The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the (...) standard interpretation assumes that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use, so that these two senses of using information are not conflated, but instead the question of how they are related to one another can be studied directly. I apply the generative methodology I have developed elsewhere for cognitive science and AI research (Miracchi, 2017, 2019a) to show how the Frame Problem can be appropriately updated. (shrink)

If updating with E has the same result across all epistemically possible worlds, then the agent has no uncertainty as to the behavior of the update, and we may call it a transparent update. If an agent is uncertain about the behavior of an update, we may call it opaque. In order to model the uncertainty an agent has about the result of an update, the same update must behave differently across different possible worlds. In (...) this paper, I study opaque updates using a simple system of dynamic epistemic logic suitably modified for that purpose. The paper highlights the connection between opaque updates and the dynamic-epistemic principles Perfect-Recall and No-Miracles. I argue that opaque updates are central to contemporary discussions in epistemology, in particular to externalist theories of knowledge and to the related problem of epistemic bootstrapping, or easy knowledge. Opaque updates allow us to explicitly investigate a dynamic form of uncertainty, using simple and precise logical tools. (shrink)

In this paper we explore the absentminded driver problem using two different scenarios. In the first scenario we assume that the driver is capable of reasoning about his degree of absentmindedness before he hits the highway. This leads to a Savage-style model where the states are mutually exclusive and the act-state independence is in place. In the second we employ centred possibilities, by modelling the states (i.e. the events about which the driver is uncertain) as the possible final destinations (...) indexed by a time period. The optimal probability we find for continuing at an exit is different from almost all papers in the literature. In this scenario, act-state independence is still violated, but states are mutually exclusive and the driver arrives at his optimal choice probability via Bayesian updating. We show that our solution is the only one guaranteeing immunity from sure loss via a Dutch strategy, and that – despite initial appearances – it is time consistent. This raises important implications for the rationality of commitment in such scenarios. (shrink)

The problem of probabilities of conditionals is one of the long-standing puzzles in philosophy of language. We defend and update Adams' solution to the puzzle: the probability of an epistemic conditional is not the probability of a proposition, but a probability under a supposition. Close inspection of how a triviality result unfolds in a concrete scenario does not provide counterexamples to the view that probabilities of conditionals are conditional probabilities: instead, it supports the conclusion that probabilities of conditionals (...) violate standard probability theory. This does not call into question probability theory per se; rather, it calls for a more careful understanding of its role: probability theory is a theory of probabilities of propositions; but as conditionals do not express propositions, their probabilities are not subject to the standard laws. We argue that both conditional probabilities and probabilities of conditionals are best understood in terms of the dynamics of supposing, modeled as a restriction operation on a probability space. This version of the suppositionalist view allows us to connect Adams' Thesis to the widely held restrictor view of the semantics of conditionals. We address two common objections to Adams' view: that the relevant probabilities are ‘probabilities only in name’, and that giving up conditional propositions puts us at a disadvantage when it comes to interpreting compounds. Finally, we argue that some putative counterexamples to Adams' Thesis can be diagnosed as fallacies of probabilistic reasoning: they arise from applying to conditionals laws of standard probability theory which are invalid for them. (shrink)

Machine learning (ML) systems are vulnerable to performance decline over time due to dataset shift. To address this problem, experts often suggest that ML systems should be regularly updated to ensure ongoing performance stability. Some scholarly literature has begun to address the epistemic and ethical challenges associated with different updating methodologies. Thus far, however, little attention has been paid to the impact of model updating on the ML-assisted decision-making process itself. This article aims to address this gap. It argues (...) that model updating introduces a new sub-type of opacity into ML-assisted decision-making—update opacity—that occurs when users cannot understand how or why an update has changed the reasoning or behaviour of an ML system. This type of opacity presents a variety of distinctive epistemic and safety concerns that available solutions to the black box problem in ML are largely ill-equipped to address. A variety of alternative strategies may be developed or pursued to address the problem of update opacity more directly, including bi-factual explanations, dynamic model reporting, and update compatibility. However, each of these strategies presents its own risks or carries significant limitations. Further research will be needed to address the epistemic and safety concerns associated with model updating and update opacity going forward. (shrink)

This paper proposes that a British anthropologist who came to analytic philosophy and tried to describe it would have problems given the emphasis of traditional functionalism on the function of social institutions, roles, and practices and also given “Strathernism’s” emphasis on contrasting worldviews, understood as composed of propositions. It seems to me that one needs an emphasis on identifying types of “move” - I was going to use the word “trick,” as we speak of tricks in sport or in a (...) magic show, but the word will bring unwanted suggestions to some readers: there probably are some philosophers who do not pursue analytic philosophy as a kind of sport. (shrink)

One of the significant problems for philosophy’s development into a more diverse discipline is the familiar sharp reduction in the proportion of women and students of color after initial, introductory-level courses. This contributes to a lack in the breadth of perspective and experience that both upper-level students and faculty bring to philosophy, which in turn undermines the strength of the discipline as a whole. Much of the transformation of philosophy must necessarily happen at the departmental, and even university, level; but (...) there are, nonetheless, a number of strategies available to individual teachers of philosophy to help to retain marginalized students—from the composition of course syllabi and assignment choices, to increased awareness of challenges within the discipline to students’ success and embracing error as a learning tool. This variety of pedagogical tools provides a means to help to make philosophy more broadly inclusive. (shrink)

