EOH Generation¶

Module: hours_eoh/core/eoh_generation.py

Pure physics — these functions take the actual physical state of the civilization and return entropy obligations. ε is not a primary input here; these functions measure what entropy demands, not who fulfills it.

Four Domain Functions¶

`personal_eoh(population, age_distribution, ..., p)` → `float`¶

Entropy obligation from human bodies — biological needs across the population.

Inputs: population, age distribution, p: EohParams

Arc: At ε = 0 this domain consumes nearly all labor. The physical obligation never disappears — only who (or what) fulfills it changes.

`infrastructure_eoh(capital_stock_teh, capital_age_ratio, ..., p)` → `float`¶

Entropy obligation from built systems — maintenance burden of the capital stock.

Inputs: capital stock in TEH, capital age ratio (older stock generates more EOH), monitoring capability, p

Arc: Grows as the capital stock expands. Drives increasing stewardship obligation at high ε.

`ecological_eoh(ecosystem_health, monitoring_capability, ..., p)` → `float`¶

Entropy obligation from natural systems — what ecosystem degradation demands.

Inputs: ecosystem health index, monitoring capability, p

Arc: Independent of automation level. Compounding consequences when neglected.

`knowledge_eoh(knowledge_base_size, knowledge_complexity_per_unit, ..., p)` → `float`¶

Entropy obligation from information systems — skill atrophy, institutional memory, standard drift.

Arc: Grows monotonically with ε; becomes the dominant domain at high automation.

`total_eoh(**physical_state, p)` → `dict`¶

Aggregate entropy obligation across all four domains.

from hours_eoh.core.eoh_generation import total_eoh
from hours_eoh.core.trajectory import canonical_physical_state

state = canonical_physical_state(0.40)
eoh = total_eoh(**state, p=p)
# Returns: {personal, infrastructure, ecological, knowledge, total}

Supporting Functions¶

Function	Returns	Description
`ecological_eoh_breakdown(...)`	`dict`	Decompose ecological EOH by service category
`domain_labor_requirements(eoh_dict, epsilon, p)`	`dict`	Human labor requirements per domain
`eoh_to_essential_domains(total_eoh, epsilon, p)`	`dict`	EOH allocation for Condition IV competency modeling
`epsilon_delta_sensitivity(epsilon, delta, p)`	`dict`	Sensitivity of EOH totals to Δε at a given point

Design invariant

EOH generation functions must not use ε to proxy physical assumptions. If ε is accepted as an optional backward-compat parameter, it must call canonical_physical_state(epsilon) to derive physical state — it must never use ε directly inside EOH calculation logic. See Extending the Library.

EOH Generation¶

Four Domain Functions¶

personal_eoh(population, age_distribution, ..., p) → float¶

infrastructure_eoh(capital_stock_teh, capital_age_ratio, ..., p) → float¶

ecological_eoh(ecosystem_health, monitoring_capability, ..., p) → float¶

knowledge_eoh(knowledge_base_size, knowledge_complexity_per_unit, ..., p) → float¶

total_eoh(**physical_state, p) → dict¶