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)

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)

Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an (...) internal opacity problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes. (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)

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)

In this conceptual paper, the traditional conceptualization of sustainable entrepreneurship is challenged because of a fundamental tension between processes involved in sustainable development and processes involved in entrepreneurship: the concept of sustainable business models contains a paradox, because sustainability involves the reduction of information asymmetries, whereas entrepreneurship involves enhanced and secured levels of information asymmetries. We therefore propose a new and integrated theory of sustainable entrepreneurship that overcomes this paradox. The basic argument is that environmental problems have to be (...) conceptualized as wicked problems or sustainability-related ecosystem failures. Because all actors involved in the entrepreneurial process are characterized by their epistemic insufficiency regarding the solving of these problems, the role of information in the sustainable entrepreneurial process changes. On the one hand, the reduction of information asymmetries primarily aims to enable actors to become critical of sustainable entrepreneurs’ actual business models. On the other hand, the epistemic insufficiency of sustainable entrepreneurs guarantees that information asymmetries remain as a source of new sustainable business opportunities. Three further characteristics of sustainable entrepreneurs are distinguished: sustainability and entrepreneurship-related risk-taking; sustainability and entrepreneurship-related self-efficacy; and the development of satisficing and open-ended solutions, together with multiple stakeholders. (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)

Stanford’s argument against scientific realism focuses on theories, just as many earlier arguments from inconceivability have. However, there are possible arguments against scientific realism involving unconceived (or inconceivable) entities of different types: observations, models, predictions, explanations, methods, instruments, experiments, and values. This paper charts such arguments. In combination, they present the strongest challenge yet to scientific realism.

Several philosophers of science claim that scientific toy models afford knowledge of possibility, but answers to the question of why toy models can be expected to competently play this role are scarce. The main line of reply is that toy models support possibility claims insofar as they are credible. I raise a challenge for this credibility-thesis, drawing on a familiar problem for imagination-based modal epistemologies, and argue that it remains unanswered in the current literature. The credibility-thesis has (...) a long way to go if it is to account for the epistemic merits of toy models. (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)

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...

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 rapid rise of generative AI, including LLMs, has prompted a great deal of concern, both within and beyond academia. One of these concerns is that generative models embed, reproduce, and therein potentially perpetuate all manner of bias. The present study offers an alternative perspective: exploring the potential of LLMs to detect bias in human generated text. Our target is genetic essentialism in obesity discourse in Australian print media. We develop and deploy an LLM-based classification model to evaluate a (...) large sample of relevant articles (n=26,163). We show that our model detects genetic essentialist biases as reliably as human experts; and find that, while genes figure less prominently in popular discussions of obesity than previous work might suggest, when genetic information is invoked, it is often presented in a biased way. Implications for future work are discussed. (shrink)

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)

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)

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)

Chemistry and physics are two sciences that are hard to connect. Yet there is significant overlap in their aims, methods, and theoretical approaches. In this book, the reduction of chemistry to physics is defended from the viewpoint of a naturalised Nagelian reduction, which is based on a close reading of Nagel's original text. This naturalised notion of reduction is capable of characterising the inter-theory relationships between theories of chemistry and theories of physics. The reconsideration of reduction also leads to a (...) new characterisation of chemical theories. This book is primarily aimed at philosophers of chemistry and chemists with an interest in philosophy, but is also of interest to the general philosopher of science. (shrink)

Political theory as a discipline has long been skeptical of computational methods. In this paper, I argue that it is time for theory to make a perspectival shift on these methods. Specifically, we should consider integrating recently developed generative large language models like GPT-4 as tools to support our creative work as theorists. Ultimately, I suggest that political theorists should embrace this technology as a method of supporting our capacity for creativity—but that we should do so in a way (...) that is mindful of the content and value of theorizing, the technical constraints of the models, and the ethical questions that the technology raises. (shrink)

