babylon.config.defines

Game defines for centralized coefficient configuration.

This module provides the GameDefines model which extracts hardcoded values from systems into a single, configurable location. This enables: 1. Easier calibration of game balance 2. Scenario-specific coefficient overrides 3. Clear documentation of magic numbers

Sprint: Paradox Refactor Phase 1

Classes

AidDefines(**data)	AID verb coefficients.
ArcGISDefines(**data)	ArcGIS organization and host configuration for external data sources.
BehavioralDefines(**data)	Behavioral economics coefficients.
BifurcationDefines(**data)	Bifurcation Topology Analysis coefficients (Feature 033).
CarceralDefines(**data)	Carceral equilibrium coefficients (Terminal Crisis Dynamics).
ClassDynamicsDefines(**data)	Class dynamics coefficients (FRED DFA-derived, Feature 016).
ClassSystemDefines(**data)	Unified class system coefficients (Feature 038).
CommunityDefines(**data)	Hypergraph community layer coefficients (Feature 022).
ConsciousnessDefines(**data)	Consciousness drift coefficients.
ContradictionFieldDefines(**data)	Contradiction field topology coefficients (Feature 002).
CrisisDefines(**data)	Crisis and Devaluation Mechanics coefficients (Feature 018).
DispossessionDefines(**data)	Dispossession event intensity weights (Feature 021, FR-004/FR-005).
EconomyDefines(**data)	Economic system coefficients.
EdgeTransitionDefines(**data)	Edge mode transition threshold values (Feature 002, FR-010).
EndgameDefines(**data)	Configuration for endgame detection thresholds (Slice 1.6).
ExternalDataDefines(**data)	External data source configuration.
GameDefines(**data)	Centralized game coefficients extracted from hardcoded values.
InfraTerrainDefines(**data)	Terrain classification and biocapacity coefficients (Feature 036).
InfrastructureDefines(**data)	Infrastructure capacity and internet operation coefficients (Feature 036).
InitialDefines(**data)	Initial condition coefficients.
InstitutionDefines(**data)	Institution Base Model coefficients (Feature 040).
LifecycleDefines(**data)	D-P-D' Lifecycle Circuit coefficients (Feature 030).
MetabolismDefines(**data)	Metabolic rift coefficients (Slice 1.4 - Ecological Limits).
MobilizeDefines(**data)	Configuration for MOBILIZE verb organizational actions.
MoveDefines(**data)	MOVE verb coefficients.
NegotiateDefines(**data)	NEGOTIATE verb coefficients.
OODADefines(**data)	OODA Loop System tunable coefficients (Feature 032).
OrganizationDefines(**data)	Organization system tunable coefficients (Feature 031).
PrecisionDefines(**data)	Numerical precision configuration for deterministic simulation.
RentCircuitDefines(**data)	Parameters for ground rent extraction in Volume III equalization (Feature 043).
ReserveArmyDefines(**data)	Reserve army of labor coefficients (Feature 021, FR-001/FR-002).
ServicesDefines(**data)	ArcGIS FeatureServer service names and layers.
SolidarityDefines(**data)	Solidarity and consciousness transmission coefficients.
StateApparatusAIDefines(**data)	State Apparatus AI coefficients (Feature 039).
StruggleDefines(**data)	Struggle dynamics coefficients (Agency Layer - "George Floyd" Dynamic).
SurvivalDefines(**data)	Survival calculus coefficients.
TensionDefines(**data)	Tension dynamics coefficients.
TerritoryDefines(**data)	Territory dynamics coefficients.
TimescaleDefines(**data)	Simulation timescale configuration for weekly ticks.
TopologyDefines(**data)	Phase transition coefficients for solidarity network analysis.
VitalityDefines(**data)	Mortality coefficients for Mass Line population dynamics.
WorkingDayDefines(**data)	Working day characterization thresholds (Feature 021, FR-007/FR-008).

class babylon.config.defines.CrisisDefines(**data)

Bases: BaseModel

Crisis and Devaluation Mechanics coefficients (Feature 018).

Configures the multi-period crisis detector, phased amplification, bifurcation risk assessment, and wage compression mechanics.

