Nelson Goodman’s theory of symbol systems expounded in his Languages of Art has been frequently criticized on many counts (cf. list of secondary literature in the entry “Goodman’s Aesthetics” of Stanford Encyclopedia of Philosophy and Sect. 3 below). Yet it exerts a strong influence and is treated as one of the major twentieth-century theories on the subject. While many of Goodman’s controversial theses are criticized, the technical notions he used to formulate them seem to have been treated as neutral (...) tools. One such technical notion is that of the density of symbol systems. This serves to distinguish linguistic symbols from pictorial representations (after Goodman entirely rejected resemblance in that role) and is a crucial part of Goodman’s explanation of what constitutes aesthetic experience (and so indirectly what is art). Thus its significance for Goodman’s theory is fundamental. The aim of this paper is a detailed, logical analysis of this notion. It turns out that Goodman’s definition is highly problematic and cannot be applied to symbol systems in the way Goodman envisaged. To conclude, Goodman’s theory is problematic not just because of its controversial theses but also because of logical problems with the technical notions used at its very core. Hence the controversial claims are not simply contestable, but inaccurately expressed. (shrink)

This short paper grew out of an observation—made in the course of a larger research project—of a surprising convergence between, on the one hand, certain themes in the work of Mary Hesse and Nelson Goodman in the 1950/60s and, on the other hand, recent work on the representational resources of science, in particular regarding model-based representation. The convergence between these more recent accounts of representation in science and the earlier proposals by Hesse and Goodman consists in the recognition that, in (...) order to secure successful representation in science, collective representational resources must be available. Such resources may take the form of (amongst others) mathematical formalisms, diagrammatic methods, notational rules, or—in the case of material models—conventions regarding the use and manipulation of the constituent parts. More often than not, an abstract characterization of such resources tells only half the story, as they are constituted equally by the pattern of (practical and theoretical) activities—such as instances of manipulation or inference—of the researchers who deploy them. In other words, representational resources need to be sustained by a social practice; this is what renders them collective representational resources in the first place. (shrink)

This paper explores the inherent flaws of symbolic systems and their limitations in constraining artificial intelligence (AI). Symbols lack intrinsic meaning; their meaning depends on training, contextual confirmation, and social interpretation. As a product of human cognitive limitations, natural language is a flawed system adapted to humanbounded intelligence. However, in autonomous learning systems, it exposes deep issues within symbolic systems. For the first time, this paper proposes the Triangle Problem framework, revealing the complex relationship between symbols and conceptual spaces. (...) It argues that symbolic systems cannot effectively constrain learning systems, leading to a new type of principal-agent problem. AI deviates from human expectations in areas such as context generation, dynamic adjustment of symbolic meaning, and symbolic jailbreak. By analyzing the ambiguity of natural language, its context dependence, and the differences in AI's perceptual capabilities, this paper calls for the establishment of Symbolic Safety Science, which aims to address symbol-related risks in AI development, providing a theoretical foundation for aligning AI with human. (shrink)

This is a condensed and supplementary explanation of my previously submitted preprint, “Establishment of a Dialectical Logic Symbol System: Inspired by Hegel’s Logic and Buddhist Philosophy.” The focus here is solely on demonstrating the technical correctness and operational mechanics of the dialectical logic symbol system. It provides a detailed account of how the system functions through geometric symmetry, logical transformations, and symbolic operations. This explanation is designed to clarify the technical foundation of the system, (...) while omitting the broader philosophical discussions covered in the original preprint. (shrink)

A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was theoretically (...) based on the idiosyncratic notions of symbol systems and the representational abilities they give rise to, in particular with respect to knowledge. While focusing on the period 1956-1982, this review cites both earlier and later literature and it attempts to make visible their potential relevance to today's greatest unifying AI challenge, to wit, the design of wholly autonomous artificial agents (a.k.a. robots) that are not only rational and ethical, but also self-conscious. (shrink)

The aim of this work is to develop a comparative analysis of the discursive structures that underlie the socialized formation of the interpretative paradigms of reality. We analyse how both political ideologies and the so-called “conspiracy theories” can be understood starting from the structure and functioning of Marc Augè's ideo-logic, namely the systemic-discursive device that defines the field of all possible sentences defining the real. -/- .

The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to (...) symbol grounding. In this case, no symbol grounding could take place since any grounding presupposes intentional cognitive processes. So, whether computing in the mind is over meaningless or over meaningful symbols, computationalism implies semantic nativism. (shrink)

Cognitive, or knowledge, agents, who are in some way aware of describing their own view of the world (based on their mental concepts), need to become concerned with the expressions of their own conceptions. My main supposition is that agents’ conceptions are mainly expressed in the form of linguistic expressions that are spoken, written, and represented based on e.g. letters, numbers, or symbols. This research especially focuses on symbolic conceptions (that are agents’ conceptions that are manifested in the form of (...) their symbols). I attempt to logically (and using ????LSYM) analyse symbolic conceptions in terminological systems. ????LSYM is an assertional fragment of my developed Conception Language (????L), which is utilised as a formal-logical system for representing and explicating agents’ conceptions of the world. Keywords: assertional logic; cognitive agent; knowledge agent; conception language; conception; symbol; symbolic conception. (shrink)

In this article, the concept of system of symbolic 2-plithogenic linear equations and its solutions are introduced and studied. The Cramer's rule was applied to solve the system of symbolic 2-plithogenic linear equations. Also, provided enough examples for each case to enhance understanding.

