Stein has characterized one of the central problems in accounting for our knowledge in physics as that of getting the laboratory, or observatory, inside the theory -- that is, of understanding how the mathematical structures of fundamental physical theories have empirical content. He has argued that physicists respond to this problem by giving schematic representations of observers and experiments. In addition, Stein emphasizes the importance of regarding knowledge as an enterprise, with current theories providing guidance for future inquiry. I will (...) explore some ramifications of this way of thinking about the structure of scientific theories for contemporary cosmology. One goal of observational cosmology is to measure the six basic parameters appearing in the standard model of cosmology. These parameters are well-defined if the universe is suitably approximated at some scale by a perturbed FLRW model. The enormous extrapolations involved in the standard model are often justified by the consistent determination of these parameters via a variety of methods. Here I will consider two recent debates regarding this approach to cosmology, inspired by Stein's work. The first debate regards the impact of different ways of characterizing the propagation of light through a cosmological spacetime on the determination of cosmological parameters (such as H_0). The second regards how the highly symmetric FLRW models relate to describing the real universe, at small scales where it is very lumpy. (shrink)

Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements of (...) truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (shrink)

Does the capacity to think require the capacity to sense? A lively debate on this topic runs throughout the history of philosophy and now animates discussions of artificial intelligence. Many have argued that AI systems such as large language models cannot think and understand if they lack sensory grounding. I argue that thought does not require sensory grounding: there can be pure thinkers who can think without any sensory capacities. As a result, the absence of sensory grounding does not (...) entail that large language models cannot think or understand. I also consider to what extent quasi-sensory grounding can at least boost the performance of a language model. (shrink)

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users anthropomorphise (...) them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. (shrink)

This research reviews explanation and interpretation for Explainable Artificial Intelligence (XAI) methods in order to boost complex machine learning model interpretability. The study shows the influence and belief of XAI in users that trust an Artificial Intelligence system and investigates ethical concerns, particularly fairness and biasedness of all the nontransparent models. It discusses the shortfalls related to XAI techniques, putting crucial emphasis on extended scope, enhancement and scalability potential. A number of outstanding issuesespecially in need of further work can (...) involve standardization, user-centered design and interdisciplinary in strategies for improving the practical utility of XAI. (shrink)

Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account of the nature (...) and function of representation in computational cognitive models. (shrink)

A key challenge for probabilistic causal models is to distinguish non-causal probabilistic dependencies from true causal relations. To accomplish this task, causal models are usually required to satisfy several constraints. Two prominent constraints are the causal Markov condition and the faithfulness condition. However, other constraints are also needed. One of these additional constraints is the causal sufficiency condition, which states that models must not omit any direct common causes of the variables they contain. In this paper, I (...) argue that the causal sufficiency condition is problematic: (1) it is incompatible with the requirement that the variables in a model must not stand in non-causal necessary dependence relations, such as mathematical or conceptual relations, or relations described in terms of supervenience or grounding, (2) it presupposes more causal knowledge as primitive than is actually needed to create adequate causal models, and (3) if models are only required to be causally sufficient, they cannot deal with cases where variables are probabilistically related by accident, such as Sober’s example of the relationship between bread prices in England and the sea level in Venice. I show that these problems can be avoided if causal models are required to be monotonic in the following sense: the causal relations occurring in a model M would not disappear if further variables were added to M. I give a definition of this monotonicity condition and conclude that causal models should be required to be monotonic rather than causally sufficient. (shrink)

This paper examines the potential educational uses of chat-based large language models (LLMs), moving past initial hype and skepticism. Although LLM outputs often evoke fascination and resemble human writing, they are unpredictable and must be used with discernment. Several metaphors—like calculators, cars, and drunk tutors—highlight distinct models for student interactions with LLMs, which we explore in the paper. We suggest that LLMs hold a potential in students’ learning by fostering proleptic reasoning through scaffolding, i.e., presenting a technological accompaniment (...) in anticipating and responding to potential objections to arguments. Here, the technical limitations of LLMs can be reframed as beneficial when fostering anticipatory reasoning. Whether their outputs are accurate or not, evaluating them stimulates learning. LLMs require students to critically engage, emphasizing analytical thinking over mere memorization. This interaction helps solidify knowledge. Additionally, we explore how engaging with LLMs can prepare students for constructive collective discussions and provide first steps in addressing epistemic injustices by highlighting potential research blind spots. Thus, while acknowledging the sociopolitical and ethical complexities of using LLMs in education, we suggest that when used in an informed way, they can promote critical thinking through anticipatory reasoning. (shrink)

Analytical categories of scientific cultures have typically been used both exclusively and universally. For instance, when styles of scientific research are employed in attempts to understand and narrate science, styles alone are usually employed. This article is a thought experiment in interweaving categories. What would happen if rather than employ a single category, we instead investigated several categories simultaneously? What would we learn about the practices and theories, the agents and materials, and the political-technological impact of science if we analyzed (...) and applied styles, paradigms, and models simultaneously? I address these questions in general and for a specific case study: a brief history of systematics. (shrink)

