Changelog¶

All notable changes to this project will be documented here.

Format follows Keep a Changelog. Versions follow Semantic Versioning.

[Unreleased]¶

Added¶

Collective land-inventory system (hours_eoh/land/) - collective.py — Standard parcel schema and batch GUF calculator for a collective land inventory. compute_collective_guf(parcels, epsilon, median_income) loops all parcels through ground_use_fee(), applies soil-health credits, review-cycle caps, and income-linked subsidies per parcel, then aggregates via guf_trust_inflow(). Schema maps directly to geo-data pipeline column names for zero-friction integration with GeoJSON/CSV sources. - collective.make_urban_collective(parcel_count) — Synthetic dense-urban archetype (75 % residential_primary · 15 % commercial_retail · 5 % commercial_office · 5 % institutional). - collective.make_rural_collective(parcel_count) — Synthetic rural archetype (50 % agricultural_active · 20 % agricultural_fallow · 20 % residential_primary · 10 % conservation). - calibration.py — Rate and weight calibration tools. - guf_rate_calibration(inventory, target_guf_levy_ratio, population, epsilon) — Closed-form linear solve to find the use-coefficient multiplier k such that aggregate GUF ≈ target × levy_revenue. Binary-verifies with a sample run; reports converged flag. - guf_lvi_weight_sensitivity(inventory, epsilon, weight_variants) — Sweeps Location Value Index weight configurations to quantify how sensitive aggregate GUF is to sub-index weighting choices (centrality vs. transit vs. services vs. natural amenity). Ships five canonical variants; callers may supply their own. - land/__init__.py re-exports all four new public functions.

Multi-period automation→levy→GUF stress scenario - scenarios/guf_stress.automation_levy_guf_stress(parcel_inventory, epsilon_start, epsilon_end, n_periods, ...) — Period-by-period fiscal stress loop: as ε rises, levy revenue (derived from the EOH pipeline) falls, GUF tracks the Ψ(ε) bell curve, and the sufficiency guarantee cost evolves. Reports levy_peak_period, guf_peak_period, crossover_period (first period where GUF exceeds levy), first_insolvency, compensation_adequacy, and outcome ADEQUATE / PARTIAL / CRISIS.

Multi-period simulation scenarios (hours_eoh/scenarios/) - long_run.py — canonical_arc_trajectory, trust_depletion_stress, automation_transition_trajectory: three functions that call run_simulation() and return multi-period trajectories with inflection-point detection and stressor profiles. - indust_overshoot.py — indust_overshoot_baseline, indust_recovery_trajectory: models the industrial-overshoot archetype (high capital age, degraded ecosystem, large deferred-ecological backlog) and recovery pathways. - shocks.py additions — labor_income_shock (income-fraction shock with solvency delta) and compound_shock (combines ecological, demographic, and automation shocks; combined outcome is always ≥ worst individual outcome).

Simulation engine extensions (hours_eoh/core/simulation.py) - simulate_period(..., workforce_epsilon_decay: bool = False) — Optional parameter; when True, workforce_fraction shrinks proportionally with rising ε. Default False preserves backward compatibility. - simulate_period(..., guf_net_inflow: float | None = None) — Optional GUF land-fee revenue injected into the Trust each period. - run_simulation() and the eoh simulate CLI forward both new parameters.

GUF calibration fix - GUF_USE_* constants in data.py multiplied by 100 (e.g. residential_primary 0.10 → 10.0 TEH/SLU/yr, commercial_retail 0.30 → 30.0). At ε = 0.40 with a 1 M-population territory (~420 k parcels), aggregate GUF is now co-equal with levy revenue — the design target in the mission statement. - guf_fiscal_integration() labor-income proxy replaced: was trust_balance × 0.5 (35× inflated); now uses eoh_to_teh_pipeline(epsilon, population)["teh_created"].

CLI (utils/) - guf inventory calculate --parcels FILE [--epsilon ε] [--median-income TEH] — Batch GUF from a JSON parcel file; prints aggregate summary table or JSON. - guf inventory sweep --parcels FILE [--epsilon-start ε] [--epsilon-end ε] [--steps N] — Sweeps aggregate GUF across the ε arc. - guf inventory stress --parcels FILE [--epsilon-start ε] [--epsilon-end ε] [--periods N] — Multi-period automation→GUF stress with per-period trajectory table. - scenario command: 10 new scenarios wired — canonical-arc, trust-depletion, automation-transition, indust-baseline, indust-recovery, labor-shock, compound-shock, guf-integration, guf-writedown, guf-sweep. - simulate command: --workforce-decay and --guf-inflow TEH flags.