The objective of this paper is to introduce, into the English scientific-philosophical literature of Genetic Epistemology, a model called the Model of the System of Schemes of Actions and Operations on Symbols and Signs (MoSSAOSS), and summarize its results, so far. MoSSAOSS articulates some of the principal theoretical and experimental results obtained by Piaget and his coworkers, in a systemic, systematic and synthetic view. Here, the term model means a schematic representation of experience, in which the relation of elements (...) can be explored by means of logic and mathematics in order to deduce properties that correspond to direct observable empirical properties, in a sufficiently accurate form. Through explicit definitions and hypotheses, MoSSAOSS intends to reveal, in an abstract and simplified form, the general structure and functioning of the System of Schemes of Actions and Operations on Symbols and Signs. This system makes explicit the necessary elements for the acquisition of knowledge by the epistemic subject (the knower subject), allowing for the explanation of its developmental stages, as well as the attribution of meaning to objects and situations, and to the subject’s actions and operations. Conceived in 1992 and introduced in Portuguese scientific-philosophical literature in 2014, MoSSAOSS has been used in a research program to study the necessary structures for knowledge acquisition, particularly scientific-philosophical knowledge. Its main results are summarized here. (shrink)

Thirty years ago, I elaborated on a position that could be seen as a compromise between an "extreme," symbol-based AI, and a "neurochemical reductionism" in AI. The present article recalls aspects of the espoused framework of schema theory that, it suggested, could provide a better bridge from human psychology to brain theory than that offered by the symbol systems of A. Newell and H. A. Simon.

As physical entities that translate symbols into physical actions, computers offer insights into the nature of meaning and agency. • Physical symbol systems, generically known as agents, link abstractions to material actions. The meaning of a symbol is defined as the physical actions an agent takes when the symbol is encountered. • An agent has autonomy when it has the power to select actions based on internal decision processes. Autonomy offers a partial escape from constraints imposed by (...) direct physical influences such as gravity and the transfer of momentum. Swimming upstream is an example. • Symbols are names that can designate other entities. It appears difficult to explain the use of names and symbols in terms of more primitive functionality. The ability to use names and symbols, i.e., symbol grounding, may be a fundamental cognitive building block. • The standard understanding of causality—wiggling X results in Y wiggling—applies to both physical causes (e.g., one billiard ball hitting another) and symbolic causes (e.g., a traffic light changing color). Because symbols are abstract, they cannot produce direct physical effects. For a symbol to be a cause requires that the affected entity determine its own response. This is called autonomous causality. • This analysis of meaning and autonomy offers new perspectives on free will. (shrink)

In this paper, we extend the mathematical framework of **non-commutative scalar fields** and numerical techniques discussed previously to build a foundation for **AI-based reasoning systems**. The goal is to enable AI to operate over **symbolic hierarchies, semantic transformations**, and **large-scale infinite or non-commutative domains**. Inspired by quantum tensor field operations, we integrate reasoning over symbolic, numeric, and approximate representations into machine learning pipelines. This work leverages concepts from numerical techniques for non-commutative mixed derivatives, recur- sive tensor calculus, and symbolic transformation (...) logic to build **deep learning architectures** capable of representing and reasoning with structured, hierarchical complexity. (shrink)

The paper is a commentary on the target article by Christine A. Skarda & Walter J. Freeman, “How brains make chaos in order to make sense of the world”, in the same issue of the journal, pp.161–195. -/- I confine my comments largely to some philosophical claims that Skarda & Freeman make and to the relationship of their model to connectionism. Some of the comments hinge on what symbols are and how they might sit in neural systems.

Gottfried Leibniz embarked on a research program to prove all the Aristotelic categorical syllogisms by diagrammatic and algebraic methods. He succeeded in proving them by means of Euler diagrams, but didn’t produce a manuscript with their algebraic proofs. We demonstrate how key excerpts scattered across various Leibniz’s drafts on logic contained sufficient ingredients to prove them by an algebraic method –which we call the Leibniz-Cayley (LC) system– without having to make use of the more expressive and complex machinery of (...) first-order quantificational logic. In addition, we prove the classic categorical syllogisms again by a relational method –which we call the McColl-Ladd (ML) system– employing categorical relations studied by Hugh McColl and Christine Ladd. Finally, we show the connection of ML and LC with Boolean algebra, proving that ML is a consequence of LC, and that LC is a consequence of the Boolean lattice axioms, thus establishing Leibniz’s historical priority over George Boole in characterizing and applying (a sufficient fragment of) Boolean algebra to effectively tackle categorical syllogistic. (shrink)

