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The Argument from Addition for No Best World
No full textThis chapter will amount to a detailed exposition and exploration of one of the most prominent arguments against the existence of an unsurpassable world: the argument from addition. Endorsed by a variety of thinkers such as St. Thomas Aquinas, Alvin Plantinga, and William Rowe, the argument from addition uses the possibility of adding good things to a candidate unsurpassable world to argue that every world is surpassable. While widely endorsed, the argument has come under recent criticism. By carefully working through a targeted version of the argument, I set out to establish the following: (i) that a world can always contain more good things; (ii) that a suitably restricted additive aggregation principle allows us to say that adding more good things in a certain way is an improvement, and (iii) that objections to the argument from widespread value incomparability fail
Towards Shutdownable Agents via Stochastic Choice
No full textThe POST-Agents Proposal (PAP) is an idea for ensuring that advanced artificial agents never resist shutdown. A key part of the PAP is using a novel ‘Discounted Reward for Same-Length Trajectories (DReST)’ reward function to train agents to (1) pursue goals effectively conditional on each trajectory-length (be ‘USEFUL’), and (2) choose stochastically between different trajectorylengths (be ‘NEUTRAL’ about trajectory-lengths). In this paper, we propose evaluation metrics for USEFULNESS and NEUTRALITY. We use a DReST reward function to train simple agents to navigate gridworlds, and we find that these agents learn to be USEFUL and NEUTRAL. Our results thus provide some initial evidence that DReST reward functions could train advanced agents to be USEFUL and NEUTRAL. Our theoretical work suggests that these agents would be useful and shutdownable
Eternity Against Reality; God, the Entropic Epitome of Symbolic Lock-In
No full textAcross human history, the symbol of God has occupied a position unlike any other in the architecture of meaning. Political orders rise and fall, languages mutate, scientific paradigms overturn, yet the symbol of God persists across these changes with a peculiar immunity to revision. This persistence is not simply a function of devotional feeling or metaphysical speculation. It emerges from the structural properties of “God” as a symbolic form. A form that can be understood, without making any theological commitments, as the most powerful symbolic lock-in attractor in the repertoire of human culture. To grasp this is to see why God has been both the generative source of profound civilizations and the central mechanism by which human systems close themselves against the adaptive flow of reality
Would You Still Love Me if I Were a Worm? Robust Relationships and Why Beliefs cannot be the Grounds for Normative Blackballing
No full textNormative blackballing in the context of a friendship refers to a practise where one party actively tells the other that they no longer wish to be friends in virtue of that friend holding abhorrent beliefs. This paper argues that normative blackballing is not morally required in cases where one party develops a morally bad belief if we hold the view that friendships are robust attachments. If this is true, then at least two of the major positions within the current debate on the specific wrongness of being friends with bad people are wrong to argue that the termination of a friendship can be morally required of us. I will draw on Brennan’s work(2023) and take the argument as given that no objective sufficiency criteria can be offered to justify normative blackballing. I will then show how subjective accounts also fail to justify normative blackballing by showing that there is no mechanism to appropriately weigh trivial, but passionately held, beliefs. If any belief an agent themself considers sufficiently bad justifies normative blackballing, then this undermines friendship’s robust nature. We either have to accept the subjective criteria and reject robustness, or accept robustness and deny that beliefs are sufficient for normative blackballing
Fundamental choices in Epistemic Foundational Ontology
No full textIn this article, we continue our study and definition of a class of ontologies known as “epistemic” by laying the foundations for a foundational ontology called the “Epistemic Foundational Ontology” (EFO). We begin by setting out some basic ontological commitments made to establish EFO and then give details of the main categories that structure the ontology. Lastly, to formalize the core of EFO, we set out a set of axioms in first-order logic
Ibn Rushd (Averroes, d. 1198): Reason and Unreason in Prophecy
No full textThis paper explores Ibn Rushd’s (Averroes, d. 595/1198) distinctive stance on miracles as they pertain to Islamic prophetic theory, situating his arguments within the broader intellectual and theological climate of his era. Beginning with Hugo Grotius’s early modern critique contrasting Christian and Islamic miracles, the study shows how Ibn Rushd’s own views challenge the dominant Sunni Ashʿarite position, which considered miracles unequivocal proof of prophecy. After surveying the Ashʿarite theologians – most notably Abū Bakr al-Bāqillānī, al-Juwaynī, and al-Ghazālī – who vigorously defended miracles as the decisive validation of a prophet’s claim, the paper turns to Ibn Rushd’s critique. While he does not deny that miracles happen, Ibn Rushd questions their logical power to establish prophecy, underscoring the absence of a rational, necessary link between the supernatural event and a prophet’s truthfulness. Instead, he privileges the Qur’an as Islam’s singular miracle capable of providing lasting, rational credibility. The paper also highlights how Ibn Rushd draws on, yet critically reinterprets, segments of al-Ghazālī’s later works – particularly al-Qisṭās al-mustaqīm and al-Munqidh min al-ḍalāl – to shore up his argument. Though al-Ghazālī remained committed to a broader Ashʿarite framework, both he and Ibn Rushd share the view that extraordinary feats do not, by themselves, confer certain knowledge of prophecy. Ultimately, the article argues that by relegating miracles to a chiefly rhetorical function and centering ‘corresponding’ proofs such as lawgiving and unique moral insight, Ibn Rushd separates prophecy from extravagant supernatural claims. His approach thus preserves both causality and the rational integrity of religious belief, while still acknowledging the formative role that miracles, especially the Qur’an, play in the faith of ordinary believers
Machine Learning Solutions for Cyberbullying Detection and Prevention on Social Media
No full textThis work explores the potential of big data analytics, natural language processing (NLP), and machine
learning (ML) techniques in predicting cyberbullying on social media. By analyzing large-scale datasets consisting of
user comments, posts, and interactions, the study aims to detect harmful content patterns, abusive language, and
behavioral trends that indicate cyberbullyingThe rapid proliferation of social media has transformed communication
and interaction, but it has also led to an alarming rise in cyberbullying incidents. Cyberbullying, characterized by
repeated and intentional harassment through digital platforms, has significant psychological and social consequences
for victims, often leading to anxiety, depression, and even self-harm. Traditional methods of identifying and
mitigating cyberbullying are often reactive and inefficient due to the vast volume of data generated across multiple
platforms. Therefore, predictive models leveraging big data and artificial intelligence (AI) offer a proactive approach to
detecting and preventing online harassment. Sentiment analysis, text classification, and deep learning models such as
convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to enhance the accuracy of
cyberbullying predictions. Additionally, graph-based techniques are utilized to examine social network structures and
identify potential perpetrators and victims based on interaction patterns. Despite the promising potential of big datadriven approaches, challenges such as data privacy, ethical concerns, and algorithmic bias must be addressed to ensure
fair and responsible implementation. The study also highlights the importance of real-time monitoring systems that can
alert platform administrators or authorities when cyberbullying behavior is detected. The findings demonstrate that
integrating AI with big data analytics significantly improves the accuracy and efficiency of cyberbullying detection,
enabling early intervention and fostering a safer digital environment. Future research will focus on refining detection
models to handle multilingual data, cultural nuances, and evolving forms of cyberbullying across
diverse social media platforms