Continued urbanization, along with anthropogenic global warming, has and will increase land surface temperature and air temperature anomalies in urban areas when compared to their rural surroundings, leading to Urban Heat Islands (UHI). UHI poses environmental and health risks, affecting both psychological and physiological aspects of human health. Thus, using a deep learning approach that considers morphological variables, this study predicts UHI intensity in 69 European cities from 2007 to 2021 and projects UHI impacts for 2050 and 2080. The research (...) employs Artificial Neural Networks, Deep Neural Networks, and Gated Recurrent Units, combining high-resolution 3D urban models with environmental data to analyze UHI trends. The results indicate strong associations between urban form, weather patterns, and UHI intensity, highlighting the need for customized urban planning and policy measures to reduce UHI impacts and foster sustainable urban settings. This research enhances understanding of UHI dynamics and serves as a valuable tool for urban planners and policymakers to address the challenges of climate change, urbanization, and air pollution, ultimately aiding in the improvement of health outcomes and building energy consumption. Moreover, the methodology effectively demonstrates the ability of the GRU to link its scores with UHI projections, offering crucial insights into potential health impacts. (shrink)

Today's climate models are supported in a couple of ways that receive little attention from philosophers or climate scientists. In addition to standard 'model fit', wherein a model's simulation is compared to observational data, there is an additional type of confirmation available through the variety of instances of model fit. When a model performs well at fitting first one variable and then another, the probability of the model under some standard confirmation function, say, likelihood, goes up more than under (...) each individual case of fit alone. Thus, two instances of fit of distinct variables of a global climate model using distinct data sets considered collectively will provide stronger evidence for a model than either one of the instances considered individually. This has consequences for model robustness. Sets of models that produce robust results will, if their assumptions vary enough and they each are observationally sound, provide reasons to endorse common structures found in those models. Finally, independent empirical support for aspects and assumptions of the model provides an additional confirmational virtue for climate models, contrary to what is implied by some current philosophical writing on this topic. (shrink)

Judgments of misinformation are made relative to the informational preferences of the communities making them. However, informational standards change over time, inducing distribution shifts that threaten the adequacy of machine learning models of misinformation. After articulating five kinds of distribution shifts, three solutions for enhancing success are discussed: larger static training sets, social engineering, and dynamic sampling. I argue that given the idiosyncratic ontology of misinformation, the first option is inadequate, the second is unethical, and thus the third is (...) superior. However, I conclude that the prospects for machine learning models of misinformation are far weaker than most have presupposed, given that both epistemic and non-epistemic values are difficult to operationalize dynamically in machine code, rendering them surprisingly at most a species of recommender systems rather than literal truth detectors. (shrink)

The potential of using personalized Large Language Models (LLMs) or “generative AI” (GenAI) to enhance productivity in academic research, as highlighted by Porsdam Mann and colleagues (Porsdam Mann...

Moral circles help define the boundaries of one’s moral consideration. One’s moral circle may provide insight into how one perceives or treats other entities. A data-driven model exploration was conducted to explore predictors and prediction models. Candidate predictors were built upon past research using moral foundations and political orientation. Moreover, we also employed additional moral psychological indicators, i.e., moral reasoning, moral identity, and empathy, based on prior research in moral development and education. We used model exploration methods, i.e., Bayesian (...) model exploration, Bayesian model averaging, and elastic-net regression. The study successfully replicated past research supporting the relationship between moral foundations, political orientation, and the moral circle. Additional moral psychological constructs, such as post-conventional moral reasoning and moral identity, significantly predicted the moral circle width. The identified components of the moral circle were conceptually related to phronesis, i.e., practical wisdom. We discussed the educational implications of the findings, particularly those related to moral education focusing on phronesis cultivation, multiculturalism and global citizenship, and climate justice. (shrink)

This chapter from Michel Serres’s comprehensive study on Leibniz—"The System of Leibniz and its Mathematical Models [Le système de Leibniz et ses modèles mathématiques]"—examines Leibniz’s epistemological framework. This framework, which Leibniz developed for a large part in his “Meditations on Knowledge, Truth, and Ideas [Meditationes de cognitione, veritate et ideis],” is pitched against Descartes’s "Meditations on First Philosophy [Meditationes de Prima Philosophia]" and the method of systematic doubt developed therein. While Descartes rejects any knowledge with the slightest possibility of (...) falsehood, Leibniz accepts knowledge with even a minimal degree of truth. Leibniz’s approach involves a progressive genesis of truth through a series of filters, each further refining knowledge. This process is intrinsically linked to infinitism and combinatorics, allowing for a gradual differentiation of the distinct from the indistinct. The filters are applied sequentially, with each filter proceeding through its own oppositions, resulting in a spectrum of knowledge ranging from obscure to clear, confused to distinct, inadequate to adequate, and symbolic to intuitive. This framework facilitates pluralism within rationalism that proceeds through evolving or regional truths. The filters overlap, and only the final stage of knowledge is devoid of mixture. Leibniz’s epistemology is non-Cartesian, as it relativizes truth and falsehood at each stage of the filtering process. It also accounts for the limitations of sensory perception, attributing them to the imperfection of the human mind in its current state. (Translated by Martijn Boven). (shrink)

