Canonical Trajectory¶
Module: hours_eoh/core/trajectory.py
Provides the canonical physical-state reference at each ε (the ideal arc for a civilization investing optimally), and ε derivation utilities.
canonical_physical_state(epsilon) → dict¶
Returns the ideal-arc physical state at a given ε. Used as the reference baseline for arc testing.
from hours_eoh.core.trajectory import canonical_physical_state
state = canonical_physical_state(0.40)
# Returns: {capital_stock_teh, capital_age_ratio, ecosystem_health,
# monitoring_capability, age_distribution, knowledge_base_size,
# knowledge_complexity_per_unit}
Real simulations pass actual state
canonical_physical_state(ε) is the ideal-arc reference for a civilization that invests optimally. Real simulations track actual capital stock, ecosystem health, etc. Divergence from canonical is the point of modeling.
canonical_age_distribution(epsilon) → dict[str, float]¶
Ideal-arc age distribution at ε.
compute_epsilon(machine_eoh, total_eoh_value) → float¶
Derives ε from machine EOH and total EOH: ε = machine_eoh / total_eoh, clamped to [0.0, 0.99].
from hours_eoh.core.trajectory import compute_epsilon
epsilon = compute_epsilon(machine_eoh=1.5e9, total_eoh_value=2.5e9) # → 0.60
Currently ε is often set exogenously. The architecture supports endogenous ε when machine capacity is modeled from capital stock — see civilization.py in Workforce & ε Derivation.
effective_capital_from_epsilon(capital_stock_at_eps0, epsilon) → float¶
Expected capital stock at ε given starting capital at ε=0.