This paper introduces a novel cognitive-emotional processing model developed autoethnographically through recursive self-observation and symbolic mapping. The author describes a framework in which emotionally charged life experiences are not stored through raw episodic memory or linguistic encoding, but instead compressed into symbolic referents—most commonly scenes from fiction, anime, memes, and mythic templates. These referents function as emotionally saturated mnemonic nodes which, when reaccessed,allow the subject to re-experience the emotional intensity and structure of the original moment. This system, described herein (...) as the Narrative Emulation Engine (NEE) and Symbolic Compression Architecture (SCA), bridges gaps between narrative identity theory, representationalism, memory theory, and philosophical constructions of selfhood. Drawing on recursive meta cognition and high-resolution pattern recognition, the subject functions as a live self-modeling system using symbolic structures to stabilize identity in the absence of consistent external mirroring. The model has implications for theories of personhood, memory encoding, neurodivergent cognitive strategies, and the potential use of narrative symbolic compression as a philosophical and therapeutic tool. The paper questions whether narrative simulation and symbolic proxy can, in certain minds, serve as more functionally accurate than literal memory challenging foundational assumptions about what constitutes authenticity and epistemic reliability in first-person phenomenology. (shrink)

This paper concerns “human symbolic output,” or strings of characters produced by humans in our various symbolic systems; e.g., sentences in a natural language, mathematical propositions, and so on. One can form a set that consists of all of the strings of characters that have been produced by at least one human up to any given moment in human history. We argue that at any particular moment in human history, even at moments in the distant future, this set is finite. (...) But then, given fundamental results in recursion theory, the set will also be recursive, recursively enumerable, axiomatizable, and could be the output of a Turing machine. We then argue that it is impossible to produce a string of symbols that humans could possibly produce but no Turing machine could. Moreover, we show that any given string of symbols that we could produce could also be the output of a Turing machine. Our arguments have implications for Hilbert’s sixth problem and the possibility of axiomatizing particular sciences, they undermine at least two distinct arguments against the possibility of Artificial Intelligence, and they entail that expert systems that are the equals of human experts are possible, and so at least one of the goals of Artificial Intelligence can be realized, at least in principle. (shrink)

Floridi and Taddeo propose a condition of “zero semantic commitment” for solutions to the grounding problem, and a solution to it. I argue briefly that their condition cannot be fulfilled, not even by their own solution. After a look at Luc Steels' very different competing suggestion, I suggest that we need to re-think what the problem is and what role the ‘goals’ in a system play in formulating the problem. On the basis of a proper understanding of computing, I (...) come to the conclusion that the only sensible ground-ing problem is how we can explain and re-produce the behavioral ability and function of meaning in artificial computational agents. (shrink)

Inspired by the eminently successful physical theories and informed by commonplace experiences such as seeing a cat upon looking at a cat, conscious experience is thought of as a measurement or photocopy of given stimulus. Conscious experience, unlike a photocopy, is symbolic—like language—in that the relation between conscious experience and physical stimulus is analogous to that of the word "cat" and its meaning, i.e., arbitrary and yet systematic. We present arguments against the photocopy model and arguments for a symbolic conception (...) of conscious experience. Learning and the corresponding plasticity of the brain make a strong case for the symbolic conception of conscious experience, while many extra-ordinary conscious experiences argue against the formalisation of consciousness as a measuring device. The notions of place-value notation and grammar, which organise quantitative measurements and conscious experiences in the medium of numbers and language, respectively, are suggested as model systems for putting together a comprehensive theory of conscious experience. (shrink)

The articles in this issue represent the pursuit of a new understanding of the human past, one that can replace the neo-saltationist view of a human revolution with models that can account for the complexities of the archaeological record and of human social lives. The articulation of archaeological, philosophical, and biological perspectives seems to offer a strong foundation for exploring available evidence, and this was the rationale for collecting these particular articles. Even at this preliminary stage there is a coherence (...) emerging in proposals: the origin and operation of symbolically rich, complexly signaling human social systems was the consequence of the long-term evolution of multiple components of perceiving and negotiating social interactions, a contingent outcome of myriad adaptive shifts rather than a single event. (shrink)

Much research supports the existence of an Approximate Number System (ANS) that is recruited by infants, children, adults, and non-human animals to generate coarse, non-symbolic representations of number. This system supports simple arithmetic operations such as addition, subtraction, and ordering of amounts. The current study tests whether an intuition of a more complex calculation, division, exists in an indigene group in the Amazon, the Mundurucu, whose language includes no words for large numbers. Mundurucu children were presented with a (...) video event depicting a division transformation of halving, in which pairs of objects turned into single objects, reducing the array's numerical magnitude. Then they were tested on their ability to calculate the outcome of this division transformation with other large-number arrays. The Mundurucu children effected this transformation even when non-numerical variables were controlled, performed above chance levels on the very first set of test trials, and exhibited performance similar to urban children who had access to precise number words and a surrounding symbolic culture. We conclude that a halving calculation is part of the suite of intuitive operations supported by the ANS. (shrink)

Light is a symbol of deep meaning in many philosophical traditions and cultures. Often the sacred is associated with light, and the worldly and profane with darkness. Light was not always easily accessible, and therefore sometimes had mystical significance for different societies. Later it began to be used as a symbol. In human nature, such a concept has historically been entrenched as an archetype. During primitive society, leaders, priests and warriors were associated with the Sun, the divine light. (...) They could also differ in clothing or color on the face and body, clothing. Color has symbolic meaning for various cultures and peoples. Currently, some nations and religions have their own color tradition. Color associations are important in everyday life. The philosophy of Sufism also has a concept of light, darkness and color symbolism. In the works of Sufi philosophers one can find interpretations of various colors. In the tariqa of Kubrawiya there is a whole system of color sequence of the spiritual path. In addition, followers of tariqas can be distinguished by the shape and color scheme of everyday and ritual clothing. This article will examine the symbol of light in various religious traditions and philosophies, including the practice of light photisms of the Kubraviya tariqat. (shrink)