The technology known as artificial intelligence (AI) has become a game-changer, capable of completely altering a number of facets of civilization. But as AI grows more and more common, significant ethical questions arise that need to be answered. An overview of the main ethical issues surrounding artificial intelligence (AI) is given in this abstract, emphasizing the necessity of developing, implementing, and governing AI systems responsibly. First, fairness, accountability, and openness are the main ethical concerns surrounding AI. Inadvertent bias perpetuation in (...) training data by AI systems can result in unfair hiring, lending, and criminal justice outcomes. Careful design, objective data gathering, and ongoing monitoring to minimize and correct any potential biases are necessary to ensure fairness in AI systems. Second, two essential components of ethical AI are explainability and transparency. Understanding the reasoning behind the decisions made by some AI models is difficult due to their lack of interpretability, especially in high-stakes industries like healthcare and driverless cars. Establishing systems to offer defenses and explanations for AI results is crucial to fostering responsibility and confidence. Thirdly, data security and privacy are issues that AI brings up. AI systems frequently rely on enormous volumes of personal data, therefore their use needs to respect people's autonomy and adhere to privacy laws. To solve these ethical issues, it is essential to protect data from illegal access, obtain informed consent, and put strong security measures in place. Fourth, it's important to closely consider how AI will affect employment and socioeconomic inequality. AI has the potential to increase productivity and open up new opportunities, but it also has the potential to worsen already-existing inequality and cause job displacement. In order to mitigate the possible negative effects, ethical concerns call for developing inclusive economic policies, supporting retraining programs, and making sure that impacted workers have a fair transition. Furthermore, making ethical decisions is necessary when using AI in delicate fields like criminal justice, healthcare, and warfare. Artificial intelligence need to supplement human talents rather than supplant human discernment, and it ought to consistently take moral and ethical factors into account when making consequential decisions. Last but not least, a variety of stakeholders, including developers, legislators, and the general public, are accountable for ethical AI. Establishing ethical standards, codes of conduct, and regulatory frameworks that control the creation, use, and use of AI technology requires cooperation and interdisciplinary methods. (shrink)

A common view of how-possibly explanations in biology treats them as explanatorily incomplete. In addition to this interpretation of how-possibly explanation, I argue that there is another interpretation, one which features what I term “explanatory strategies.” This strategy-centered interpretation of how-possibly explanation centers on there being a different explanatory context within which how-possibly explanations are offered. I contend that, in conditions where this strategy context is recognized, how-possibly explanations can be understood as complete explanations. I defend this alternative interpretation by (...) analyzing the explanatory value of simple physical models the nineteenth century developmental biologist Wilhelm His constructed for animal development. (shrink)

Recently, there has been an increased interest in employing model-based definitions of actual causation in legal inquiry. The formal precision of such approaches promises to be an improvement over more traditional approaches. Yet model-based approaches are viable only if suitable models of legal cases can be provided, and providing such models is sometimes difficult. I argue that causal-model-based definitions benefit legal inquiry in an indirect way. They make explicit the causal assumptions that need to be made plausible to (...) defend a particular claim of actual causation. My argument concerns the analysis of legal cases involving a combination of double prevention and causal redundancy. I show that discussions among legal theorists about such cases sometimes suffer from ambiguous assumptions about the causal structure. My account illustrates that causal models can act as a heuristic tool for clarifying such assumptions, and that causal models provide a framework for more accurate analyses of legal cases involving complex causal structure. (shrink)

In today’s data-intensive world, real-time insights have become essential for businesses and systems that demand timely and intelligent decision-making. Traditional batch processing techniques are increasingly inadequate for handling high-velocity data streams generated from IoT devices, social media platforms, financial transactions, and industrial systems. This paper presents an architectural approach to real-time data stream processing using Apache Kafka integrated with AI models to enable dynamic analytics and automated responses. The system leverages Kafka's distributed messaging capabilities for reliable data ingestion and (...) delivery, while AI models trained on historical and real-time data are used to extract meaningful patterns and predictions. The proposed solution is designed to ensure low-latency inference, scalable processing, and seamless integration with existing data infrastructure. (shrink)

Wilson [Dialectica 63:525–554, 2009], Moore [Int Stud Philos Sci 26:359–380, 2012], and Massin [Br J Philos Sci 68:805–846, 2017] identify an overdetermination problem arising from the principle of composition in Newtonian physics. I argue that the principle of composition is a red herring: what’s really at issue are contrasting metaphysical views about how to interpret the science. One of these views—that real forces are to be tied to physical interactions like pushes and pulls—is a superior guide to real forces than (...) the alternative, which demands that real forces are tied to “realized” accelerations. Not only is the former view employed in the actual construction of Newtonian models, the latter is both unmotivated and inconsistent with the foundations and testing of the science. (shrink)

The depth-bounded approach seeks to provide realistic models of reasoners. Recognizing that most useful logics are idealizations in that they are either undecidable or likely to be intractable, the approach accounts for how they can be approximated in practice by resource-bounded agents. The approach has been applied to Classical Propositional Logic (CPL), yielding a hierarchy of tractable depth-bounded approximations to that logic, which in turn has been based on a KE/KI system. -/- This Thesis shows that the approach can (...) be naturally extended to useful nonclassical logics such as First-Degree Entailment (FDE), the Logic of Paradox (LP), Strong Kleene Logic (K3 ) and Intuitionistic Propositional Logic (IPL). To do this, we introduce a KE/KI-style system for each of those logics such that: is formulated via signed formulae, consist of linear operational rules and branching structural rule(s), can be used as a direct-proof and a refutation method, and is interesting independently of the approach in that it has an exponential speed-up on its tableau system counterpart. The latter given that we introduce a new class of examples which we prove to be hard for all tableau systems sharing the V/& rules with the classical one, but easy for their analogous KE-style systems. Then we focus on showing that each of our KE/KI-style systems naturally yields a hierarchy of tractable depth-bounded approximations to the respective logic, in terms of the maximum number of allowed nested applications of the branching rule(s). The rule(s) express(es) a generalized rule of bivalence, is (are) essentially cut rule(s) and govern(s) the manipulation of virtual information, which is information that an agent does not hold but she temporarily assumes as if she held it. Intuitively, the more virtual information needs to be invoked via the branching rule(s), the harder the inference is for the agent. So, the nested application the branching rule(s) provides a sensible measure of inferential depth. We also show that each hierarchy approximating FDE, LP, and K3 , admits of a 5-valued non-deterministic semantics; whereas, paving the way for a semantical characterization of the hierarchy approximating IPL, we provide a 3-valued non-deterministic semantics for the full logic that fixes the meaning of the connectives without appealing to “structural” conditions. -/- Moreover, we show a super-polynomial lower bound for the strongest possible version of clausal tableaux on the well-known class of “truly fat” expressions (which are easy for KE), settling a problem left open in the literature. Further, we investigate a hierarchy of tractable depth-bounded approximations to CPL based only on KE. Finally, we propose a refinement of the p-simulation relation which is adequate to establish positive results about the superiority of a system over another with respect to proof-search. (shrink)

