PSYCHOMETRIC OPERATIONALIZATION OF FUNCTIONAL ROLES IN AUTONOMOUS MULTI-AGENT SYSTEMS BASED ON PERSONAMATRIX: AN AI TRANSITIONAL FIELD FRAMEWORK

Abstract

Background: Autonomous LLM agents increasingly operate in shared environments where behavior depends on interaction history, role allocation, local norms, authority, and group structure. Objective: This conceptual methods paper develops a psychometric operationalization of dynamic functional roles in autonomous multi-agent systems using PersonaMatrix-derived measurement principles without attributing human psychological ontology to AI. Methods: A targeted evidence synthesis (2019–2026) was conducted across AI psychometrics, dynamic multi-agent roles, collective-field modeling, norm emergence, trajectory prediction, and anthropomorphism. Peer-reviewed sources were prioritized, while current OpenAI, METR/Redwood, Anthropic, and Moltbook materials were used as primary or emerging evidence for recent agent ecosystems. Results: The framework separates output-level stability (RSI, IDS, RCS) from role-level and field-level constructs. It proposes the Functional Role Consistency Index, Role Transition Index, Agent–Field Differentiation Index, Collective Utility Index, Norm Conflict Index, Regulatory Closure Index, and Field Role Composition Vector. Three falsifiable hypotheses test whether role variables add out-of-sample predictive value for agent behavior, role transitions, and future field states. Conclusion: PersonaMatrix is positioned as a measurement architecture rather than an AI personality diagnosis. The archetypal-role hypothesis is supported only if role coding is reliable, improves prediction beyond non-archetypal baselines, and generalizes to unseen agents and independent multi-agent environments.