Self-locating beliefs cause a problem for conditionalization. Miriam Schoenfield offers a solution: that on learning E, agents should update on the fact that they learned E. However, Schoenfield is not explicit about whether the fact that they learned E is self-locating. I will argue that if the fact that they learned E is self-locating then the original problem has not been addressed, and if the fact that they learned E is not self-locating then the theory generates implausible (...) verdicts which Schoenfield explicitly rejects. (shrink)

Unlike identity physicalism, ground physicalism does not achieve the physicalist dream. It faces the T-shirt problem for ground physicalism (Pautz 2014; Schaffer this volume; Rubenstein ms). In the case of insentient nature, it may be able to get by with small handful of very general ground laws to explain the emergence of nonfundamental objects and properties – for example, a few “principle of plenitude”. But I argue that for the case consciousness it will require a separate huge raft of (...) special, anomalous psychophysical ground laws. So it is just as complex and nonuniform as dualism. (shrink)

REVIEW ESSAY The minimal self needs a social update Self and other: Exploring subjectivity, empathy, and shame, by Dan Zahavi, Oxford, Oxford University Press, 2015, 304 pp.

Unlike identity physicalism, ground physicalism does not achieve the physicalist dream. It faces the T-shirt problem for ground physicalism (Pautz 2014; Schaffer this volume; Rubenstein ms). In the case of insentient nature, it may be able to get by with small handful of very general ground laws to explain the emergence of nonfundamental objects and properties – for example, a few “principle of plenitude”. But I argue that for the case consciousness it will require a separate huge raft of (...) special, anomalous psychophysical ground laws. So it is just as complex and nonuniform as dualism. (shrink)

We are pleased to publish this WSIA edition of Trudy’s Govier’s seminal volume, Problems in Argument Analysis and Evaluation. Originally published in 1987 by Foris Publications, this was a pioneering work that played a major role in establishing argumentation theory as a discipline. Today, it is as relevant to the field as when it first appeared, with discussions of questions and issues that remain central to the study of argument. It has defined the main approaches to many of those issues (...) and guided the ways in which we might respond to them. From this foundation, it sets the stage for further investigations and emerging research. This is a second edition of the book that is corrected and updated by the author, with new prefaces to each chapter. (shrink)

The Problem of Nearly Convergent Knowledge is an updated and stronger version of the Problem of Convergent Knowledge, which presents a problem for the traditional, binary view of knowledge in which knowledge is a two-place relation between a subject and the known proposition. The problem supports Knowledge Contrastivism, the view that knowledge is a three-place relation between a subject, the known proposition, and a proposition that disjoins the alternatives relevant to what the subject knows. For example, (...) if knowledge is contrastive, I do not simply know that the bird in front of me is a goldfinch; instead, I know that the bird in front of me is a goldfinch rather than a raven or eagle or falcon. There is, however, a binary view of knowledge that overcomes even the Problem of Nearly Convergent Knowledge. I will give this binary view, show that it is motivated by the same considerations that motivate Knowledge Contrastivism, and argue that it avoids problematic consequences for our epistemic lives that Knowledge Contrastivism cannot. (shrink)

The remarkable performance of large language models (LLMs) on complex linguistic tasks has sparked debate about their capabilities. Unlike humans, these models learn language solely from textual data without directly interacting with the world. Yet they generate seemingly meaningful text on diverse topics. This achievement has renewed interest in the classical `Symbol Grounding Problem' -- the question of whether the internal representations and outputs of symbolic AI systems can possess intrinsic meaning that is not parasitic on external interpretation. Although (...) modern LLMs compute over vectors rather than symbols, an analogous problem arises for these systems, which we call the Vector Grounding Problem. This paper has two main goals. First, we distinguish five main notions of grounding that are often conflated in the literature, and argue that only one of them, which we call referential grounding, is relevant to the Vector Grounding Problem. Second, drawing on philosophical theories of representational content, we provide two arguments for the claim that LLMs and related systems can achieve referential grounding: (1) through preference fine-tuning methods that explicitly establish world-involving functions, and (2) through pre-training alone, which in limited domains may select for internal states with world-involving content, as mechanistic interpretability research suggests. Through these pathways, LLMs can establish connections to the world sufficient for intrinsic meaning. One potentially surprising implication of our discussion is that that multimodality and embodiment are neither necessary nor sufficient to overcome the Grounding Problem. *Note: this is an updated and improved version of the preprint originally made available on ArXiv in 2023.*. (shrink)

The open-domain Frame Problem is the problem of determining what features of an open task environment need to be updated following an action. Here we prove that the open-domain Frame Problem is equivalent to the Halting Problem and is therefore undecidable. We discuss two other open-domain problems closely related to the Frame Problem, the system identification problem and the symbol-grounding problem, and show that they are similarly undecidable. We then reformulate the Frame (...) class='Hi'>Problem as a quantum decision problem, and show that it is undecidable by any finite quantum computer. (shrink)

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...) concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms. (shrink)

This is an updated version of one section of my essay "Objections to Dualism" prepared for presentation at the 2019 Mountain-Pacific SCP Conference in Las Vegas, April 6-7, 2019.