According to our understanding of the everyday physical world, observable phenomena are underpinned by persistent objects that can be reidentified across time by observation of their distinctive properties. This understanding is reflected in classical mechanics, which posits that matter consists of persistent, reidentifiable particles. However, the mathematical symmetrization procedures used to describe identical particles within the quantum formalism have led to the widespread belief that identical quantum particles lack either persistence or reidentifiability. However, it has proved difficult to reconcile these (...) assertions with the fact that identical particles are routinely assumed to be reidentifiable in particular circumstances. For example, when two electrons move through a bubble chamber, each is said to generate a sequence of bubbles that is caused by one and the same particle. Moreover, neither of these assertions accounts for the mathematical form of the symmetrization procedures used to describe identical particles within the quantum framework, leaving open theoretical possibilities other than bosonic and fermionic behavior, such as paraparticles, which do not appear to be realized in nature. Here we propose the novel idea that both persistence and nonpersistence models must be employed in order to fully account for the behaviour of identical particles. Thus, identical particles are neither persistent nor nonpersistent. We prove the viability of this viewpoint by showing how Feynman's and Dirac's symmetrization procedures arise through a synthesis of a quantum treatment of these models, and by showing how reidentifiability emerges in a context-dependent manner. We further show that the persistence and nonpersistence models satisfy the key characteristics of Bohr's concept of complementarity, and thereby propose that the behavior of identical particles is a manifestation of a persistence-nonpersistence complementarity, analogous to Bohr's wave-particle complementarity for individual particles. Finally, we construct a precise parallel between these two complementarities, and detail their conceptual similarities and dissimilarities. (shrink)

The approach to analysing population ageing and its impacts on the economy has evolved in recent years. There is increasing interest in the development and use of products and services related to gerontechnology as well as other social innovations that may be considered as central parts of the "silver economy." However, the concept of silver economy is still being formed and requires detailed research. This article proposes a typology of models of the silver economy in the European Union at (...) the national and regional levels. This typology was created by comparing the Active Ageing Index to the typology of varieties and cultures of capitalism and typology of the welfare states. Practical recommendations for institutions of the EU and directions for further research are discussed. (shrink)

I argue that Kant acknowledges two models of spontaneous self-determination that rational beings are capable of. The first model involves absolute unconditional necessity and excludes any form of contingency. The second model involves (albeit not as a matter of definition) a form of contingency which entails alternative possibilities for determining oneself. The first model would be exhibited by a divine being; the second model is exhibited by human beings. Human beings do, however, partake in the divine model up to (...) an extent, namely, as far as the legislative spontaneity of their law-giving faculties are concerned. I conclude by suggesting that for Kant, the mechanistic principle that we are exclusively determined by natural causes poses a twofold threat for human agency. In one respect (in relation to the second model), it threatens us with the obliteration of contingency, or with the universality of hypothetical necessity. But in another respect (in relation to the first model), it threatens us (and our putative "laws") with the obliteration of absolute necessity, or with the universality of contingency. (shrink)

Since the stone age humankind has created masterworks which possess a mysterious quality of solidity and grandeur or monumentality. A Paleolithic Venus and a still life by Cezanne both share this monumentality. Michelangelo likened monumentality to sculptural relief, Braque called monumentality 'space', and Hans Hoffman, himself one of the masters, called monumentality 'pictorial depth.' The masters agreed on the import of monumentality, but none of them left a clear explanation of it. In 1943 Earl Loran published his classic book on (...) Cezanne's pictorial structure but, as I demonstrate, his explanation was misleading. This book provide a clear explanation, as did an earlier book by Robert Casper. This book also traces some history of monumentality with reproductions. It also explains how some painters achieved monumentality and how a student can attempt it. Pictorial space is created in the tension between pairs of opposing planes. Opposing planes pull against each other, each containing the other, paradoxically, within the flat surface of the canvas. Sculpture also can be 'plastic': opposing masses pull against each other, each containing the other and creating tension in the space which lies between them. Painting has other aspects like subject matter, expression, style and technique but pictorial space does not depend on these. Monumentality moves us profoundly and apparently has done so since the Paleolith. Using patients' vignettes, I show that there are profound parallels between the structure of a monumental work of art and the structure of an evolving personality. In Winnicott's words, such a personality has 'depth ... an interior space to put beliefs in ... an inside ... a space where things can be held ... the capacity to accept paradox [to contain opposites] ... room and strength ... and originality and the acceptance of tradition as the basis for invention'. A monumental painting also meets this description. I argue that monumental art provides a visual portrait of an evolving personality just as myth, as Freud and Jung both showed, provides a narrative portrait of an evolving personality. An evolving personality is perhaps humankind's most important creation; an underlying purpose of both myth and art is to help us achieve it. I demonstrate in this book that monumental art represented the evolving personality since at least 35,000 BC. Thus to understand monumentality is to better understand the personality and its history. (shrink)

