining machine learning systems, and categorizes them into distinct types. An examination is conducted on how abstention in the different machine learning system types aligns with the epistemological counterpart of suspended judgment, addressing both the nature of suspension and its normative profile. Additionally, a philosophical analysis is suggested on the autonomy and explainability of the abstaining response. It is argued, specifically, that one of the distinguished types of abstaining systems is preferable as it aligns more closely with our criteria for suspended judgment. Moreover, it is better equipped to autonomously generate abstaining outputs and offer explanations for abstaining outputs when compared to the other type.

We explore the influence and interconnectivity of philosophical thinkers within the Wikipedia knowledge network. Using a dataset of 237 articles dedicated to philosophers across nine different language editions (Arabic, Chinese, English, French, German, Japanese, Portuguese, Russian, and Spanish), we apply the PageRank and CheiRank algorithms to analyze their relative ranking and influence in each linguistic context. Furthermore, we compare our results with entries from the Stanford Encyclopedia of Philosophy and the Internet Encyclopedia of Philosophy, providing insight into the differences between general knowledge networks like Wikipedia and specialized philosophical databases. A key focus of our analysis is the sub-network of 21 presocratic philosophers, grouped into four traditional schools: Italic (Pythagorean + Eleatic), Ionian, Abderian (Atomist), and Sophist. Using the reduced Google matrix method, we uncover both direct and hidden links between these early thinkers, offering new perspectives on their intellectual relationships and influence within the Western philosophical tradition.

Since I first became enthralled with physics as a teenager, I've been intrigued by the philosophical aspects of the discipline. As I approached the end of my career as an experimental physicist and observational astronomer (I'm now retired), I decided to return to these philosophical matters, some of which were still perturbing me, to see if I could finally make enough sense of them to quiet my discomfort. I've more or less succeeded in this quest in large part, I believe, because of my experimentalist background and a concomitant proclivity for pragmatic explanation. My purpose in this essay is to sketch a pragmatic worldview with which one might be able to approach fundamental philosophical and interpretational problems. You might ask how I can possibly expect to say anything meaningful in the less than 50 pages of the present essay? On the other hand, it might be an advantage to avoid the depth and precision that would limit flexibility in dealing with the philosophical conundrums I seek to resolve. In any case, I here offer my thoughts on the philosophical foundations of physics.

Inspired by the Turing test, we present a novel methodological framework to assess the extent to which a population of machines mirrors the philosophical views of a population of humans. The framework consists of three steps: (i) instructing machines to impersonate each human in the population, reflecting their backgrounds and beliefs, (ii) administering a questionnaire covering various philosophical positions to both humans and machines, and (iii) statistically analyzing the resulting responses. We apply this methodology to the debate on scientific realism, a long-standing philosophical inquiry exploring the relationship between science and reality. By considering the outcome of a survey of over 500 human participants, including both physicists and philosophers of science, we generate their machine personas using an artificial intelligence engine based on a large language model. We reveal that the philosophical views of a population of machines are, on average, similar to those endorsed by a population of humans, irrespective of whether they are physicists or philosophers of science. As compared to humans, however, machines exhibit a weaker inclination toward scientific realism an