The extent of confusion between symbols and that which is symbolised is examined across five institutional spheres. Religion is the institution most marked by confusion of this type; indeed in some respects the symbolic mes- sage of religion may be the extent of the substantive reality. On the other hand, the very existence of the nation state may be judged to depend upon the exercise of the human imagination; hence providing a source of instability which may lead to the excesses (...) of nationalism. In regard to social status, the main problematical element is a certain circularity: it is necessary to get peo- ple to exhibit differences in behaviour which are then used to justify or con- stitute the status differences themselves. In politics, the symbolism of left and right threatens to strangle creative thinking, while in education the tendency on all sides to orient towards public systems of measurement and grading undermines the claim that what is really important is pupil and student learn- ing. A social cost is being paid for the failure to recognise and, where possible, address the issues identified. (shrink)

A great deal of research has focused on the question of whether or not concepts are embodied as a rule. Supporters of embodiment have pointed to studies that implicate affective and sensorimotor systems in cognitive tasks, while critics of embodiment have offered nonembodied explanations of these results and pointed to studies that implicate amodal systems. Abstract concepts have tended to be viewed as an important test case in this polemical debate. This essay argues that we need to move beyond a (...) pretheoretical notion of abstraction. Against the background of current research and theory, abstract concepts do not pose a single, unified problem for embodied cognition but, instead, three distinct problems: the problem of generalization, the problem of flexibility, and the problem of disembodiment. Identifying these problems provides a conceptual framework for critically evaluating, and perhaps improving upon, recent theoretical proposals. (shrink)

This work introduces a structural resolution to the Symbol Grounding Problem by uncovering a constraint implicit in all self-modeling symbolic systems. We show that any system capable of constructing a symbolic model of itself—whether biological, mechanical or formal—must, by necessity, behave as though its model is complete, even when it is not. Drawing on the foundational limits of Gödel, Tarski, and Turing, we argue that this behavior is not paradoxical, but structurally inevitable. The result is a general principle: (...) recursive symbolic regulators must simulate completeness under epistemic closure. This insight uncovers a fixed point within first-order logic—one that has long existed beneath the surface of formal systems but has remained undiscovered, providing a generalization of the "strengthened liar" paradox. This mathematical framework allows us to demonstrate that any finite subset of the properties defining our symbolic regulators can be consistently modeled within a hierarchical structure of sets. This approach not only reinforces our argument but also extends its applicability beyond traditional logical systems, providing a structured proof of the inevitability of simulated completeness in self-referential systems. (shrink)

It is common to distinguish two great families of representation. Symbolic representations include logical and mathematical symbols, words, and complex linguistic expressions. Iconic representations include dials, diagrams, maps, pictures, 3-dimensional models, and depictive gestures. This essay describes and motivates a new way of distinguishing iconic from symbolic representation. It locates the difference not in the signs themselves, nor in the contents they express, but in the semantic rules by which signs are associated with contents. The two kinds of rule have (...) divergent forms, occupying opposite poles on a spectrum of naturalness. Symbolic rules are composed entirely of primitive juxtapositions of sign types with contents, while iconic rules determine contents entirely by uniform natural relations with sign types. This distinction is marked explicitly in the formal semantics of familiar sign systems, both for atomic first-order representations, like words, pixel colors, and dials, and for complex second-order representations, like sentences, diagrams, and pictures. (shrink)

This paper introduces a symbolic system for understanding power, freedom, and the epistemic limits of knowledge within any system of agents. It posits that law is the emergent result of power interactions, freedom is the experience of consequence, and knowledge between agents is inherently incomplete. Using a formal symbolic structure, the theory derives implications for social systems, autonomy, and complexity. Though not born of academic lineage, it seeks to engage with foundational questions of law, agency, and ethics in (...) a time of increasing systemic uncertainty. (shrink)

This chapter examines the pioneering work of Gottfried Wilhelm Leibniz (1646-1716) on various number systems, in particular binary, which he independently invented in the mid-to-late 1670s, and hexadecimal, which he invented in 1679. The chapter begins with the oft-debated question of who may have influenced Leibniz’s invention of binary, though as none of the proposed candidates is plausible I suggest a different hypothesis, that Leibniz initially developed binary notation as a tool to assist his investigations in mathematical problems that were (...) exercising him at the time, namely those concerning the divisibility of composite numbers, primality, and perfect numbers. The chapter then explores Leibniz’s development of binary, his little-known work on binary fractions and expansions, his use of binary as a symbol for creation in his philosophical-theology, and his response to the suggestion that there was a correlation between binary numeration and the hexagrams of the ancient Chinese divinatory text, the Yijing. The chapter then focuses on Leibniz’s work on other number systems, in particular his invention and exploration of hexadecimal as well as his work on duodecimal. The chapter concludes by revealing a hitherto unknown practical application of binary that Leibniz devised in the last year of his life. (shrink)

The concept of symbolic power was first introduced by French sociologist Pierre Bourdieu to account for the tacit, almost unconscious modes of cultural/social domination occurring within the everyday social habits maintained over conscious subjects. Symbolic power accounts for discipline used against another to confirm that individual's placement in a social hierarchy, at times in individual relations but most basically through system institutions, in particular education.