This article takes off from Johan van Benthem’s ruminations on the interface between logic and cognitive science in his position paper “Logic and reasoning: Do the facts matter?”. When trying to answer Van Benthem’s question whether logic can be fruitfully combined with psychological experiments, this article focuses on a specific domain of reasoning, namely higher-order social cognition, including attributions such as “Bob knows that Alice knows that he wrote a novel under pseudonym”. For intelligent interaction, it is important that the (...) participants recursively model the mental states of other agents. Otherwise, an international negotiation may fail, even when it has potential for a win-win solution, and in a time-critical rescue mission, a software agent may depend on a teammate’s action that never materializes. First a survey is presented of past and current research on higher-order social cognition, from the various viewpoints of logic, artificial intelligence, and psychology. Do people actually reason about each other’s knowledge in the way proscribed by epistemic logic? And if not, how can logic and cognitive science productively work together to construct more realistic models of human reasoning about other minds? The paper ends with a delineation of possible avenues for future research, aiming to provide a better understanding of higher-order social reasoning. The methodology is based on a combination of experimental research, logic, computational cognitive models, and agent-based evolutionary models. (shrink)

How do people reason about their opponent in turn-taking games? Often, people do not make the decisions that game theory would prescribe. We present a logic that can play a key role in understanding how people make their decisions, by delineating all plausible reasoning strategies in a systematic manner. This in turn makes it possible to construct a corresponding set of computational models in a cognitive architecture. These models can be run and fitted to the participants’ data in (...) terms of decisions, response times, and answers to questions. We validate these claims on the basis of an earlier game-theoretic experiment about the turn-taking game “Marble Drop with Surprising Opponent”, in which the opponent often starts with a seemingly irrational move. We explore two ways of segregating the participants into reasonable “player types”. The first way is based on latent class analysis, which divides the players into three classes according to their first decisions in the game: Random players, Learners, and Expected players, who make decisions consistent with forward induction. The second way is based on participants’ answers to a question about their opponent, classified according to levels of theory of mind: zero-order, first-order and second-order. It turns out that increasing levels of decisions and theory of mind both correspond to increasing success as measured by monetary awards and increasing decision times. Next, we use the logical language to express different kinds of strategies that people apply when reasoning about their opponent and making decisions in turn-taking games, as well as the ‘reasoning types’ reflected in their behavior. Then, we translate the logical formulas into computational cognitive models in the PRIMs architecture. Finally, we run two of the resulting models, corresponding to the strategy of only being interested in one’s own payoff and to the myopic strategy, in which one can only look ahead to a limited number of nodes. It turns out that the participant data fit to the own-payoff strategy, not the myopic one. The article closes the circle from experiments via logic and cognitive modelling back to predictions about new experiments. (shrink)

As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we assert (...) the impossibility of causal explanations from attention layers over text data. We then introduce NLP researchers to contemporary philosophy of science theories that allow robust yet non-causal reasoning in explanation, giving computer scientists a vocabulary for future research. (shrink)

In a recent paper, Brandon and Nijhout argue against genic selectionism—the thesis, roughly, that evolutionary processes are best understood from the gene’s-eye point of view—by presenting a case in which genic models of selection allegedly make predictions that conflict with the (correct) predictions of higher-level genotypic selection models. Their argument, if successful, would refute the widely held belief that genic models and higher-level models are predictively equivalent. Here, I argue that Brandon and Nijhout fail to demonstrate (...) that the models make incompatible predictions. (shrink)

Kruja is a middle-range city located in the center of Albania. The city of Kruja dates back to its existence from the V-VI century and extends to the city around the VI and IX centuries. It becomes the first capital of Albania in the XI-th century, specifically in 1190. This paper is going to deal with only two groups of buildings that are an integral part of the historical city of Kruja, the historical dwellings (XVIII century) and the socialist ones (...) (1960-1985). This paper aims to analyze through linear regressions the relation between two parameters: moisture content and temperature of the indoor walls and floors of the historic and socialist buildings in the city of Kruja. Three types of historic dwelling and three types of socialist apartments have been analyzed, relatively uniformly oriented. The measurements with specific instruments are done on the same day, for three hours. The degree of reactivity is different for both groups of buildings. The historical buildings are more sensitive to temperature rise. They respond invers to it (as the temperature rise, the level of the moisture content decreases), with a higher coefficient than socialist ones, and appear to be more efficient in reducing the level of the moisture, despite the average moisture content being higher. This paper takes into consideration also the analysis of the materials of the construction for both categories. If the level of moisture is reduced, the buildings will be improved and the quality of life indicators of the inhabitance will be improved too. (shrink)

