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Canonical Trajectory

Module: hours_eoh/core/trajectory.py

Provides the canonical physical-state reference at each ε — a reference frame for arc testing, not a measurement of any real economy — and ε derivation utilities.

canonical_physical_state(epsilon) → dict

Returns the physical state on the reference trajectory at a given ε. Used to test functions at specific ε without a full simulation, and as the baseline a simulated trajectory's divergence is measured against.

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 reference arc. Real simulations track actual capital stock, ecosystem health, etc. Divergence from canonical is the point of modeling.

canonical_age_distribution(epsilon) → dict[str, float]

Age distribution on the canonical arc. Independent of ε: the former drift from children toward elders as ε rises is retired, and epsilon is kept only so existing callers do not break.

compute_epsilon(machine_eoh_fulfilled, total_eoh_collective_potential) → 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_fulfilled=1.5e9,
                          total_eoh_collective_potential=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

Canonical capital stock at ε from an ε=0 baseline. Not equivalent to canonical_physical_state(ε)["capital_stock_teh"] — the two answer different questions; the function's docstring gives the distinction.