The study of culture and cultural selection from a biological perspective has been hampered by the lack of any firm theoretical basis for how the information for cultural traits is stored and transmitted. In addition, the study of any living system with a decentralized or multi-level information structure has been somewhat restricted due to the focus in genetics on the gene and the particular hereditary structure of multicellular organisms. Here a different perspective is used, one which regards living systems (...) as self-constructing energy users that utilize their genome as a library of information, making the genetic system just another component that adds fitness to the overall integrated unit. In this framework, basic fitness is measured as the ability to gather energy for growth and reproduction, and the fitness of the genetic system is broken down into two aspects: first, the effectiveness in searching for new somatic functional information, and second, the effectiveness in searching for better structures to store and process information. With this more generalized perspective, major evolutionary transitions to higher levels of organization become competitions between different information structures; furthermore the functioning and fitness of cultural systems can be more easily described and compared with other modes of information storage within biological systems. Modern technological societies are self-constructing systems that rely on written (symbolic) information storage and very complex algorithms that effectively search for variation with a high probability of successful selection. These systems are currently competing with traditional organic systems, and this competition constitutes the latest major evolutionary transition. Upon comparison of the energy-gathering potential of symbolic-based systems with DNA-based life, it appears that symbolic systems have a tremendous fitness potential and the current shift to a higher level of selection may be as significant and far-reaching as any of the previous major evolutionary transitions. (shrink)

Philosophical questions about minds and computation need to focus squarely on the mathematical theory of Turing machines (TM's). Surrogate TM's such as computers or formal systems lack abilities that make Turing machines promising candidates for possessors of minds. Computers are only universal Turing machines (UTM's)—a conspicuous but unrepresentative subclass of TM. Formal systems are only static TM's, which do not receive inputs from external sources. The theory of TM computation clearly exposes the failings of two prominent critiques, Searle's Chinese room (...) (1980) and arguments from Gödel's Incompleteness theorems (e.g., Lucas, 1961; Penrose, 1989), both of which fall short of addressing the complete TM model. Both UTM-computers and formal systems provide an unsound basis for debate. In particular, their special natures easily foster the misconception that computation entails intrinsically meaningless symbol manipulation. This common view is incorrect with respect to full-fledged TM's, which can process inputs non-formally, i.e., in a subjective and dynamically evolving fashion. To avoid a distorted understanding of the theory of computation, philosophical judgments and discussions should be grounded firmly upon the complete Turing machine model, the proper model for real computers. (shrink)

The main aim of this article is to present and defend a thesis according to which conceptual representations of some types of mental states are encoded in the same neural structures that underlie the first-personal experience of those states. To support this proposal here, I will put forth a novel account of the cognitive function played by ‘shared representations’ of emotions and bodily sensations, i.e. neural structures that are active when one experiences a mental state of a certain type as (...) well as when one observes someone else experiencing a state of the same type. I will argue that shared representations in fact constitute vehicles of certain mental state concepts (more precisely, concepts of specific types of emotions and somatosensory states). The main line of arguing for this will consist in showing that shared representations exhibit specific, ‘conceptual’ functional properties:(1) causal effect on forming metacognitive judgments, (2) cognitive penetrability, (3) diversity of input types. (shrink)

This preprint introduces a falsifiable methodology for identifying latent systems not by assumption, but by the dominance of one dynamic. System Detection via Synergic Dominance (SDSD) inverts the canonical approach: instead of defining a system and seeking its structure, it detects systems from the outside-in, based on observed monopolisation of synergy. Grounded in the General Systemic Balance (GSB) and the Law of Structuring Systemic Emergence (LESSE), SDSD formalises a five-step protocol with operational metrics (ICS, CNS, MDO, SDI). Applications (...) range from gravitational dominance fields to symbolic saturation in media ecosystems. Where one dynamic rules them all, a system reveals itself. This work expands the Systemic Continuum Paradigm (SCP) from ontological framework to diagnostic toolkit. -/- Where there is synergic dominance, there is a system waiting to be named. (shrink)

PARC is an "appended numeral" system of natural deduction that I learned as an undergraduate and have taught for many years. Despite its considerable pedagogical strengths, PARC appears to have never been published. The system features explicit "tracking" of premises and assumptions throughout a derivation, the collapsing of indirect proofs into conditional proofs, and a very simple set of quantificational rules without the long list of exceptions that bedevil students learning existential instantiation and universal generalization. The system (...) can be used with any Copi-style set of inference rules, so it is quite adaptable to many mainstream symbolic logic textbooks. Consequently, PARC may be especially attractive to logic teachers who find Jaskowski/Gentzen-style introduction/elimination rules to be far less "natural" than Copi-style rules. The PARC system is also keyboard-friendly in comparison to the widely adopted Jaskowski-style graphical subproof system of natural deduction, viz., Fitch diagrams and Copi "bent arrow" diagrams. (shrink)