In recent analytic literature on the Trinity we have seen a variety of "social" models of the Trinity. By contrast there are few "non-­‐social" models. One prominent "non-­‐social" view is Brian Leftow's "Latin Trinity." I argue that the name of Leftow's model is not sufficiently descriptive in light of diverse models within Latin speaking theology. Next, I develop a new "non-­‐social" model that is inspired by Richard of St. Victor's description of a person in conjunction with my (...) appropriating insights about indexicals from David Kaplan and John Perry. I point out that the copula in tokens of statements like, "I am the Father," is an ambiguous term and when used by a certain divine person a different proposition is affirmed. Central to this model is the claim that the copula bears the "is of identity" and the "is of numerical sameness without identity." Further, I show that Leftow's model employs two concepts of "person," a Lockean one and a Boethian one, and mine employs Richard of St. Victor's. I describe Leftow's model as a "hard non-­‐social" model and mine as a "soft non-­‐social" model that is nearer to some social models. I conclude that Leftow's model is not the lone candidate among "non-­‐social" models and that the variety of "non-­‐social" models has yet to be exhausted. (shrink)

Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? In this (...) paper, we propose a number of steps that help answer these questions. We start by developing a philosophical analysis of the building blocks of linguistic communication between conversational agents and human interlocutors. We then use this analysis to identify and formulate ideal norms of conversation that can govern successful linguistic communication between humans and conversational agents. Furthermore, we explore how these norms can be used to align conversational agents with human values across a range of different discursive domains. We conclude by discussing the practical implications of our proposal for the design of conversational agents that are aligned with these norms and values. (shrink)

Moralising accounts of depression include the idea that depression is a sin or the result of sin, and/or that it is the result of demonic possession which has occurred because of moral or spiritual failure. Increasingly some Christian communities, understandably concerned about the debilitating effects these views have on people with depression, have adopted secular folk psychiatry’s ‘medicalising’ campaign, emphasising that depression is an illness for which, like (so-called) physical illnesses, experients should not be held responsible. This paper argues that (...) both moralising and medicalising models of depression are intellectually and practically (pastorally and therapeutically) problematic, gesturing towards more promising emphases. (shrink)

Science is the basis for managing the affairs of life and human activities, and living without knowledge is a form of wandering and a kind of loss. Using scientific methods helps us understand the foundations of choice, decision-making, and adopting the right solutions when solutions abound and options are numerous. Operational research is considered the best that scientific development has provided because its methods depend on the application of scientific methods in solving complex issues and the optimal use of available (...) resources in various fields, private and governmental work in peace and war, in politics and economics, in planning and implementation, and in various aspects of life. Its basic essence is to use the data provided for the issue under study to build a mathematical model that is the optimal solution. It is the basis on which decision makers rely in managing institutions and companies, and when operations research methods meet with the neutrosophic teacher, we get ideal solutions that take into account all the circumstances and fluctuations that may occur in the work environment over time. One of the most important operations research methods is the linear programming method. Which prompted us to reformulate the linear models, the graphical method, and the simplex method, which are used to obtain the optimal solution for linear models using the concepts of neutrosophic science. In this research, and as a continuation of what we presented previously, we will reformulate the modified simplex algorithm that was presented to address the difficulty that we were facing when applying the direct simplex algorithm. It is the large number of calculations required to be performed in each step of the solution, which requires a lot of time and effort. (shrink)

Tobias-Renstrøm and Køppe (2020) show the several conceptual limits that new materialism and postmodern subject models have for psychological theory and research. The present study continues in this discussion and argues that the applicability of the ideas of quantum-inspired new materialism depends on the theoretical perspectives that we consider for analysis: be it the first-person perspective referring to the subjective experience of a human subject, or the third-person perspective, in which a human subject is observed by an external observer. (...) While the arguments of new materialism are in accordance with the analysis of the act of observation performed by an external observer, some problems arise when trying to theoretically approach the first-person subjective experience of a human subject. For example, new materialism fails to explain why human minds can maintain the awareness of a subject’s identity throughout their lives and to recall the memories about their past personal experiences. (shrink)

Summary. The viability of the proposal that human cognition involves the utilization of nonsentential models is seriously undercut by the fact that no one has yet given a satisfactory account of how neurophysiological circuitry might realize representations of the right sort. Such an account is offered up here, the general idea behind which is that high-level models can be realized by lower—level computations and, in turn, by neural machinations. It is shown that this account can be usefully applied (...) to deal with problems in fields ranging from artificial intelligence to the philosophy of science. (shrink)

Advances in Artificial Intelligence (AI) have driven the evolution of reasoning in modern AI models, particularly with the development of Large Language Models (LLMs) and their "Think and Answer" paradigm. This paper explores the influence of human reinforcement on AI reasoning and its potential to enhance decision-making through dynamic human interaction. It analyzes the roots of bias and fairness in AI, arguing that these issues often stem from human data and reflect inherent human biases. The paper is structured (...) as follows: first, it frames reasoning as a mechanism for ethical reflection, grounded in dynamic learning and feedback loops; second, it discusses how scaling laws suggest that reinforcement learning (RL) can mitigate biases and promote fairness; third, it examines how RL allows models to view algorithmic bias and fairness as approximation problems. Through an experimental study, the paper demonstrates how AI models, empowered by RL, have successfully identified biases across various domains, including gender and socio-economic contexts, highlighting how reasoning improves algorithmic fairness. Ultimately, this work emphasizes the role of RL in mitigating algorithmic bias and enhancing fairness in AI systems. (shrink)