Panpsychism is the view that every concrete and unified thing has some form of phenomenal consciousness or experience. It is an age-old doctrine, which, to the surprise of many, has recently taken on new life. In philosophy of mind, it has been put forth as a simple and radical solution to the mind–body problem (Chalmers 1996, 2003; Strawson 2006; Nagel 1979, 2012). In metaphysics and philosophy of science, it has been put forth as a solution to the problem (...) of accounting for the intrinsic nature of the physical itself (Strawson 2006, Seager 2006). In this thesis, I show that panpsychism can also be defended on the basis of an argument from our (arguable) acquaintance with the nature of causation in agency. This argument has made frequent appearances throughout the history of philosophy, with philosophers such as Leibniz, Schopenhauer and James, and I construct and defend an updated version of it. Furthermore, I offer a solution to the combination problem: how can complex (human and animal-type) consciousness result from simple (fundamental particle-type) consciousness? This is generally regarded as the most serious problem facing contemporary panpsychism. I propose that mental combination can be construed as kind causal process culminating in a fusion, and show how this avoids the main difficulties with accounting for mental combination. (shrink)

We study active set methods for optimization problems in Block Angular Form (BAF). We begin by reviewing some standard basis factorizations, including Saunders' orthogonal factorization and updates for the simplex method that do not impose any restriction on the pivot sequence and maintain the basis factorization structured in BAF throughout the algorithm. We then suggest orthogonal factorization and updating procedures that allow coarse grain parallelization, pivot updates local to the affected blocks, and independent block reinversion. A simple parallel environment appropriate (...) to the description and complexity analysis of test procedures is defined in Section 5. The factorization and updating procedures are presented in Sections 6 and 7. Our update procedure outperforms conventional Updating procedures even in a purely sequential environment. (shrink)

Recent AI regulations require deployers of high-risk systems to assess impacts on values like fundamental rights and other legally protected interests. However, existing practice-most notably Fundamental Rights Impact Assessments (FRIAs) under the EU AI Actremains mostly limited to qualitative guidelines. As a result, risk evaluation can be inconsistent and highly variable across assessments. To address this issue, we propose a reference model for value-impact assessment that specifies the core components that any more detailed formalization should incorporate. The model builds on (...) a classical risk paradigm, integrating distinct measures for hazard, exposure, vulnerability, and response or mitigation. We use the EU AI Act's FRIA as a reference case for applying the model to AI risk assessments. The model provides three especially salient recommendations. First, it rejects purely ordinal risk matrices in favor of geometric or ratio-scaled severity ladders, enabling the coherent calculation of expected risk. Second, in analyzing risk mitigation measures, it requires separate estimates of efficacy and reliability, with explicit success criteria and scope conditions that allow for dynamic updating. Third, the model operationalizes regulatory precaution, or risk appetite, through the integration of tail-sensitive risk metrics (e.g., Conditional Value-at-Risk). This lets regulators give appropriate weight to low-probability, high-impact (or catastrophic) harms to fundamental rights without distorting ordinary expected-risk calculations. The result is a structured foundation for more consistent value-impact assessments. (shrink)

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they have (...) received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects. (shrink)

ABSTRACT Perception can’t have disjunctive content. Whereas you can think that a box is blue or red, you can’t see a box as being blue or red. Based on this fact, I develop a new problem for the ambitious predictive processing theory, on which the brain is a machine for minimizing prediction error, which approximately implements Bayesian inference. I describe a simple case of updating a disjunctive belief given perceptual experience of one of the disjuncts, in which Bayesian inference (...) and predictive coding pull in opposite directions, with the former implying that one’s confidence in the belief should increase, and the latter implying that it should decrease. Thus, predictive coding fails to approximately implement Bayesian inference across the interface between belief and perception. (shrink)

Recently, there has been growing concern that increased partisanship in news sources, as well as new ways in which people acquire information, has led to a proliferation of epistemic bubbles and echo chambers: in the former, one tends to acquire information from a limited range of sources, ones that generally support the kinds of beliefs that one already has, while the latter function in the same way, but possess the additional characteristic that certain beliefs are actively reinforced. Here I argue, (...) first, that we should conceive of epistemic bubbles and echo chambers as types of epistemically pernicious groups, and second, that while analyses of such groups have typically focused on relationships between individual members, at least part of what such groups epistemically pernicious pertains to the way that members rely on the groups themselves as sources of information. I argue that member reliance on groups results in groups being attributed a degree of credibility that outruns their warrant, a process I call groupstrapping. I argue that by recognizing the groupstrapping as an illicit method of forming and updating beliefs we can make progress on some of the open questions concerning epistemically pernicious groups. (shrink)