See also

babylon.economics.tick.types: CrisisPhase, CrisisState specs/018-crisis-devaluation-mechanics/spec.md: FR-023

Parameters:

• crisis_period_ticks (Annotated[int, Ge(ge=1), Le(le=52)])

• r_threshold (Annotated[float, Gt(gt=0), Le(le=1)])

• n_consecutive (Annotated[int, Ge(ge=1), Le(le=20)])

• m_recovery (Annotated[int, Ge(ge=1), Le(le=20)])

• r_cap (Annotated[int, Ge(ge=1), Le(le=52)])

• hysteresis_coefficient (Annotated[float, Gt(gt=0), Lt(lt=1)])

• wage_compression_rate (Annotated[float, Ge(ge=0), Le(le=0.5)])

• wage_compression_floor_ratio (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• bifurcation_solidarity_weight (Annotated[float, Ge(ge=0)])

• bifurcation_burden_weight (Annotated[float, Ge(ge=0)])

• class_burden_epsilon (Annotated[float, Gt(gt=0), Le(le=0.1)])

• bifurcation_event_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• dispossession_cascade_milestones (list[float])

• stagnation_credit_growth (Annotated[float, Ge(ge=0), Le(le=0.5)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

crisis_period_ticks: int

r_threshold: float

n_consecutive: int

m_recovery: int

r_cap: int

hysteresis_coefficient: float

wage_compression_rate: float

wage_compression_floor_ratio: float

bifurcation_solidarity_weight: float

bifurcation_burden_weight: float

class_burden_epsilon: float

bifurcation_event_threshold: float

dispossession_cascade_milestones: list[float]

stagnation_credit_growth: float

class babylon.config.defines.EconomyDefines(**data)

Bases: BaseModel

Economic system coefficients.

Parameters:

• extraction_efficiency (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• comprador_cut (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• base_labor_power (Annotated[float, Ge(ge=0)])

• super_wage_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• superwage_multiplier (Annotated[float, Ge(ge=0)])

• superwage_ppp_impact (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• initial_rent_pool (Annotated[float, Ge(ge=0)])

• pool_high_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• pool_low_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• pool_critical_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• min_wage_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• max_wage_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• subsidy_conversion_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• subsidy_trigger_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• shadow_wage_hourly (Annotated[float, Ge(ge=0)])

• negligible_rent (Annotated[float, Ge(ge=0)])

• negligible_subsidy (Annotated[float, Ge(ge=0)])

• base_subsistence (Annotated[float, Ge(ge=0), Le(le=0.5)])

• death_threshold (Annotated[float, Ge(ge=0)])

• trpf_coefficient (Annotated[float, Ge(ge=0.0), Le(le=0.01)])

• rent_pool_decay (Annotated[float, Ge(ge=0.0), Le(le=0.01)])

• bribery_wage_delta (Annotated[float, Ge(ge=-1.0), Le(le=1.0)])

• austerity_wage_delta (Annotated[float, Ge(ge=-1.0), Le(le=1.0)])

• iron_fist_repression_delta (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• crisis_wage_delta (Annotated[float, Ge(ge=-1.0), Le(le=1.0)])

• crisis_repression_delta (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• bribery_tension_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• iron_fist_tension_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• trpf_efficiency_floor (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

extraction_efficiency: float

comprador_cut: float

base_labor_power: float

super_wage_rate: float

superwage_multiplier: float

superwage_ppp_impact: float

initial_rent_pool: float

pool_high_threshold: float

pool_low_threshold: float

pool_critical_threshold: float

min_wage_rate: float

max_wage_rate: float

subsidy_conversion_rate: float

subsidy_trigger_threshold: float

shadow_wage_hourly: float

negligible_rent: float

negligible_subsidy: float

base_subsistence: float

death_threshold: float

trpf_coefficient: float

rent_pool_decay: float

bribery_wage_delta: float

austerity_wage_delta: float

iron_fist_repression_delta: float

crisis_wage_delta: float

crisis_repression_delta: float

bribery_tension_threshold: float

iron_fist_tension_threshold: float

trpf_efficiency_floor: float

class babylon.config.defines.SurvivalDefines(**data)

Bases: BaseModel

Survival calculus coefficients.

