EOH Dynamics¶

Module: hours_eoh/core/eoh_dynamics.py

Time-evolution of EOH obligations: compounding from deferred maintenance, regenerative labor offsets, investment ranking, and paydown schedules.

Deferred Maintenance¶

`deferred_eoh(accumulated_deferred, current_eoh, fulfillment_rate, p)` → `float`¶

Net change in deferred EOH over a period: current_eoh × (1 − fulfillment_rate) minus decay from previous deferrals.

`eoh_compounding(deferred_eoh, compounding_rate, years, p)` → `float`¶

Models non-linear compounding of deferred EOH. A neglected roof does not need five years of routine maintenance — it needs replacement. Behavior is discontinuous, not smooth like monetary interest.

Not interest

EOH compounding is physics, not a social convention. It generates obligation without creating TEH. No party benefits from the compounding; all parties pay through degraded systems.

`compounding_profile(deferred_eoh, years, p)` → `list[dict]`¶

Projects deferred EOH accumulation over a period.

`deferred_eoh_paydown(deferred_eoh, paydown_teh_per_year, epsilon, p)` → `list[dict]`¶

Models the paydown trajectory given a sustained fulfillment investment.

`update_deferred_from_fulfillment(deferred, fulfilled, p)` → `float`¶

Updates the deferred balance after a fulfillment event.

Regenerative Labor¶

`regenerative_offset(labor_teh, service_type, epsilon, p)` → `dict`¶

Quantifies the future EOH reduction from regenerative labor (soil enrichment, preventive maintenance) versus maintenance labor (current EOH fulfillment).

`regenerative_vs_maintenance_comparison(total_teh, split_fraction, epsilon, p)` → `dict`¶

Compares outcomes of allocating labor to regenerative vs. maintenance work over a planning horizon.

`eoh_reduction_ratio(asset_teh_cost, asset_eoh_eliminated, design_life, p)` → `float`¶

Ratio of EOH eliminated to EOH generated (maintenance burden) for a proposed investment. Values > 1.0 indicate a net EOH reduction — the case for building.

`regenerative_investment_required(target_eoh_reduction, years, epsilon, p)` → `float`¶

TEH investment required to achieve a target future EOH reduction through regenerative labor.

Investment Ranking¶

`rank_investment_candidates(candidates, epsilon, p)` → `list[dict]`¶

Ranks infrastructure investment candidates by their EOH reduction ratio. Highest-leverage investments first.

from hours_eoh.core.eoh_dynamics import rank_investment_candidates

candidates = [
    {"label": "water treatment", "teh_cost": 50000, "eoh_eliminated": 80000, "design_life": 40},
    {"label": "road resurfacing", "teh_cost": 10000, "eoh_eliminated": 5000, "design_life": 10},
]
ranked = rank_investment_candidates(candidates, epsilon=0.40, p=p)

`optimal_investment(budget_teh, candidates, epsilon, p)` → `dict`¶

Selects the optimal set of investments within a TEH budget to maximize EOH reduction.

`maintenance_strategy_compare(asset, strategies, epsilon, p)` → `dict`¶

Compares maintenance strategies (preventive vs. reactive vs. deferred) over a planning horizon in TEH cost and EOH outcome.

EOH Dynamics¶

Deferred Maintenance¶

deferred_eoh(accumulated_deferred, current_eoh, fulfillment_rate, p) → float¶

eoh_compounding(deferred_eoh, compounding_rate, years, p) → float¶

compounding_profile(deferred_eoh, years, p) → list[dict]¶

deferred_eoh_paydown(deferred_eoh, paydown_teh_per_year, epsilon, p) → list[dict]¶

update_deferred_from_fulfillment(deferred, fulfilled, p) → float¶

Regenerative Labor¶

regenerative_offset(labor_teh, service_type, epsilon, p) → dict¶

regenerative_vs_maintenance_comparison(total_teh, split_fraction, epsilon, p) → dict¶

eoh_reduction_ratio(asset_teh_cost, asset_eoh_eliminated, design_life, p) → float¶

regenerative_investment_required(target_eoh_reduction, years, epsilon, p) → float¶