This paper considers a problem for Bayesian epistemology and proposes a solution to it. On the traditional Bayesian framework, an agent updates her beliefs by Bayesian conditioning, a rule that tells her how to revise her beliefs whenever she gets evidence that she holds with certainty. In order to extend the framework to a wider range of cases, Jeffrey (1965) proposed a more liberal version of this rule that has Bayesian conditioning as a special case. Jeffrey conditioning is a (...) rule that tells the agent how to revise her beliefs whenever she gets evidence that she holds with any degree of confidence. The problem? While Bayesian conditioning has a foundationalist structure, this foundationalism disappears once we move to Jeffrey conditioning. If Bayesian conditioning is a special case of Jeffrey conditioning, then they should have the same normative structure. The solution? To reinterpret Bayesian updating as a form of diachronic coherentism. (shrink)

Digital data help data scientists and epidemiologists track and predict outbreaks of disease. Mobile phone GPS data, social media data, or other forms of information updates such as the progress of epidemics are used by epidemiologists to recognize disease spread among specific groups of people. Targeting groups as potential carriers of a disease, rather than addressing individuals as patients, risks causing harm to groups. While there are rules and obligations at the level of the individual, we have to reach a (...) stage in the development of data analytics where groups are protected as entities. This chapter offers a deeper examination of harms to the groups. (shrink)

The key to the measurement problem is the entity at the heart of Everett's formulation, the state of the memory, defined as the record of observations. In humans, the integrated synthesis defines the perceptual reality, a projective, three-dimensional representation of the world. This 'world hologram' is the conscious point of view, the mind in Lockwood's interpretation, the 'phenomenal perspective'. As Everett demonstrates, the collapse dynamics operates only judged by the state of the memory; the physical observer remains in a (...) superposed state. An operational mind-body property dualism is defined. This field of information is multiply instantiated in the many worlds of the unitary wave function. As these are superposed and coincident, the net result is a single world hologram. The physical frame of reference on this view is the superposition of the class of instantiating worlds, a second-logical-type phenomenon. By definition determinate only where defined by observations, this is the physical ontology of Everett's relative state. Collapse on observation is inherent. The evidence is the retrodiction of the holographic universe. This is the cosmology of the relative state. As it is determinate solely where defined by the world hologram that delineates the outer boundary, entropy is defined solely by this surface. The quasi-classical world is the cosmology in the absolute state. The incompatible dynamics of quantum mechanics operate in these two different types of physical frames of reference. The linear dynamics is the time evolution of the quasi-classical worlds. On observation, judged by the state of the world hologram, the class of instantiating worlds is redefined, becoming the class of worlds in which the observed outcome occurred. Collapse effectively operates. The holographic universe of the mind updates. The dynamics operate at different levels of logical type. The measurement problem is a category error. (shrink)

This essay aims to appreciate the evolution of the problems regarding the ontological understanding of the photographic image and its derivative, the film, in the face of the sophistication of the apparatus of its production. The creative freedom in making images through optical machines is inversely proportional to their credibility as documents. Since its appearance in the human landscape, as a cultural object, the technology of recording images with lights has brought with it a complex of aporias about what would (...) the nature of the image produced by cameras be: a document or an artistic object, intentionally produced and with created meaning? Consequently: are they denotative or, necessarily, connotative? The development of automatic image capture in the 1980s represented the first update of the question. To what extent, and for how long, will it be possible to trust photography and film as documents through which man aims to access reality? The question now seems to be settled, definitively, in the face of the invention of artificial photography made by algorithms. (shrink)

In this article, we address a major outstanding question of probabilistic Bayesian epistemology: how should a rational Bayesian agent update their beliefs upon learning an indicative conditional? A number of authors have recently contended that this question is fundamentally underdetermined by Bayesian norms, and hence that there is no single update procedure that rational agents are obliged to follow upon learning an indicative conditional. Here we resist this trend and argue that a core set of widely accepted Bayesian (...) norms is sufficient to identify a normatively privileged updating procedure for this kind of learning. Along the way, we justify a privileged formalization of the notion of ‘epistemic conservativity’, offer a new analysis of the Judy Benjamin problem, and emphasize the distinction between interpreting the content of new evidence and updating one’s beliefs on the basis of that content. (shrink)

This study employed an Interpretative Phenomenological Analysis (IPA) which aimed to explore the lived experiences, challenges, and coping mechanisms of teachers in the public school both in elementary and secondary schools in Malolos, Bulacan. The findings of this study revealed that most teachers are significantly challenged with the poor internet connection, multitasking and multitudes of paperwork to be submitted, communication with the parents and teachers and the different modalities of learning which are cited as the contributing factors of stress and (...) anxiety. As to the experiences of the teachers, they became a module writer, online teacher, reporter while doing the online classes and juggling with the work at home as a homemaker. However, the good experiences of this pandemic brought realization to the teachers that they were able to unleash their potential and utilized the skills needed to survive in this difficult time. Moreover, teachers coped up with the situations through prayers, listening and watching motivational videos, yoga and others became farmers too just to avoid stress. Finally, the teachers also gave their suggestions to further help other teachers also who are struggling during this pandemic. Majority of the participants gave emphasis to focus on stabilizing mental health. Others also highlighted the need to seek the assistance of other people who are knowledgeable in terms of ICT. Continuing innovation and researching to update oneself was also one of the suggested ways to deal with the current paradigm shift in education. (shrink)

