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 encoding sensory inputs, procedural tasks, and reflective narratives into these layers, SWS enables progressive development of perception, attention, and metacognition.
Central to SWS is the Wisdom Equation (W_total = Σ (wₖ × Iₖ^Cₖ)), wherein I denotes the depth of the agent’s processing architecture and C represents its degree of conscious integration [8]. A reinforcement-based trainer module rewards behaviors that maximize W, guiding agents toward balanced engagement across layers [8].
The system comprises three interconnected modules: the Scenario Engine, which creates parametrizable environments (automotive, clinical, social) tagged by cognitive layer [1], [7]; the Multilayer Collector, capturing agent responses at sensory, algorithmic, and reflective strata [4]; and the Wisdom Trainer, applying reward signals based on W to adjust both structural depth and activation thresholds Θ(σᵢ–θᵢ(S)) of each layer [1], [4].
While no empirical results exist yet, it is hypothesized that agents trained within SWS could reduce distraction errors, improve clinical decision-making accuracy, and enhance educational content retention by systematically optimizing cognition through layer-specific training.
The paper concludes by outlining both current applications in neuroscience research, cognitive robotics, and adaptive interfaces [6], [7], as well as speculative futures such as brain–machine symbiosis, collective artificial wisdom, and ethically aligned conscious agents [10].