Abstract human concepts—such as wisdom, love, justice, and consciousness—shape cultural norms, inform personal choices, and underpin ethical frameworks. Yet their semantic richness and inherent ambiguity, labeled “suitcase words” by Marvin Minsky [1], have defied precise measurement and reproducible modeling. This white paper introduces a systematic methodology to transform intangible concepts into structured, data-driven models that…

This white paper introduces the Simulated Wisdom System (SWS), a virtual training environment designed to cultivate layered consciousness in artificial agents by immersing them in structured, multimodal experiences [1]. SWS is built upon the Awareness Model’s hierarchical framework (U₀–U₂ unconscious; C₁–C₃ preconscious; C₄–C₇ conscious) and its state-dependent modulation of awareness (Awake, Asleep, Impaired) [1], [4]. By…

This paper proposes the transformation of the intuitive concept of wisdom into a quantitative, simulation-ready framework. By applying a layered approach to cognition, we develop a computable architecture that enables artificial and biological agents to be analyzed, compared, and evolved over time. This paper introduces a refined conceptual and computational framework for modeling human-like intelligence…

The emergence of Large Language Models (LLMs) has transformed how humans interact with knowledge. Rather than reading full texts, users now ask questions and expect concise, accurate answers — often delivered by AI systems that rely heavily on existing books, articles, and expert-authored content. Yet, this transformation has occurred largely without the consent or compensation…

This white paper introduces a novel layered model that delineates the relationships among the brain’s physiology, the mind’s cognitive processes, and the emergence of consciousness. To demonstrate the model’s real-world utility, three applied scenarios are explored: a virtual assistant interacting with a driver, a clinician coordinating with an AI in a hospital, and an asymmetric…

In increasingly complex domains such as healthcare, automation, and digital infrastructure, the ability to design systems that not only perform well but also explain their behavior has become critical. Traditional black-box approaches to modeling—especially those based on data-first machine learning pipelines—lack transparency, making them difficult to audit, debug, or trust. This lack of explainability presents…