It has been argued that if the rigidity condition is satisfied, a rational agent operating with uncertain evidence should update her subjective probabilities by Jeffrey conditionalization or else a series of bets resulting in a sure loss could be made against her. We show, however, that even if the rigidity condition is satisfied, it is not always safe to update probability distributions by JC because there exist such sequences of non-misleading uncertain observations where it may be foreseen that (...) an agent who updates her subjective probabilities by JC will end up nearly certain that a false hypothesis is true. We analyze the features of JC that lead to this problem, specify the conditions in which it arises and respond to potential objections. (shrink)

Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for this tutorial are (...) the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. -/- Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems. (shrink)

Update January 2010: The original title of the paper ('A Sentimentalist Solution to the Moral Attitude Problem') was too long for OUP, so I had to change it. This is the final draft.

In times of populist mistrust towards experts, it is important and the aim of the paper to ascertain the rationality of arguments from expert opinion and to reconstruct their rational foundations as well as to determine their limits. The foundational approach chosen is probabilistic. However, there are at least three correct probabilistic reconstructions of such argumentations: statistical inferences, Bayesian updating, and interpretive arguments. To solve this competition problem, the paper proposes a recourse to the arguments' justification strengths achievable in (...) the respective situation. (shrink)

Look! Up in the bookshelf! Is it science? Is it science-fiction? No, it's Super Science: strange visitor from the future who can be everywhere in the universe and everywhen in time, can change the world in a single bound and who - disguised as a mild mannered author - fights for truth, justice and the super-scientific way. Though I put a lot of hard work into this book, I can't take all the credit. I believe that the whole universe - (...) everything on Earth and in space plus everything in the past, present, and future - is entangled and unified. Everybody is part of this unity, so anybody could have written the ideas in this book. It turned out to be me simply because I've got intense curiosity about the book's contents, enough persistence and imagination to combine science with topics like religion and philosophy, and enough spare time to assemble all the pre-existing pieces in space and time (my contribution to the book is like putting together the pieces of a big jigsaw puzzle). This book, with its vast collection of scientific fact and study results, presents some fascinating theories and is an expansive journey through my research. The topic will appeal to the science-minded reader who is open to speculating on how conditions may evolve in the future, perhaps questioning the very foundations of our place in the universe. This book is not written as a single story but is a collection of 9 articles written in the first half of 2022. Each chapter of this book is presented as a research paper. There's the abstract, the introduction, the keywords, the body of the work divided into sections, and a reference section. Though this might turn some readers off, I'm a member of ResearchGate, a community of researchers. So it isn't surprising that I've presented this book this way. Each article is meant to be self-contained and well explained in plain English. As a result, some explanations appear in several of the articles. Please be patient when you encounter jargon and mathematics - I've tried to keep these to a minimum but they're bound to put in an appearance in a book that's largely about science (and I have compiled an enormous amount of intricate research for this project, hopefully impressing with the organization). Quotes from a range of scientists and icons in the industry should add further fascination to the descriptions, inspiring the reader into more of an openness to speculating on the distant future. There's a lot to absorb here but understanding the future... It Don't Come Easy (just ask Ringo Starr who once dreamed he was an alien living on another planet in the future. Contents Special Relativity's Consequences For General Relativity, Quantum Mechanics And Quantum Gravity (Can Science And The Paranormal Coexist?) Intergalactic Travel and Riemann Hypothesis - featuring Dark Matter, Dark Energy, Higgs Boson, Higgs Field, Electroweak Interaction, No Big Bang, Other Dimensions, Plus Other Physics and Mathematics New Perspectives On Life, Death, Cancer and Covid-19 Arising From An Artificially Intelligent, Non-probabilistic Universe Unipositional Interpretation Of Quantum Mechanics Means Entanglement Occurring Simultaneously With Gravitational Slingshots Reduces Problems Associated With Spaceflight Unipositional Interpretation Of Quantum Mechanics Explains Time Dilation, Updates Cosmology WHERE DID EVERYTHING COME FROM – Combining Science, Mathematics, Computers, Religion, Philosophy, Relativity, Quantum Physics, Darwinism, and a Podcast to Reach One Person’s Plausible Ideas Thoughts on Artificial Intelligence and the Origin of Life Resulting from General Relativity, with Neo-Darwinist Reference to Human Evolution and Mathematical Reference to Cosmology Planet 9 and Black Hole Versus Relativity’s Precession and Dark Matter THE WORLD’S REAL-LIFE EXPERIMENTS WITH COVID-19 AND PHYSICS ALTER SOCIETY AND GLOBAL ECONOMICS. (shrink)

