Gordana Dodig-Crnkovic

Quantum theory places observation within the physical interactions through which values of physical variables are established. Relational interpretations make this explicit by describing physical facts as relative to interactions among systems. This article develops an info-computational account of this relationality within the Info-Computational Cognitive Naturalism (ICON) framework. Its central proposal is observer-agency: every interaction has a receptive direction, in which a system detects input and undergoes change, and an active direction, in which it acts and contributes to change in other systems. At the fundamental physical level, observer-agency provides the reciprocity from which more organized forms of observation and action develop. Perspectival relationality is distinguished from constructive relationality, in which interactions alter the organization conditioning later interactions. Quantum measurement exemplifies this through nested physical, cognitive, social and technological processes. Relational objectivity (intersubjectivity) is achieved through reproducible and world-constrained relations among situated observations. Extending the analysis across organizational levels establishes generative continuity. Morphology embodies constraints as Natural Logic, while computation unfolds this logic through transformation processes. Cross-domain logical continuity in the organization of constraints is interpreted as a form of Logical Permeationism.

1. Computing education has traditionally focused on technical knowledge but awareness of the importance of non-technical and non-mathematical skills is increasing, suggesting that more holistic com...

Cognitive Platonism offers a naturalistic account of abstract forms. It accepts the objectivity and non-arbitrariness of mathematical structures without requiring that they exist as autonomous entities in an independently given Platonic realm. The present paper clarifies this position in response to Michael Levin's recent comparison between his account of Platonic Space and Cognitive Platonism. The central distinction concerns the relation between natural regularities and abstract forms. Natural systems exhibit structured regularities independently of observers, while cognitive agents identify, stabilize, and progressively abstract those regularities into representations, concepts, models, and mathematical structures. Mathematical discovery is therefore compatible with cognitive construction: once an abstract relational structure has been constituted, previously unknown consequences can genuinely be discovered within it. Historical examples such as π, e, and the Pythagorean theorem illustrate how mathematical structures can emerge through interaction with practical and physical problems while later acquiring considerable inferential autonomy. Their necessity does not by itself establish that they exist prior to and independently of the physical world in a separate ontological domain. They may instead express inherent relational properties of nature itself. The paper argues that the main difference between Cognitive Platonism and Levin's Platonic Space concerns ontological direction: whether abstract forms independently exist and are instantiated in nature, or whether they are embodied in natural processes and expressed through cognitive abstractions of relational regularities.

This paper provides a presentation and structural overview of the book Evolving Intelligence. The Generative Path from Information and Computation to Cognition and Intelligence. The monograph is forthcoming with World Scientific in 2026. Evolving Intelligence develops a naturalistic, generative framework for understanding how intelligence emerges from the organization of nature. Rather than beginning with human reasoning or with artificial intelligence, the book traces a continuous path from physical and chemical organization in nature through life and cognition to human symbolic culture, artificial intelligence, and hybrid human-AI systems. Its central framework, ICON (Information-Computation-Cognition Naturalism), connects these domains through morphology, interaction, observer-agency, information, computation, cognition, consciousness, and intelligence.The guiding question is not simply “What is intelligence?” but “How can intelligence arise at all within nature?” The book therefore shifts attention from intelligence as an isolated human faculty to the generative processes through which increasingly complex forms of organization, sensing, memory, anticipation, agency, meaning, and problem solving emerge.

Mark Burgin's work spanned mathematics, computer science, and information theory with occasional articles in variety of fields, including philosophy and engineering. This study reconstructs his publication record and co-authorship network from verifiable bibliographic sources. An itemized bibliography maintained by the International Society for the Study of Information, the DBLP computer-science bibliography, and OpenAlex bibliographic catalogue were retrieved on 8 August 2026. The source layer contains 1,281 records. After identity screening, title and identifier matching, version control, author-name normalization, and deduplication, 837 canonical records remained; 805 were included in the core metrics. The core bibliography spans 1973-2025, contains 447 co-authored and 358 sole-authored records, and identifies 160 normalized co-authors. Pagination was available for 668 records (83.0%), yielding 16,890 known pages. 3 included records published posthumously were results of collaborations started together with Mark Burgin during his last years. For each co-author the dataset reports unique joint publications, known joint pages and coverage, first and last joint-publication years, inclusive joint-publication span, distinct publication years, publications per span-year, median known pages, distinct recorded venues and publication-type counts. Rankings are separate, unweighted, and reproducible from the retained publication-level data.The complete reference corpus underlying the calculations comprises all 805 included publications and is reproduced in Appendix C. Each reference is linked by a stable publication ID to the canonical and source-record tables retained with the preprint.

AI systems generate ethical tensions that cannot be addressed through principle-based guidance alone. This paper brings forward an Integrated Axiology–MCDA Framework for AI ethics that distinguishes intrinsic, instrumental, and relational values and uses multi-criteria analysis to operationalize value pluralism in practice. The framework structures ethical evaluation by making value commitments explicit, enabling transparent examination of trade-offs, and supporting context-sensitive judgment. A healthcare hyper-scenario with sensitivity analysis shows how different weight configurations influence the relative acceptability of diagnostic systems and clarifies the thresholds at which accuracy considerations outweigh privacy or fairness. Cross-domain applications in education, criminal justice, and finance further illustrate how domain-specific value tensions require distinct criteria sets and weighting structures. The analysis shows that ethical challenges in AI arise from genuine value pluralism. Explicit value classification enables more accountable decision making across the AI lifecycle.

Cognition and intelligence are central concepts in cognitive science, biology, philosophy of mind, and artificial intelligence, yet these disciplines offer conflicting accounts of what each of them means and how the two notions are related. In many accounts the two notions are used interchangeably, while in others intelligence is defined independently of cognitive processes. Dominant human-centered traditions identify cognition with mental processes associated with brains, whereas life-centered perspectives attribute cognitive capacities to all living systems. This article proposes a relational, life-centered, info-computational framework in which cognition is the ongoing autopoietic and sense-making organization of living systems, while intelligence is the degree of competence with which such organization achieves goal-directed problem solving under novelty, perturbation, and uncertainty. Cognition exists in degrees across living systems, from basal cellular sensing and regulation to increasingly complex cognitive organizations, while intelligence correspondingly appears in degrees in the ability to solve cognitive problems. Current artificial systems can exhibit engineered or derivative intelligence and may implement cognition-like functions, but they are not cognitive in the biological sense. The resulting framework clarifies how human-centered, life-centered, computational, and artificial intelligence can be related.

Cognition, sentience, intelligence, awareness, and mind are often treated as distinct phenomena that emerge only at higher levels of biological organization, typically associated with nervous systems or human cognition. However, empirical research increasingly demonstrates learning, memory, adaptive behavior, and goal-directed regulation across a wide range of living systems, including single cells, tissues, and organisms without brains. This paper proposes a unifying framework in which cognition is understood as an organizational property of living systems, grounded in information embodied in their physical structures and in their ongoing interactions with the environment. Within this info-computational (ICON) perspective, living systems engage in behavior, learning, and anticipation by dynamically transforming embodied information through distributed, physically realized processes that support viability and self-maintenance. These processes are present from the onset of life and become progressively more integrated and temporally extended with increasing biological organization. The framework provides explanatory continuity across biological scales and clarifies how complex forms of cognition, awareness, and mind arise as elaborations of basic life-regulatory dynamics. It generates empirically grounded, testable implications for basal cognition, developmental biology, and embodied artificial systems, in the domains such as morphogenetic regulation, bioelectric control, and embodied physical architectures where its implications can be tested.