Documentation and diagrams - MkDocs GitHub Pages site deployed at https://hours-source.github.io/HOURS/. - Five GUF Mermaid diagrams: calculation flow, LVI component weights, Ψ(ε) epsilon arc, Trust fund flow, and ecological write-down pathways. Rendered SVGs in docs/images/.

Fixed¶

eco-collapse-1 (closed) — Ecological collapse is now handled via the GUF layer (land/guf.py §9) rather than direct TEH destruction. Two pathways: restoration (V_s baselines reset to recovery target, revenue maintained) and abandonment (rebuilding surcharge R_b amortised over 50 years). Preventive monitoring via eoh_accumulation_warning() (§9.8). research/writedown.py re-exports with rationale.
guf_net_inflow guard in simulate_period() corrected from > 0.0 to is not None so negative inflows (subsidy-heavy periods) are no longer silently dropped.
WORKFORCE_FRACTION_MIN named constant added to data.py; replaces anonymous 0.05 literal in simulation.

Changed¶

Test count: 1040 → 1169 (129 new tests across tests/land/, tests/scenarios/).

0.1.0 — 2026-05-06¶

Initial public release of the HOURS EOH framework.

Added¶

Core package (hours_eoh/core/) - trajectory.py — Canonical arc, ε derivation, canonical_physical_state(), compute_epsilon() - eoh_generation.py — Four EOH domain functions: personal_eoh, infrastructure_eoh, ecological_eoh, knowledge_eoh, total_eoh - registration.py — Per-domain sigmoid admission curves; personal_eoh_registration_share (near-zero at ε=0), total_registration_share (labor composite) - eoh_fulfillment.py — EOH → TEH pipeline: human_eoh_share, human_eoh_per_domain, registered_eoh, eoh_to_teh_pipeline - multipliers.py — Condition II multiplier band and tier logic - fiscal.py — Fiscal architecture: levies, stewardship and ecological allocations (co-equal), sufficiency guarantee, trust management, care stipend - prices.py — Price dynamics tied to human labor content: basket_price, purchasing_power, floor_purchasing_power - capital.py — Asset and human capital lifecycle: make_asset, birth_event, maturation_update, death_event, writedown_trigger, execute_writedown - eoh_dynamics.py — Time-evolution: eoh_compounding, asset_condition_trajectory, regenerative_offset, eoh_reduction_ratio - population.py — Population structure, age distribution, demographic events (aging) - workforce.py — Workforce lifecycle, domain headcount, competency reserve, apply_death_redistribution - conditions.py — Structural Conditions I–IV enforcement: balance_check, condition_iii_balance_growth_check - dashboard.py — Condition monitors and EOH/fiscal health indicators - civilization.py — Endogenous ε from capital stock: civilization_epsilon, CAPITAL_MACHINE_PROFILES - simulation.py — Period simulation engine: make_economy_state, simulate_period

Land module (hours_eoh/land/) - guf.py — Ground Use Fee framework (NLSA TM-0042, 7th Ed.): 14 functions from epsilon_scaling through guf_trust_inflow

Scenarios (hours_eoh/scenarios/) - sweep.py — epsilon_sweep: arc coherence check with fiscal solvency at every ε - shocks.py — automation_failure_shock, demographic_shock, ecological_eoh_spike - maintenance.py — deferred_maintenance_crisis, care_registration_delay - recovery.py — maintenance_recovery_schedule, minimum_fulfillment_for_recovery - sensitivity.py — fiscal_parameter_sweep, eoh_arc_sensitivity, epsilon_delta_sensitivity

Research (hours_eoh/research/) - investment.py — rank_investment_candidates, optimal_investment, eoh_reduction_ratio (re-exported from core) - writedown.py — Placeholder: ecological write-down for collapse scenarios (eco-collapse-1, future work)

Test suite - 1040 tests across 20 test files covering the full arc from ε = 0 to ε = 0.99 - Phase tests (1–14) cover the complete development arc - Module tests cover trajectory, civilization ε, GUF, and all scenario modules

Design invariants established¶

ε is a derived observable, not a policy lever or generation-function input
EOH generation takes physical state; EOH fulfillment takes ε
data.py is the single source of truth for all named constants
Per-domain registration split: personal uses demand sigmoid; non-personal use labor composite
Ecological and stewardship allocations are co-equal (neither is residual)
Zero interest (Condition III): balances grow only through labor
All functions verified at ε = 0 (subsistence) and ε = 0.99 (effective post-scarcity)

Known gaps¶

eco-collapse-1 — Ecological write-down for collapse scenarios not yet implemented (placeholder in research/writedown.py)