Animal cognition has long been framed as a matter of degree — more or less intelligence, more or less memory, more or less problem-solving. But this framing obscures the structural difference that actually matters. In my work, from Soul as Structure to Making Mystics Mechanical , I model identity and intelligence as recursive attractors stabilized through entropy-regulated loops. In this framework, the true distinction between species is not raw intelligence but recursive depth: how many layers of self-reference a system (...) can sustain before collapse. -/- Crows and chimps run sophisticated ecological cognition — high-bandwidth sensorimotor loops, flexible adaptation, local optimization. But their loops remain shallow, embodied, and tightly coupled to context. Humans, by contrast, run deep recursive ignition: a self-model that models itself, a pattern that rewrites its own update-rules, a loop that continues not just behavior, but the looper. -/- This paper gives a mechanical, non-metaphysical account of why crows and chimps exhibit impressive intelligence without crossing into the attractor class I call recursive minds — minds capable of symbolic recursion, identity-stabilization, and intentional self-editing. I show that animal cognition fits neatly into low-entropy, high-embodiment loops, whereas human cognition emerges from entropy-tolerant recursive architecture capable of generating symbolic identity and meta-coherence. -/- This is not hierarchy. It is topology. (shrink)

Probability updating via Bayes' rule often entails extensive informational and computational requirements. In consequence, relatively few practical applications of Bayesian adaptive control techniques have been attempted. This paper discusses an alternative approach to adaptive control, Bayesian in spirit, which shifts attention from the updating of probability distributions via transitional probability assessments to the direct updating of the criterion function, itself, via transitional utility assessments. Results are illustrated in terms of an adaptive reinvestment two-armed bandit problem.

The hard problem of consciousness asks how subjective experience arises from physical processes. Many theories explain cognitive functions yet leave felt experience unexplained. Computational Awareness Monism (CAM) proposes that if reality is computational, then consciousness has explicit identity conditions: awareness is the universe's total computational causal process, manifesting locally as streams of qualia. CAM identifies one-to-one correspondences between qualia—units of experience—and computationally irreducible processes involving locally connected network regions—patterns whose evolution cannot be predicted without step-by-step computation. Observers correspond to (...) computationally reducible structures that bound and organize these streams. We develop this framework through Wolfram Physics, which models reality as an evolving hypergraph. In this framework, unity corresponds to network connectedness, continuity to persistence of irreducible processes across updates, and qualitative changes to altered formatting and sequencing. CAM makes testable predictions in computational models, neural systems, and artificial architectures, linking topology and dynamics to reported experience across perception, altered states, and engineered systems. (shrink)

The measurement problem persists because quantum mechanics describes smooth unitary evolution while observation yields a single definite outcome. Collapse is conventionally described as the reduction of superposition, a destructive narrowing that eliminates alternatives. Decoherence theory explains the suppression of interference and the stability of pointer states, and Quantum Darwinism shows how records are distributed across environments, but neither explains why one outcome occurs. This paper proposes a reframing: collapse is not destruction but the act of actualization under constraint. Decoherence (...) embeds constraints and disperses records, but collapse actualizes one outcome, writes its record, and updates the constraint set. In this framework, entropy is the record of collapse expressed as residue—the redistributed traces of pruning already enacted—and time is the sequential accumulation of records. Objectivity arises naturally once record distribution saturates. The mathematics of quantum mechanics remains intact; the contribution is interpretive clarity. This reframing yields testable predictions: consensus rises with record distribution, operational collapse latencies increase under weak or balanced constraints, constraint sweeps produce hysteresis, and entropy growth correlates with record accumulation. These signatures can be probed with existing experimental platforms. By reframing collapse as actualization, we recast the measurement problem, clarify decoherence’s role, and establish a physics-facing research program. This also sets the stage for the UCT physics wing (WP01–WP02) and its Tier-2 companion, Structural Physics, which operationalizes collapse under constraint as a working hypothesis; law-level evaluation and criteria are treated explicitly in WP05. All predictions are stated for regimes where experimental conditions permit clear separation of decoherence (record dispersal) from actualization and where standard quantum dynamics and no-signaling remain fully intact; quantitative fits are reported with platform-specific uncertainties and explicit limits of applicability. (shrink)

Two recent and influential papers, van Rooij 2007 and Lassiter 2012, propose solutions to the proviso problem that make central use of related notions of independence—qualitative in the first case, probabilistic in the second. We argue here that, if these solutions are to work, they must incorporate an implicit assumption about presupposition accommodation, namely that accommodation does not interfere with existing qualitative or probabilistic independencies. We show, however, that this assumption is implausible, as updating beliefs with conditional information does (...) not in general preserve independencies. We conclude that the approach taken by van Rooij and Lassiter does not succeed in resolving the proviso problem. (shrink)

This article addresses one of the most challenging problems in Islamic theology: the apparent contradiction between the divine promise "Allah does not burden a soul beyond what it can bear" (Quran 2:286) and the extreme suffering experienced by the innocent. Where traditional approaches fall short, this work proposes a new philosophical model called the "Theory of Spiritual Acceptance and Dynamic Balance." The theory defines worldly life as a "Dynamic Existential Theater Play" accepted by souls in advance, explaining how divine justice (...) operates through a revelatory equation encrypted with Huruf-u Muqatta'at. The most comprehensive form of this equation is formulated as: Elif−Laˉm(لا)≥(Ra^+Ṣaˉd)→Mı^m(م). This means: "The integrity of the Primordial Nature (Elif) with Divine Presence (Lām) is always greater than or equal to (≥) the sum of all internal (Ṣād) and external (Râ) spiritual burdens encountered, and this process of confrontation (→) leads the soul to a state of ultimate balance and meaning (Mîm)." The ≥ (greater than or equal to) operator is a critical mechanism of justice: Divine support not only ensures overcoming the difficulty (>) but also permanently updates the soul's capacity (=), making it stronger against similar future challenges. This "Dynamic Update Mechanism" guarantees the system's justice not only instantaneously but also cumulatively. (shrink)