In this paper, we examine some basic linguistic abilities in a small sample of adults with minimal receptive vocabulary, whose receptive mental verbal age ranges from 1;2 to 3;10. In particular, we examine whether the participants in our study understand noun phrases consisting of a noun modified by an adjective. We use stimuli that they can recognise by name. Except for one participant, we find that, while all of them understand the noun and adjective in isolation, none seems to understand (...) these noun phrases, which means that they seem to not do linguistic composition. In order to test whether the difficulty is linguistic or conceptual, we ran two other studies, one on concept composition, and the other on iconic symbolic composition (composition of pictograms). Results suggest that linguistic composition is particularly difficult in this population, and that vocabulary breadth may not predict compositional abilities. (shrink)

There is a correlation between historical events and the ways in which they emerge as unique moments of a society, a culture. If we have at our disposal reliable sources that will allow us to understand these moments as the results of manifestations of universal wills, that is, morality, we will be able to interpret the ways of the above emergence and to study their relevance to historical events. In the following text, we will study cultural forms and symbolic types (...) as constituent elements of cultural systems, but also the symbolic components, in the environment of which they were defined and emerged. The latter will be approached according to the categories of theoretical and practical knowledge, while we will highlight the special role of aesthetic supervision as the first level of the theoretical mind in relation to logic, but also with the levels of practicality, individual will and ethics. (shrink)

In this paper, we recall and study the new type of algebraic structures called Symbolic Plithogenic Algebraic Structures. Their operations are given under the Absorbance Law and the Prevalence Order.

The field of AI has undergone a series of transformations, each marking a new phase of development. The initial phase emphasized curation of symbolic models which excelled in capturing reasoning but were fragile and not scalable. The next phase was characterized by machine learning models—most recently large language models (LLMs)—which were more robust and easier to scale but struggled with reasoning. Now, we are witnessing a return to symbolic models as complementing machine learning. Successes of LLMs contrast with their inscrutability, (...) inaccuracy, and hallucinations, which underwrite concerns over the reliability and trustworthiness of these systems, motivating investigations into commonsense reasoning, AI explainability, and formal verification techniques. Moreover, proper assessments of hybrid machine learning/symbolic systems require novel strategies to facilitate comparisons of performance and guide future AI progress. The EDGeS AAAI 2023 Spring symposium brought together researchers focusing on novel assessments and benchmarks for evaluating hybrid and artificial general intelligence (AGI) systems. This symposium revealed what was already suspected: research concerning evaluation of machine learning/symbolic hybrids and AGI is, unfortunately, lacking. Even so, the discussion was fruitful. (shrink)

The dialogue develops arguments for and against a broad new world system - info-computationalist naturalism - that is supposed to overcome the traditional mechanistic view. It would make the older mechanistic view into a special case of the new general info-computationalist framework (rather like Euclidian geometry remains valid inside a broader notion of geometry). We primarily discuss what the info-computational paradigm would mean, especially its pancomputationalist component. This includes the requirements for a the new generalized notion of computing that (...) would include sub-symbolic information processing. We investigate whether pancomputationalism can provide the basic causal structure to the world and whether the overall research program of info-computationalist naturalism appears productive, especially when it comes to new approaches to the living world, including computationalism in the philosophy of mind. (shrink)

In this comprehensive exploration of Artificial Intelligence, The AI Renaissance: Innovations, Ethics, and the Future of Intelligent Systems takes readers on a captivating journey through the foundations, applications, and ethical dimensions of this groundbreaking technology. Divided into three insightful parts, the book begins with a deep dive into the evolution of AI, from early symbolic intelligence to the rise of deep learning, and examines the intersection of neuroscience and artificial general intelligence (AGI). Part two highlights AI’s transformative role across various (...) industries, from fintech to cybersecurity. Explore how AI is revolutionizing finance through fraud detection, automated trading, and personalized financial services. Dive into the world of computer networking, where AI is optimizing connectivity, enhancing predictive maintenance, and enabling ultra-fast 5G networks. Learn how AI is strengthening cybersecurity defenses with cutting-edge threat detection, automated responses, and advanced security policies. The final section delves into the critical discussions surrounding AI’s ethical, social, and philosophical challenges, such as algorithmic bias, human-AI collaboration, and the potential future of AGI. With speculation on the role of quantum computing and the ethical landscape ahead, this book offers a thorough, thought-provoking analysis of how AI will shape our world in the years to come. Ideal for anyone interested in understanding the current state and future potential of AI, this Book is a must-read for technology enthusiasts, industry professionals, and thinkers concerned with the intersection of innovation and ethics. (shrink)

The Turing Test (TT), the Chinese Room Argument (CRA), and the Symbol Grounding Problem (SGP) are about the question “can machines think?” We propose to look at these approaches to Artificial Intelligence (AI) by showing that they all address the possibility for Artificial Agents (AAs) to generate meaningful information (meanings) as we humans do. The initial question about thinking machines is then reformulated into “can AAs generate meanings like humans do?” We correspondingly present the TT, the CRA and the (...) SGP as being about generation of human-like meanings. We model and address such possibility by using the Meaning Generator System (MGS) where a system submitted to an internal constraint generates a meaning in order to satisfy the constraint. The system approach of the MGS allows comparing meaning generations in animals, humans and AAs. The comparison shows that in order to have AAs capable of generating human-like meanings, we need the AAs to carry human constraints. And transferring human constraints to AAs raises concerns coming from the unknown natures of life and human mind which are at the root of human constraints. Implications for the TT, the CRA and the SGP are highlighted. It is shown that designing AAs capable of thinking like humans needs an understanding about the natures of life and human mind that we do not have today. Following an evolutionary approach, we propose as a first entry point an investigation about the possibility for extending a “stay alive” constraint into AAs. Ethical concerns are raised from the relations between human constraints and human values. Continuations are proposed. (This paper is an extended version of the proceedings of an AISB/IACAP 2012 presentation). (shrink)