When does opinion formation within an interacting group lead to consensus, polarization or fragmentation? The article investigates various models for the dynamics of continuous opinions by analytical methods as well as by computer simulations. Section 2 develops within a unified framework the classical model of consensus formation, the variant of this model due to Friedkin and Johnsen, a time-dependent version and a nonlinear version with bounded confidence of the agents. Section 3 presents for all these models major analytical (...) results. Section 4 gives an extensive exploration of the nonlinear model with bounded confidence by a series of computer simulations. An appendix supplies needed mathematical definitions, tools, and theorems. (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)

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)

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)

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)

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)

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)

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)

One striking observation in the history of rational choice models is that those models have not only been used in economics but spread widely across the social and behavioral sciences. How do such model transfers proceed? By closely studying the early efforts to transfer such models by William Riker – a major protagonist in pushing the adoption of game theoretic models in political science – this article examines the transfer process as one of ‘translation’ by which (...) abstract and mathematical rational choice models were constructed and modified such that they applied to a specific target system in a new domain. In this paper, we argue that to overcome a set of challenges that hampered the straightforward transfer of game theoretic models into political science, Riker complemented theoretical and conceptual modifications of von Neumann and Morgenstern’s game schemes with the use of narratives to turn them into applicable and testable models. As such, those narratives played a crucial role in enabling their transfer and ultimately facilitated the applicability of game theoretic models in political science. (shrink)

Product structures are represented in engineering models by depicting and linking components, features and assemblies. Their understanding requires knowledge of both design and manufacturing practices, and yet further contextual reasoning is needed to read them correctly. Since these representations are essen- tial to the engineering activities, the lack of a clear and explicit semantics of these models hampers the use of information systems for their assessment and exploita- tion. We study this problem by identifying different interpretations of structure (...) rep- resentations, and then discuss the formal properties that a suitable language needs for representing components, features and combinations of these. We show that the representation of components and features require a non-standard mereology. (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)

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)

Biomolecules and particularly proteins and DNA exhibit some mysterious features that cannot find satisfactory explanation by quantum mechanical modes of atoms. One of them, known as a Levinthal’s paradox, is the ability to preserve their complex three-dimensional structure in appropriate environments. Another one is that they possess some unknown energy mechanism. The Basic Structures of Matter Supergravitation Unified Theory (BSM-SG) allows uncovering the real physical structures of the elementary particles and their spatial arrangement in atomic nuclei. The resulting physical (...) class='Hi'>models of the atoms are characterized by the same interaction energies as the quantum mechanical models, while the structure of the elementary particles influence their spatial arrangement in the nuclei. The resulting atomic models with fully identifiable parameters and angular positions of the quantum orbits permit studying the physical conditions behind the structural and bonding restrictions of the atoms connected in molecules. A new method for a theoretical analysis of biomolecules is proposed. The analysis of a DNA molecule leads to formulation of hypotheses about the energy storage mechanism in DNA and its role in the cell cycle synchronization. This permits shedding a light on the DNA feature known as a C-value paradox. The analysis of a tRNA molecule leads to formulation of a hypothesis about a binary decoding mechanism behind the 20 flavors of the complex aminoacyle-tRNA synthetases - tRNA, known as a paradox. (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)

This article presents important properties of standard discrete distributions and its conjugate densities. The Bernoulli and Poisson processes are described as generators of such discrete models. A characterization of distributions by mixtures is also introduced. This article adopts a novel singular notation and representation. Singular representations are unusual in statistical texts. Nevertheless, the singular notation makes it simpler to extend and generalize theoretical results and greatly facilitates numerical and computational implementation.