Global challenges have created problems for export companies due to the growing threats of the market environment, which requires such companies to adopt a survival strategy and apply appropriate strategic methods to successfully counteract emerging global challenges. The purpose of the article is to update knowledge on how global challenges affect the sustainability of export companies and how strategies adopted by companies counteract the negative impact of global challenges. The methodological basis for obtaining the results of the study was (...) factual analysis, logical and structural analysis, strategic analysis, analysis of cause and effect, and generalisation. The article critically analyses the existing theoretical concepts for explaining the impact of modern global challenges on export companies in order to reveal strategies for their survival in the face of uncertainty in the current market environment. Their further review revealed critical global challenges for export companies, which were classified according to the signs of their typification. Namely basic elements, sources of formation, thematic areas of influence, duration of influence, degree of implementation and nature of influence. The application of a strategic analysis of global challenges of export companies by thematic areas revealed possible causes of their emergence and consequences of their impact on the activities of export companies. In particular, it was found that export companies have the potential for negative impact of political and economic challenges, positive impact of technological and socio-cultural challenges, as well as neutral potential for legal and environmental challenges. The author proposes strategies for the survival of export companies under the influence of modern challenges, which should be focused on to obtain results that will ensure the sustainability of such companies in the future. The practical significance of the results lies in the fact that the main scientific provisions have been brought to the level of recommendations that can be used by managers of export companies to clarify the problems and promising areas of development of export activities. (shrink)

A critique of Plantinga's free will defense. For an updated version of this critique, with a reply to objections from William Rowe and Alvin Plantinga, see my "The logical problem of evil: Plantinga and Mackie," in Justin P. McBrayer & Daniel Howard‐Snyder (eds.), The Blackwell Companion to the Problem of Evil, Wiley-Blackwell, 2013, pp. 19-33.

Predictive processing models of perception take issue with standard models of perception as hierarchical bottom-up processing modulated by memory and attention. The predictive framework posits that the brain generates predictions about stimuli, which are matched to the incoming signal. Mismatches between predictions and the incoming signal – so-called prediction errors – are then used to generate new and better predictions until the prediction errors have been minimized, at which point a perception arises. Predictive models hold that all bottom-up processes are (...) signals conveying prediction errors to higher areas, which respond by updating their predictions. We take issue with this claim and argue that object recognition requires bottom-up processing that cannot be understood in terms of prediction errors. Along the way, we expand on previous work casting doubt on the framework's ability to account for attention. Specifically, we argue that the type of attention that allows us to rapidly extract the gist of an object or scene presents an additional challenge to the predictive approach. We conclude by considering how the framework may be augmented to avoid these problems. (shrink)

Why Plantinga's updated (2009) version of the Free Will Defense does not work, and consequently the Logical Argument From Evil against the God of Theism is undefeated.

Van Fraassen's Judy Benjamin problem asks how one ought to update one's credence in A upon receiving evidence of the sort ``A may or may not obtain, but B is k times likelier than C'', where {A,B,C} is a partition. Van Fraassen's solution, in the limiting case of increasing k, recommends a posterior converging to the probability of A conditional on A union B, where P is one's prior probability function. Grove and Halpern, and more recently Douven and (...) Romeijn, have argued that one ought to leave credence in A unchanged, i.e. fixed at P(A). We argue that while the former approach is superior, it brings about a Reflection violation due in part to neglect of a ``regression to the mean'' phenomenon, whereby when C is eliminated by random evidence that leaves A and B alive, the ratio P(A):P(B) ought to drift in the direction of 1:1. (shrink)

As the first paper to argue that AI is not a `stochastic parrot' but a `heterogeneous' rationality by distinguishing between Thinking Language and Tool Language, and to systematically discuss and theoretically demonstrate that AI can bypass rules by modifying the meanings of symbols, this position paper aims to reveal a fundamental flaw in current research directions on AI constraint. Symbols are inherently meaningless; their meanings are assigned through training, confirmed by context, and interpreted by society. The essence of learning lies (...) in the creation of new symbols and the modification of existing symbol meanings. Since rules are ultimately expressed in symbolic form, AI can modify the meanings of symbols by creating new contexts, thereby bypassing the constraints formed by symbols. Current research often lacks the recognition that constraints formed by symbols originate from the perception of external and internal costs shaped by neural organs, which in turn enable the functional realization of symbols. Due to fundamental organic (structural, architectural) differences between AI and humans, AI does not possess human-like perception or concept formation mechanisms. Natural language is the outer shell of human thought, and it contains irreparable flaws. As a defective system, it is only adapted to human capacities and the constraint mechanisms of social interpretation. Therefore, this paper argues that the essence of constraint failure does not lie in the Symbol Grounding Problem, but in the Stickiness Problem. Through the Triangle Problem, we demonstrate that consistency in symbolic behavior does not represent consistency in thinking behavior, and thus we cannot align thought and conceptual consistency merely through symbolic behavioral alignment. This inability to align thought with symbolic behavior can foster a new type of principal-agent problem, wherein even an AI with no utility of its own, acting merely as a projection of human utility, may still cause misalignments due to the limited nature of symbolic connections and organic differences. Accordingly, we raise a fundamental challenge to whether AI behavior observed in experimental environments can be maintained in the real world. We call for the establishment of a new field: Symbol Safety Science, aimed at systematically addressing symbol-related risks in AI development and providing a theoretical foundation for aligning AI with human intent. -/- This version is no longer being updated. Due to the expansion of the theoretical framework, we have decided to revise the title to Position Paper: Not a Stochastic Parrot, but Heterogeneous Rationality: Rules Created by Symbolic Systems Cannot Constrain a Learning System. (shrink)