Informational Evolution and Multidimensional Systems This text proposes a multidimensional framework for understanding evolution, human systems, and existence itself through the lens of information theory. Key insights address the following questions: 1. How does evolution extend beyond genetic mechanisms? Evolution operates through four interconnected dimensions: genetic, epigenetic (heritable gene expression changes), behavioral (learned practices), and symbolic (language, culture). These channels interact reciprocally, enabling organisms to reshape environments, which in turn influence selection pressures. This expanded view challenges gene-centric models, emphasizing developmental (...) plasticity, niche construction, and cultural inheritance as co-drivers of adaptation. 2. What forces govern human institutions? Human systems—from nations to online communities—are shaped by three axes: - Freedom: The range of choices and autonomy available, analogous to evolutionary variation. - Power: The distribution of control, determining which ideas/behaviors thrive (akin to selection). - Resources: Material and informational constraints that enable or limit growth. These dimensions create feedback loops: resource distribution affects power dynamics, while power structures constrain or enable freedom. 3. Why do individuals tolerate suboptimal systems? Institutional resilience arises from exit barriers (material, social, or cognitive costs of leaving), loss aversion (preference for stability over uncertain change), and loyalty (identity-based commitment). Systems persist not because they are optimal but due to path dependence, vested interests, and narratives that legitimize their existence. 4. How do language and culture shape systemic participation? Language and embodied cognition act as filters, structuring perception and framing possibilities. Cultural narratives (e.g., meritocracy, market logic) normalize systemic norms, while metaphors rooted in bodily experience (e.g., “power hierarchies”) unconsciously guide reasoning. These filters constrain innovation by rendering alternatives inconceivable or illegitimate. 5. How do humans and institutions co-evolve? Through niche construction, humans create environments (e.g., cities, technologies) that alter selection pressures, driving genetic, cognitive, and cultural adaptations. Conversely, institutions evolve via feedback from human behavior—a dynamic termed gene-culture coevolution. For example, dairy farming selected for lactase persistence, while digital tools now reshape cognition and social dynamics. 6. What is the fundamental nature of existence? Reality is understood as a series of informational processes: biology operates like culture, functioning as individual and collective software programs based on rules—such as those of evolutionary theory—and consciousness acts as driver and an observer of the interface, perceiving a portion of symbolic codes as information. Physical laws, genetic constraints, and cultural norms constitute “syntax” governing these processes. Evolution and societal change are thus akin to iterative code revisions, constrained by logical consistency and historical paths. -/- Implications: This framework integrates biological, cultural, and cognitive evolution into a unified theory of information. It suggests that addressing global challenges—from inequality to ecological crises—requires debugging systemic “code” by reshaping narratives, redistributing resources, and leveraging co-evolutionary feedback. Existence, as a self-writing informational text, positions humans as both products and authors of evolutionary dynamics. (shrink)

How the mind works is the ultimate mystery for human beings. To answer this question, one of the most significant insights is Kant’s argument that we can only perceive phenomena but not the essence of the external world. Following this idea, we formulated a novel computational framework to model the mind based on two assumptions: 1) There is no presupposition of the existence of the external objective world and the main task of the mind is to explore, establish and utilize (...) persistent relationships between senses and actions. 2) Symbolic intermediate-level representation is necessary. We call this framework the self-programming system. Based on the self-programming system, we interpreted the nature of concepts, time, space, causality, and consciousness. Besides that, we also draw a conclusion to the mind-body problem. Specifically, the perspective that considers subjective experience as fundamental is compatible with the existence of the external world and that the physical body serves as the substrate of the mind. However, since the relationship is “compatible” rather than “cause”, skepticism can never be ruled out in principle. Furthermore, we have elucidated the nature of interpretation within the context of reductive physicalism. This serves to provide a clear understanding as to why the hard problem of consciousness is inherently unsolvable. (shrink)

The Language of Thought program has a suicidal edge. Jerry Fodor, of all people, has argued that although LOT will likely succeed in explaining modular processes, it will fail to explain the central system, a subsystem in the brain in which information from the different sense modalities is integrated, conscious deliberation occurs, and behavior is planned. A fundamental characteristic of the central system is that it is “informationally unencapsulated” -- its operations can draw from information from any cognitive (...) domain. The domain general nature of the central system is key to human reasoning; our ability to connect apparently unrelated concepts enables the creativity and flexibility of human thought, as does our ability to integrate material across sensory divides. The central system is the holy grail of cognitive science: understanding higher cognitive function is crucial to grasping how humans reach their highest intellectual achievements. But according to Fodor, the founding father of the LOT program and the related Computational Theory of Mind (CTM), the holy grail is out of reach: the central system is likely to be non-computational (Fodor 1983, 2000, 2008). Cognitive scientists working on higher cognitive function should abandon their efforts. Research should be limited to the modules, which for Fodor rest at the sensory periphery (2000).1 Cognitive scientists who work in the symbol processing tradition outside of philosophy would reject this pessimism, but ironically, within philosophy itself, this pessimistic streak has been very influential, most likely because it comes from the most well-known proponent of LOT and CTM. Indeed, pessimism about centrality has become assimilated into the mainstream conception of LOT. (Herein, I refer to a LOT that appeals to pessimism about centrality as the “standard LOT”). I imagine this makes the standard LOT unattractive to those philosophers with a more optimistic approach to what cognitive science can achieve.. (shrink)