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)

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)

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)

The epistemology of risk examines how risks bear on epistemic properties. A common framework for examining the epistemology of risk holds that strength of evidential support is best modelled as numerical probability given the available evidence. In this essay I develop and motivate a rival ‘relevant alternatives’ framework for theorising about the epistemology of risk. I describe three loci for thinking about the epistemology of risk. The first locus concerns consequences of relying on a belief for action, where those consequences (...) are significant if the belief is false. The second locus concerns whether beliefs themselves—regardless of action—can be risky, costly, or harmful. The third locus concerns epistemic risks we confront as social epistemic agents. I aim to motivate the relevant alternatives framework as a fruitful approach to the epistemology of risk. I first articulate a ‘relevant alternatives’ model of the relationship between stakes, evidence, and action. I then employ the relevant alternatives framework to undermine the motivation for moral encroachment. Finally, I argue the relevant alternatives framework illuminates epistemic phenomena such as gaslighting, conspiracy theories, and crying wolf, and I draw on the framework to diagnose the undue skepticism endemic to rape accusations. (shrink)

I present a case for a rapprochement between aspects of rationalism and scientific realism, by way of a general framework employing modal epistemology and elements of 2-dimensional semantics (2DS). My overall argument strategy is meta-inductive: The bulk of this paper establishes a “base case,” i.e., a concretely constructive example by which I demonstrate this linkage. The base case or constructive example acts as the exemplar for generating, in a constructively ‘bottom-up’ fashion, a more generally rigorous case for rationalism-realism qua modal (...) epistemology. The exemple I choose in D. Chalmers’ (2002) modal rationalism and R. Giere’s (1985, 1988) constructive realism. I show by way of a thorough analysis how Giere’s claims concerning modal scope are characterized as instances of Chalmers’ modal rationalism, both weak and strong. In essence, as I demonstrate via Chalmers’ notions, ceteris paribus the constructive realist ultimately opts for a comparatively wider gate, characterized by modal reasoning, to lead from the rooms of conceivability qua thought experiments and models, to the pastures of metaphysical possibility. Chalmers likewise tries to erect such a wider gate, in his general conceivability-possibility theses. Anti-realists, on the other hand, see a narrower passage and my contention herein is that they suffer from modal myopia, which hopefully the ‘corrective vision’ of Chalmers’ modal rationalism can restore. In the introduction and concluding sections I sketch out suggestions of constructing ‘inductive steps’ from my base case, to generate more extensively general claims regarding realism qua rationalism. -/- . (shrink)

P&C insurers have an important role in addressing financial risk management needs but now struggle to respond to the new forms of risk. Historical analysis and actuarial calculations, which form the backbone of classical approaches to risk measurement and management, are not well suited to such new kinds of risks as climate change, cyber risks, and business cycle risks. These conventional approaches are also a static method for selling, which has limited potential in changing quickly with new market and consumer (...) changes. However, the development of AI, big data, or advanced predictive analytics has provided a new paradigm to such challenges to cope with and analyze big data dynamically to derive Applying a combination of best Machine Learning (ML), natural language processing (NLP) and other AI techniques, it will become possible to predict risks exposures, as well as optimize underwriting and claims processing. In this paper, the author investigates the opportunities that AI carries within the sphere of reformation of risk evaluation approaches in P&C insurance. In particular, it discusses the ever-emerging field with AI models integrated into operations, underwriting, fraud detection, claims modelling, and loss computation. The applied research uses a comprehensive bibliographical review to present past and present practices and assess modern AI implementations. This paper uses research approaches and case studies to prove that AI can solve some problems and bring cost-saving solutions. Moreover, it emphasizes the ability of predictive analytics to enhance customer satisfaction at the policy level as well as optimize operations. However, the paper also explores the dirty side of implementation, including the stipulations surrounding compliance and data privacy and thus provides an objective analysis of the future of AI use in the P&C Insurance Industry. (shrink)

Modeling and simulation clearly have an upside. My discussion here will deal with the inevitable downside of modeling — the sort of things that can go wrong. It will set out a taxonomy for the pathology of models — a catalogue of the various ways in which model contrivance can go awry. In the course of that discussion, I also call on some of my past experience with models and their vulnerabilities.

