Chainarong Amornbunchornvej

An agent must act on the situation before it, learn what it cannot yet represent, and model other agents well enough to coordinate. These faculties are usually realized by separate mechanisms, yet they share a failure mode: the situation can exceed what the agent can currently represent, and the honest response is then a principled refusal that says what was missing. We develop a small cognitive architecture in which these limits arise from a single quantity. An Interpretation-Decision Unit (IDU) interprets a content vector through a family of regimes, local representational frames with private bases, and decides which actions the interpretation licenses. A scalar residual of the content against the active regimes' representational scope drives the unit. Low residual with a clean licensing emits an action. Otherwise, the unit re-interprets, attempts a description-length-justified expansion of its representation, or halts with a typed, witnessed terminal. We prove the unit is total and deterministic: for any content and fixed configuration, it halts in finitely many bounded-cost steps with a unique terminal witness, so abstention carries its cause by construction. We then show, by binding the architecture's open parameters without changing its mechanics, that the same residual-against-scope constraint recovers three independently documented phenomena at three scopes: the typology of not-knowing (typed abstention), a misunderstanding between agents that is forced, localized to one shared concept, and structurally invisible to the agent committing it (bounded empathy), and prerequisite dependence in learning derived from a bounded focus window rather than posited (developmental prerequisites). Each instantiation is worked for both a natural and an artificial agent and states a falsifiable prediction, showing that the same constraint can model limits in both human and machine cognition. The account contributes a unification: three faculties as one constraint, and a notion of accountable abstention that is typed and witnessed by construction.

Generative artificial intelligence (GenAI) reshapes the informational conditions under which leadership operates. As the marginal cost of producing coherent outputs approaches zero, organizations face a structural shift: the primary constraint on reliable action moves from information scarcity to validation scarcity. Existing leadership theories emphasize influence, sensemaking, and coordination but largely treat informational legitimacy as given. I introduce Verification-Centric Leadership (VCL), a theory that reconceptualizes leadership as the governance of evidentiary admissibility under generative abundance. VCL is defined as a leadership approach in which leaders design, legitimize, and protect the processes that determine when claims become sufficiently warranted to justify coordinated action. The construct comprises three dimensions: admissibility boundary setting, which specifies ex ante standards of evidentiary sufficiency; institutionalized adversarial verification, which embeds protected challenge into decision routines; and epistemic maintenance, which sustains verification capacity over time. Integrating information-processing theory, high-reliability organizing, and research on trust in automation, I develop a multilevel model in which leader behaviors shape verification climate, calibrated reliance, and decision quality under equivocality, model opacity, and delayed feedback. I further formalize the theory through a capacity logic distinguishing generation intensity (G) from verification capacity (V), showing how leadership sustains organizational reliability by aligning claim production with validation throughput.

Contemporary social epistemology faces a critical challenge in explaining "deep disagreements" that defy resolution through evidence-sharing. While recent scholarship has turned to Wittgensteinian hinge epistemology and social ontology to address this, a unified framework connecting individual belief structure to macro-level social conflict is lacking. This paper proposes a three-dimensional geometric model of moral cognition defined by Negotiability (d), Target Scope (τ), and Actionability (α). We argue that deep disagreements are not merely epistemic failures but instances of structural incompatibility between "Normative Selves." Crucially, we demonstrate that this framework is scale-invariant: the Legitimacy Trilemma applies isomorphically to individual cognition, reframing cognitive dissonance as an internal boundary defense. We establish a limit result demonstrating that a normative system -- whether a community or a single mind -- cannot simultaneously maintain Universal Invariant Norms, acknowledge Internal Violations, and practice Quiet Tolerance. We conclude by reinterpreting epistemic injustice not as a vice, but as a structural "repair move" necessary to preserve identity in the face of deep disagreement.

The advancement of computational theory has historically bifurcated into the study of algorithms -- processes of effective transformation -- and the study of data structures. However, the representational preconditions required to make a problem solvable by a Turing-equivalent system remain under-theorized in comparison to computational complexity classes such as P and NP. This report formalizes the Hierarchical Representational Machine (HRM), a theoretical framework that stratifies representational resources into four distinct layers: Structured Input Space (L1), Equivalence and Measurement (L2), Formal Symbolic Representation (L3), and Admissibility Constraints (L4). By grounding these layers in Type-2 Theory of Effectivity (TTE), Representational Measurement Theory (RMT), and Constructive Type Theory, the HRM provides a rigorous classification system for computational tasks based on their minimal representational requirements (Req(T)). This treatise synthesizes formal definitions, cross-disciplinary research from cognitive science and formal verification, and detailed solvability analyses to demonstrate that representational hierarchy is orthogonal to, yet as critical as, time-space complexity in determining problem solvability.

This paper develops a geometric framework for modeling concepts, motivation, and influence across cognitively heterogeneous agents. Each agent is represented by a personalized value space, a vector space encoding the internal dimensions through which the agent interprets and evaluates meaning. Evaluative concepts are formalized as structured vectors -- abstract beings -- whose transmission is mediated by linear interpretation maps. An abstract being survives communication only if it avoids the null spaces of these maps, yielding a structural criterion for intelligibility, miscommunication, and concept death. Within this framework, I show how conceptual distortion, motivational drift, and the limits of mutual understanding arise from purely algebraic constraints. A central result -- the No-Null-Space Leadership Condition -- characterizes leadership as a property of representational reachability rather than persuasion or authority. More broadly, the model explains how abstract beings can propagate, mutate, or disappear as they traverse diverse cognitive geometries. The account unifies insights from conceptual spaces, social epistemology, and AI value alignment by grounding meaning preservation in structural compatibility rather than shared information or rationality. I argue that this cognitive-geometric perspective clarifies the epistemic boundaries of influence in both human and artificial systems, and offers a general foundation for analyzing conceptual dynamics across heterogeneous agents.

Concept learning becomes possible only when existing representations fail to account for experience. Most models of learning and inference, however, presuppose a fixed representational basis within which belief updating occurs. In this paper, I address a prior question: under what structural conditions can the representational basis itself expand in a principled and selective way? I propose a geometric framework in which conceptual growth is modeled as admissible basis extension evaluated under a Minimum Description Length (MDL) criterion. Experience, whether externally observed or internally simulated, is represented as vectors relative to a current conceptual subspace. Residual components capture systematic representational failure, and candidate conceptual extensions are restricted to low-rank, admissible transformations. I show that any MDL-accepted extension can be chosen so that its novel directions lie entirely within the residual span induced by experience, while extensions orthogonal to this span strictly increase description length and are therefore rejected. This yields a conservative account of imagination and conceptual innovation. Internally generated counterfactual representations contribute to learning only insofar as they expose or amplify structured residual error, and cannot introduce arbitrary novelty. I further distinguish representational counterfactuals--counterfactuals over an agent's conceptual basis--from causal or value-level counterfactuals, and show how MDL provides a normative selection principle governing representational change. Overall, the framework characterizes conceptual development as an error-driven, geometry-constrained process of basis extension, clarifying both the role and the limits of imagination in learning and theory change.