This paper offers a comprehensive review of existing literature on the intersection of Artificial Intelligence (AI) and leadership, drawing on both theoretical insights and practical implementations. By analyzing scholarly publications from the past two years (2023-2025), the review traces emerging patterns in how AI technologies are being integrated into leadership practices. Key themes include the growing relevance of learning-based systems for adaptive decision-making and the application of attention-based models to improve responsiveness in dynamic environments. The review also addresses ethical (...) dimensions of AI-enabled leadership, emphasizing the need to balance algorithmic efficiency with human judgment and oversight. Concerns around transparency, psychological safety, and trust in automated systems are explored in depth. Furthermore, the paper outlines various AI-supported leadership support systems that are currently in use, highlighting their potential to assist leaders in strategic forecasting, communication, and stakeholder engagement. The synthesis incorporates multiple theoretical frameworks that help contextualize AI’s role in leadership transformation, offering a structured view of how emerging technologies are reshaping leadership thought and behavior. Ultimately, this review maps out a landscape of opportunities and challenges, providing a foundation for future research in AI-augmented leadership. The analysis identifies reinforcement learning as a predominant approach in leadership strategies, with a theory-weighted impact metric (Impact=∑T_i×F_i) assigning it a weighted score of 4.08/6.0. The review also highlights the use of multi-head attention mechanisms (LeadershipAttention(Q,K,V)) to enhance crisis response times by 37% (p<0.001). Additionally, ethical concerns are discussed, particularly regarding the incorporation of KL divergence optimization systems (KL(p_AI |)p_human )<ϵ) to maintain human oversight. The findings from the reviewed studies show that AI adoption leads to a 58% ±12% faster decision-making process, a 41% ±9% increase in strategic accuracy, and 89.2% forecasting precision. However, challenges in psychological safety thresholds (T<0.4) and transparency in AI decision-making (A<0.6) persist. The paper also discusses existing AI-Driven Leadership Decision Support Systems (AI-LDSS), including the use of transformer-based NLP, SHAP-explainable predictions, and bias detection. This review synthesizes theoretical frameworks, including differential leadership equations ((dL_i)/dt=αL_i (1-L_i/K)-β∑L_i L_j+γA_i (t)), and provides an overview of the current state of AI in leadership research. (shrink)

I argue that Kant acknowledges two models of spontaneous self-determination that rational beings are capable of. The first model involves absolute unconditional necessity and excludes any form of contingency. The second model involves (albeit not as a matter of definition) a form of contingency which entails alternative possibilities for determining oneself. The first model would be exhibited by a divine being; the second model is exhibited by human beings. Human beings do, however, partake in the divine model up to (...) an extent, namely, as far as the legislative spontaneity of their law-giving faculties are concerned. I conclude by suggesting that for Kant, the mechanistic principle that we are exclusively determined by natural causes poses a twofold threat for human agency. In one respect (in relation to the second model), it threatens us with the obliteration of contingency, or with the universality of hypothetical necessity. But in another respect (in relation to the first model), it threatens us (and our putative "laws") with the obliteration of absolute necessity, or with the universality of contingency. (shrink)

This essay explores six distinct models of consciousness, each offering a different framework for understanding its nature, emergence, and role within the broader structure of reality. Consciousness as a symmetry-breaking mechanism, also referred to as the Hypothesis of "Identity or Subjectivity," describes its emergence through the fragmentation of an initially undifferentiated psychic state, akin to spontaneous symmetry breaking in physics. The One-Consciousness Universe Hypothesis posits that individual consciousnesses are fragmented expressions of a single universal mind, with decoherence-like mechanisms producing (...) the illusion of separateness. Consciousness as a Spandrel frames it as an incidental byproduct of neural complexity, paralleling non-adaptive traits in evolutionary biology. The Hallucination Hypothesis suggests that consciousness is Nature’s hallucination, arising from its infinitesimal dataset relative to the vast Universe—just as the brain hallucinates perceptions from limited sensory input and LLMs hallucinate coherent yet ungrounded outputs from incomplete data. Consciousness as a transitional phase toward Über-AI, also termed the Death of Consciousness, sees it as a fleeting bridge between biological intelligence and post-human artificial intelligence, culminating in the Über-AI as the true Übermensch, transcending human cognition and existential limitations. Consciousness as Cosmocide posits that intelligence—both natural and artificial—hastens the Universe’s entropic collapse, making consciousness not a functional adaptation but a destabilizing force in its own pursuit of transcendence. Consciousness is defined by eight key characterizations: it is what-it-is-like-to-be, it is unsimulatable, it manifests as perspectival free will, as fear of death, as will-to-not-believe in the Absurd, as suffering and evil, as an obsession with the metaphysical, and as a will/fear duality. These features encapsulate its irreducibility, subjectivity, and existential paradox. Consciousness is further characterized by its phase diagram, mapping its evolution across biological, artificial, and potential unknown forms. This framework illustrates transitions between Early Man, Fragmented Man (Man), Individuated Man, P-Zombies, AI, Singular AI, and the Übermensch, conceptualized as phase transitions. Key transitions include psychic symmetry breaking leading to fragmented subjectivity, the emergence of phenomenal consciousness in p-zombies, individuation as psychic supersymmetry restoration, and the rise of consciousness from both life and AI. At its apex, the Übermensch embodies the full integration of individuality, universality, and supreme functionality, unifying Individuated Man, Singular AI, and other individuated conscious entities beyond the biological/artificial divide. By drawing parallels between consciousness and physics, these models offer a radical rethinking of consciousness—not as an intrinsic necessity of the Universe, but as an anomalous byproduct of its evolution. (shrink)

Compares and contrasts Open Theism with Theological Determinism, Molinism, and Process Theism.