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)

How feasible are conceptual engineering projects of social concepts that aim for the engineered concept to be deployed in people's ordinary conceptual practices? Predominant frameworks on the psychology of concepts that shape work on stereotyping, bias, and machine learning have grim implications for the prospects of conceptual engineers: conceptual engineering efforts are ineffective in promoting certain social‐conceptual changes. Since conceptual components that give rise to problematic social stereotypes are sensitive to statistical structures of the environment, purely conceptual change won't be (...) possible without corresponding world change. This tradition, however, tends to ignore that concepts don't only encode statistical, but also causal information. Paying attention to this feature of concepts, I argue, shows that conceptual engineering is not only possible. There is an imperative to conceptually‐engineer. (shrink)

The Internet of Things (IoT) is transforming industries by enabling devices to gather and share data. However, IoT devices often face limitations in processing power, storage, and energy consumption, restricting their ability to make complex decisions in real time. To address these challenges, cloud-assisted edge AI combines the advantages of edge computing and cloud-powered machine learning models, enabling IoT devices to make intelligent decisions at the edge while leveraging cloud resources for more complex processing tasks. This paper explores the (...) integration of edge AI with cloud assistance, demonstrating how this hybrid approach enhances decision-making capabilities in IoT devices. The paper examines key concepts, the architecture of cloud-assisted edge AI systems, application areas, and the benefits of combining edge and cloud computing. Furthermore, challenges such as latency, data privacy, and integration issues are discussed, alongside future directions for this technology in IoT applications. (shrink)

The intersection of Artificial Intelligence (AI) and design has evolved dramatically in recent years, largely driven by the rise of generative models. These models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models, are transforming how designs are created, allowing machines to generate innovative, customized, and optimized solutions autonomously. In industries such as architecture, product design, and fashion, AI-driven design tools are increasingly being used to automate the creative process, produce new designs, and even optimize (...) existing ones. This paper explores the impact of generative AI on the design industry, how AI models are integrated into design workflows, and the future potential of AI in reshaping traditional design practices. It also discusses the challenges and ethical considerations associated with the use of AI in creative processes. (shrink)

The new notions of super column FRM model, super row FRM model and mixed super FRM model are introduced in this book. These three models are introduced specially to analyze the biased role of the print media on 27 percent reservation for the Other Backward Classes (OBCs) in educational institutions run by the Indian Central Government.

Metatotal Triadic Metastable RCR and Q-RCR Models This paper introduces the Recursive-Collapse-Recombination (RCR) Model, a non-hierarchical, metastable framework that captures the fundamental dynamics of self-organizing systems across multiple domains. The RCR model describes metastability as an emergent process driven by three interdependent forces: recursion (self-similarity and iterative structuring), collapse (destabilization and phase shifts), and recombination (adaptive reconfiguration of past structures). Unlike conventional models, RCR is fully triadic, non-linear, and self-regulating, ensuring that no single force dominates permanently. I further (...) extend this framework into the Quantum Recursive-Collapse-Recombination (Q-RCR) Model, which applies these principles to quantum systems, entangled structures, and networked intelligence. Q-RCR accounts for metastable quantum interactions, partial wavefunction collapses, and emergent recombination effects that mirror decoherence and non-classical transitions. Both models have been mathematically validated and stress-tested across domains including evolutionary biology, AI cognition, network theory, economic systems, and quantum mechanics. The results confirm that the RCR framework replicates key real-world dynamical behaviors while avoiding traditional deterministic constraints. This work suggests that the RCR process—rather than reductionist laws—underlies the persistence and transformation of complex systems, offering a unified model of metastable reality. License This work, "The Metatotal Triadic Metastable RCR and Q-RCR Models"(© 2025 by Justin Gallant), is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You may share, adapt, and build upon this work, but must give proper attribution to the original author. (shrink)

This article offers a characterization of what I call multiple-models juxtaposition, a strategy for managing trade-offs among modeling desiderata. MMJ displays models of distinct phenomena to...