Towards an Ideometrics-Based Understanding of Consciousness, Time, Space and Dreams
I. Rudan
May 30, 2026
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Key points | Proposes that consciousness, through ideometric processes, reduces informational entropy of possible futures, with attractiveness as a consciously experienced valuation, and that subjective time, space, and dreaming are intertwined with consciousness in this framework. |
Abstract
From an ideometrics-based perspective, consciousness may reduce the informational entropy of many randomly possible future outcomes through ideometric processes. Consciousness enables a system to internally simulate alternative futures and then voluntarily act, based on ideometric processes, towards realising preferred states in external reality. This may explain why most humans gravitate towards futures that minimise threat and maximise survival, reproduction, safety and well-being. Ideometrics typically uses three fundamental criteria: attractiveness, feasibility and potential impact of many competing ideas. Feasibility and potential impact can, in principle, be computed by non-conscious systems, including artificial intelligence (AI). However, attractiveness may represent the consciously and emotionally experienced valuation of possible futures. Feasibility may have appeared first during evolution, while potential impact required predictive processing, and consciousness added subjective attractiveness to many alternative futures. Within this framework, subjective sense of time may be intertwined with consciousness, providing causal relating and internal ordering to external changes perceived by the senses. Time may require conscious beings to have a meaning, while consciousness may require the subjective sense of time to have a meaning. Space, in turn, provides the structured field in which ideas can acquire causal impact across nested scales. Dreaming may represent remnants of earlier evolutionary stages of internal modelling.