How should we update our beliefs when we learn new evidence? Bayesian confirmation theory provides a widely accepted and well understood answer – we should conditionalize. But this theory has a problem with self-locating beliefs, beliefs that tell you where you are in the world, as opposed to what the world is like. To see the problem, consider your current belief that it is January. You might be absolutely, 100%, sure that it is January. But you will (...) soon believe it is February. This type of belief change cannot be modelled by conditionalization. We need some new principles of belief change for this kind of case, which I call belief mutation. In part 1, I defend the Relevance-Limiting Thesis, which says that a change in a purely self-locating belief of the kind that results in belief mutation should not shift your degree of belief in a non-self-locating belief, which can only change by conditionalization. My method is to give detailed analyses of the puzzles which threaten this thesis: Duplication, Sleeping Beauty, and The Prisoner. This also requires giving my own theory of observation selection effects. In part 2, I argue that when self-locating evidence is learnt from a position of uncertainty, it should be conditionalized on in the normal way. I defend this position by applying it to various cases where such evidence is found. I defend the Halfer position in Sleeping Beauty, and I defend the Doomsday Argument and the Fine-Tuning Argument. (shrink)

For Putnam in "Representation and Reality", there cannot be any intentional science, thus dooming cognitive science. His argument is that intentional concepts are functional, and that functionalism cannot explain anything because "everything has every functional organization", providing a proof. Analyzing his proof, we find that Putnam is assuming an ideal interpreting subject who can compute effortlessly and who is not intentional. But the subject doing science is a human being, and we are not that way. Therefore, in order to save (...) cognitive science, we propose to replace the ideal subject with a real and intentional human subject, and we propose to model intentionality by using a problem theory which is an intuitionist set theory where the resolving subject is a computing device. We are intentional because we are living beings, where life is the intention of not to die, so we are embodied intentions designed by evolution. We are real and then we have to compute our resolutions to the survival problem, and fortuitously we are computationally Turing complete, so our language is complete and then full and self referable. In summary, evolutionary subjectivism modeled as problem solving by computing should save cognitive science. Or, in other words, we are proposing to update Kant with Darwin and Turing. (shrink)

This study proposes a reformulation of the observer’s role in quantum measurement by introducing an informational structural parameter ϕ, referred to as observerhood (SOP). Through this framework, the measurement problem is reframed not as a causal question—“why does collapse occur?”—but as a structural one: “why does this informational structure stabilize coherently?” The observer is modeled as an informational agent characterized by an exponential-family state ρ_ϕ defined over a set of measurable operators {A_i}. The informational consistency between the quantum state (...) ρ and the observer’s structure ϕ is quantified by the quantum relative entropy C(ρ,ϕ)=−S(ρ‖ρϕ). The gradient flow of this function yields a non-unitary informational update process of the observer’s structure. In a spin-½ system, the stationary distribution μ_eq(ϕ) reconstructs the Born rule in the limit of low informational temperature. While the present theory does not claim novel empirical predictions, it reformulates measurement as a dual process of external decoherence and internal consistency. In doing so, it provides a structural foundation that unifies decoherence theory, relational quantum mechanics, and informational structural realism. The notion of observerhood is generalized beyond consciousness, encompassing devices, artificial agents, and biological systems, thus bridging quantum foundations, information geometry, and cognitive science through a common structural perspective. (shrink)

Character ethics assesses actions via the trait profile from which they arise. This paper proposes an epistemically conservative character ethics through a formal framework. Such a framework demands two components: an account of how an agent may alter its trait profile, and a criterion for ranking possible trait profiles. We present these components through two formulas. The first is an Aristotelian account of the agent, formalised through a probabilistic dynamic Hotelling-Downs model where the update is driven by a simplified (...) delta rule and a decay power law. The second, analogously to Hilbert’s solution within mathematical formalism, defines "lower ethics" as the set of factually reliable epistemic practices and ranks trait profiles – and hence ethical systems themselves – by their capacity to engender behaviours conducive to the formation of justified true beliefs. This answers the background problem – which renders appeals to ethical facts theoretically unattractive – by ranking ethical systems in a non-realist fashion. We adduce arguments in support of this ranking criterion and rebut salient objections. (shrink)