Information Physics: A Position-Dependent Framework for Conscious System Dynamics

Abstract

Information Physics proposes a theoretical and mathematical framework based on the observation that organized systems appear to be entropically constrained and systemically bounded. From this potentially fundamental condition, the framework explores how conscious agents might navigate these apparent constraints using time and information as primary tools. Following the lineage of Einstein’s relativity (physics depends on reference frame) and Nash’s game theory (optimal strategies depend on others’ actions), this framework treats consciousness, position, and lived experience as mathematical variables. The theory suggests three potentially irreducible operations—MOVE, JOIN, and SEPARATE—may represent the complete set of transformations available within bounded reality. The operation values (MOVE=1, JOIN=2, SEPARATE=3) reflect actual thermodynamic energy hierarchy, grounding the framework in physical law. These operations interact with an agent’s positional entropy (E), directional intent (V), and operational capacity (O) to generate measurable effects on system entropy, captured by the core equation:

SEC = O × V / (1 + E)

Building on the established equivalence between Shannon information entropy and thermodynamic entropy, the theory explores extending these foundations to potentially address cognitive and social dynamics, organizational change, innovation cycles, and large-scale civilizational evolution. Conscious Chaos, a recent theoretical extension, introduces time-sensitivity and perturbation dynamics into the model via:

dSEC/dt = O × V × f(E) × [1 + α·sin(ωt)]

This evolution function allows entropy trajectory modeling under stress, fatigue, or emergent alignment, integrating concepts from chaos theory, percolation thresholds, and attractor basins. Here f(E) = 1/(1+E) represents the entropy dampening function.

The theory potentially offers explanations for:

• Nash Equilibrium as entropic exhaustion (ΔSEC/ΔO ≈ 0)
• Mass extinction patterns (SEC generalist > specialist)
• Organizational collapse and crowd behavior (percolation + high-E systems)
• Innovation density (entropy-based analysis of historical disparities)
• Energetic cost of cognition and decision-making (Landauer’s principle + E)

Information Physics introduces a minimal viable modeling system using E, V, O, α, and ω to simulate conscious systems constrained by entropy. The framework potentially applies to neural networks, economic behavior, agent-based modeling, and planetary survivability patterns.

This framework represents an attempt to develop a cross-scale, observer-embedded mathematical theory of conscious systems navigating entropy.

The framework shares conceptual territory with established complexity science, particularly in areas of teleonomic matter, emergent behavior from constraint, and theorizing about theorizers—as articulated by researchers like David Krakauer at the Santa Fe Institute. These parallels suggest potential for productive dialogue between Information Physics and complexity science approaches.

While theoretical predictions align with observed patterns across multiple domains, systematic empirical validation remains necessary to establish the framework’s validity.

Proposed Fields of Interest: Complexity science, theoretical physics, thermodynamics, systems theory, cognitive science, AI/ML alignment, organizational behavior, evolutionary biology, civilizational modeling.