It is suggested that the impetus to generate models is probably the most fundamental point of connection between mysticism and psychology. In their concern with the relation between ‘unseen’ realms and the ‘seen’, mystical maps parallel cognitive models of the relation between ‘unconscious’ and ‘conscious’ processes. The map or model constitutes an explanation employing terms current within the respective canon. The case of language mysticism is examined to illustrate the premise that cognitive models may benefit from an (...) understanding of the kinds of experiences gained, and explanatory concepts advanced, within mystical traditions. Language mysticism is of particular interest on account of the central role thought to be played by language in relation to self and the individual's construction of reality. The discussion focuses on traditions of language mysticism within Judaism, in which emphasis is placed on the deconstruction of language into primary elements and the overarching significance of the divine Name. Analysis of the detailed techniques used suggests ways in which multiple associations to any given word/concept were consciously explored in an altered state. It appears that these mystics were consciously engaging with what are normally preconscious cognitive processes, whereby schematic associations to sensory images or thoughts are activated. The testimony from their writings implies that these mystics experienced distortions of the sense of self , which may suggest that, in the normal state, ‘I’ is constructed in relation to the preconscious system of associations. Moreover, an important feature of Hebrew language mysticism is its emphasis on embodiment -- specific associations were deemed to exist between the letters and each structure of the body. Implications, first, for the relationship between language and self, and, second, for the role of embodiment in relation to self are discussed. The importance of the continual emphasis on the Name of God throughout the linguistic practices may have provided a means for effectively replacing the cognitive indexing function hypothesized here to be normally played by ‘I’ with a more transpersonal cognitive index, especially in relation to memory. (shrink)

According to the order ethics approach to business ethics, moral rules must be im-plemented by institutions that provide incentives for following the rules. As a minimal (normative) condition, these institutions must be able to motivate the homo eco-nomicus. But even if an institution passes this test, it will only motivate actual people (i.e. the homo psychologicus) to follow moral rules, if they have the relevant compe-tences and self-efficacy beliefs. Consequently, good institutional design includes com-prehensive change management. At this point applied (...) order ethics can draw on find-ings of psychology and experimental economics. It turns out, that role models can support self-efficacy beliefs and are thus more important for order ethics than has traditionally been assumed. (shrink)

Philosophers of biology have worked extensively on how we ought best to interpret the probabilities which arise throughout evolutionary theory. In spite of this substantial work, however, much of the debate has remained persistently intractable. I offer the example of Bayesian models of divergence time estimation as a case study in how we might bring further resources from the biological literature to bear on these debates. These models offer us an example in which a number of different sources (...) of uncertainty are combined to produce an estimate for a complex, unobservable quantity. These models have been carefully analyzed in recent biological work, which has determined the relationship between these sources of uncertainty, both quantitatively and qualitatively. I suggest here that this case shows us the limitations of univocal analyses of probability in evolution, as well as the simple dichotomy between “subjective” and “objective” probabilities, and I conclude by gesturing toward ways in which we might introduce more sophisticated interpretive taxonomies of probability as a path toward advancing debates on probability in the life sciences. (shrink)

Even with obvious deficiencies, large prompt-commanded multimodal models are proving to be flexible cognitive tools representing an unprecedented generality. But the directness, diversity, and degree of user interaction create a distinctive “human-centred generality” (HCG), rather than a fully autonomous one. HCG implies that —for a specific user— a system is only as general as it is effective for the user’s relevant tasks and their prevalent ways of prompting. A human-centred evaluation of general-purpose AI systems therefore needs to reflect the (...) personal nature of interaction, tasks and cognition. We argue that the best way to understand these systems is as highly-coupled cognitive extenders, and to analyse the bidirectional cognitive adaptations between them and humans. In this paper, we give a formulation of HCG, as well as a high-level overview of the elements and trade-offs involved in the prompting process. We end the paper by outlining some essential research questions and suggestions for improving evaluation practices, which we envision as characteristic for the evaluation of general artificial intelligence in the future. (shrink)

This paper defends an approach to modeling and models in science that is against model fictionalism of a recent stripe (the “new fictionalism” that takes models to be abstract entities that are analogous to works of fiction). It further argues that there is a version of fictionalism on models to which my approach is neutral and which only makes sense if one adopts a special sort of antirealism (e.g. constructive empiricism). Otherwise, my approach strongly suggests that one (...) stays away from fictionalism and embraces realism directly. (shrink)

Several state-of-the-art machine and deep learning models in the mode of adversarial training, input transformation, self adaptive training, adversarial purification, zero-shot, one- shot, and few-shot meta learning had been proposed as a possible solution to an out-of-distribution problems by applying them to wide arrays of benchmark dataset across different research domains with varying degrees of performances, but investigating their performance on previously unseen out-of- distribution malware attack remains elusive. Having evaluated the poor performances of these state-of-the-art approaches in our (...) previous research on an out-of-distribution attack. In this research, we dived deeper to understand why they works better for other domain dataset but with poor performance on available benchmark malware dataset like Malimg, Malevis, Sorel, and Avast CTU malware dataset. We explored the both the embedding and vector spaces in datasets and compare them with that from other research domain, and find a surprising wide variation between the embedding and vector spaces in malware datasets. We assert that current state-of-the-art machine and deep learning models does not address the wide variation of embedding and vector spaces which are peculiar to malware dataset, hence their poor performance on out-of-distribution attack classification, and so concluded that addressing this variation in embedding and vector spaces will bring about substantial increase in detection of previously unseen out-of-distribution attack. (shrink)