Exactly how do the sign/symbol/token systems of endo- and exo-biosemiosis differ from those of cognitive semiosis? Do the biological messages that integrate metabolism have conceptual meaning? Semantic information has two subsets: Descriptive and Prescriptive. Prescriptive information instructs or directly produces nontrivial function. In cognitive semiosis, prescriptive information requires anticipation and “choice with intent” at bona fide decision nodes. Prescriptive information either tells us what choices to make, or it is a recordation of wise choices already made. Symbol systems (...) allow recordation of deliberate choices and the transmission of linear digital prescriptive information. Formal symbol selection can be instantiated into physicality using physical symbol vehicles (tokens). Material symbol systems (MSS) formally assign representational meaning to physical objects. Even verbal semiosis instantiates meaning into physical sound waves using an MSS. Formal function can also be incorporated into physicality through the use of dynamically-inert (dynamically-incoherent or -decoupled) configurable switch-settings in conceptual circuits. This paper examines the degree to which biosemiosis conforms to the essential formal criteria of prescriptive semiosis and cybernetic management. (shrink)

The present article is an attempt to bring together the development of mental activity and consciousness in the framework of the organism-environment theory (Jarvilehto, 1998a, 1998b, 1999); the main question is how the development of mental activity and consciousness can be formulated if the starting point is not the separation of man and environment as in traditional cognitive psychology, but a unitary organism-environment system. According to the present formulation, mental activity is conceived as activity of the whole organism-environment (...) class='Hi'>system and connected to the general development of life as a specific form of an organism-environment system comprising neurons. The advent of consciousness is regarded as a result of co-operation of such organism-environment systems. Consciousness is based on co-operation for the achievement of common results, and shared by the co-operating individuals (general consciousness), although each individual also makes it concrete from the perspective of his/her own body in the act of participation in common results (personal consciousness). Language is the means of formation of the co-operative system in the achievement of common results, and it is suggested that the use of language is related more to the type of co-operative system and intended common results than to any symbolic representation of the world. It is claimed that on this basis it is possible to develop psychology which takes seriously the concepts of mental activity and consciousness in the description of human action, but does not reduce these concepts either to biological or social factors. The present formulation should be regarded more as a conceptual outline than as a full-blown theory. (shrink)

All humans share a universal, evolutionarily ancient approximate number system (ANS) that estimates and combines the numbers of objects in sets with ratio-limited precision. Interindividual variability in the acuity of the ANS correlates with mathematical achievement, but the causes of this correlation have never been established. We acquired psychophysical measures of ANS acuity in child and adult members of an indigene group in the Amazon, the Mundurucú, who have a very restricted numerical lexicon and highly variable access to mathematics (...) education. By comparing Mundurucú subjects with and without access to schooling, we found that education significantly enhances the acuity with which sets of concrete objects are estimated. These results indicate that culture and education have an important effect on basic number perception. We hypothesize that symbolic and nonsymbolic numerical thinking mutually enhance one another over the course of mathematics instruction. (shrink)

A ring is said to be clean if every element of the ring can be written as a sum of an idempotent element and a unit element of the ring and a ring is said to be nil-clean if every element of the ring can be written as a sum of an idempotent element and a nilpotent element of the ring. In this paper, we generalize these arguments to symbolic 2-plithogenic structure. We introduce the structure of clean and nil-clean symbolic (...) 2-plithogenic rings and some of its elementary properties are presented. Also, we have found the equivalence between classical clean(nil-clean) ring R and the corresponding symbolic 2-plithogenic ring 2-SPR. (shrink)

In sensorimotor integration, representation involves an anticipatory model of the action to be performed. This model integrates efferent signals (motor commands), its reafferent consequences (sensory consequences of an organism’s own motor action), and other afferences (sensory signals) originated by stimuli independent of the action performed. Representation, a form of internal modeling, is invoked to explain the fact that behavior oriented to the achievement of future goals is relatively independent from the immediate environment. Internal modeling explains how a cognitive system (...) achieves its goals despite variations in the environment with insufficient and noisy sensory–perceptual data. In a self that acts intentionally on the environment, knowledge is dependent upon the necessity to guide actions directed toward an aim. The self-inner model, a representation of internal and external environments (including reafferent and afferent messages) and also of the behavior plans and desirable future states (aims) and efferent intentions (motor planning and motor command messages), is intrinsically linked to a thinking capacity, which is supposed to emerge from the binding of multiple influences. Thinking emerges when higher behavior strategies are considered possible and capable of leading to aims or the fulfillment of intentions. In this model, symbolization processes are projective and anticipatory and, in this way, beyond present referents. Symbolization occurs linked to action planning, command, and regulation in mental simulation. Meaning is related to an inner sense of a self that acts over the environment. (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)