Parameters:

• steepness_k (Annotated[float, Gt(gt=0)])

• default_subsistence (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• default_organization (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• default_repression (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• revolution_threshold (Annotated[float, Gt(gt=0)])

• repression_base (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

steepness_k: float

default_subsistence: float

default_organization: float

default_repression: float

revolution_threshold: float

repression_base: float

class babylon.config.defines.VitalityDefines(**data)

Bases: BaseModel

Mortality coefficients for Mass Line population dynamics.

The Grinding Attrition Formula models probabilistic mortality based on intra-class inequality: - Even with sufficient average wealth, high inequality kills marginal workers - Deaths reduce population → per-capita wealth increases → equilibrium

Formula:

effective_wealth_per_capita = wealth / population marginal_wealth = effective_wealth_per_capita × (1 - inequality × inequality_impact) mortality_rate = max(0, (consumption_needs - marginal_wealth) / consumption_needs) deaths = floor(population × mortality_rate × base_mortality_factor)

Malthusian Correction: Population decline increases per-capita wealth, reducing future mortality rates and creating equilibrium dynamics.

Parameters:

• base_mortality_factor (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• inequality_impact (Annotated[float, Ge(ge=0.0), Le(le=2.0)])

• attrition_base_factor (Annotated[float, Ge(ge=0.0), Le(le=2.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

base_mortality_factor: float

inequality_impact: float

attrition_base_factor: float

class babylon.config.defines.SolidarityDefines(**data)

Bases: BaseModel

Solidarity and consciousness transmission coefficients.

Parameters:

• scaling_factor (Annotated[float, Ge(ge=0.0), Le(le=2.0)])

• activation_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• mass_awakening_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• negligible_transmission (Annotated[float, Ge(ge=0)])

• superwage_impact (Annotated[float, Ge(ge=0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

scaling_factor: float

activation_threshold: float

mass_awakening_threshold: float

negligible_transmission: float

superwage_impact: float

class babylon.config.defines.BehavioralDefines(**data)

Bases: BaseModel

Behavioral economics coefficients.

Parameters:

loss_aversion_lambda (Annotated[float, Gt(gt=0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

loss_aversion_lambda: float

class babylon.config.defines.TensionDefines(**data)

Bases: BaseModel

Tension dynamics coefficients.

Parameters:

• accumulation_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• aspect_flip_threshold (Annotated[float, Ge(ge=0)])

• antagonistic_intensity_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• resolution_intensity_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

accumulation_rate: float

aspect_flip_threshold: float

antagonistic_intensity_threshold: float

resolution_intensity_threshold: float

class babylon.config.defines.ConsciousnessDefines(**data)

Bases: BaseModel

Consciousness drift coefficients.

Parameters:

• sensitivity (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• decay_lambda (Annotated[float, Gt(gt=0)])

• routing_scale (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• agitation_decay_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• exploitation_sensitivity (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• rent_decline_sensitivity (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• reproduction_visibility_coefficient (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• repression_backfire (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• rent_opacity_factor (Annotated[float, Ge(ge=0)])

• agitation_consumption_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• liberal_drift_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• educate_base_effect (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• agitation_education_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• education_pressure_decay (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

sensitivity: float

decay_lambda: float

routing_scale: float

agitation_decay_rate: float

exploitation_sensitivity: float

rent_decline_sensitivity: float

reproduction_visibility_coefficient: float

repression_backfire: float

rent_opacity_factor: float

agitation_consumption_rate: float

liberal_drift_rate: float

educate_base_effect: float

agitation_education_threshold: float

education_pressure_decay: float

class babylon.config.defines.TerritoryDefines(**data)

Bases: BaseModel

Territory dynamics coefficients.

Parameters:

• heat_decay_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• high_profile_heat_gain (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• eviction_heat_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• rent_spike_multiplier (Annotated[float, Gt(gt=0)])

• displacement_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• heat_spillover_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• clarity_profile_coefficient (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• concentration_camp_decay_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• displacement_priority_mode (str)

• elimination_rent_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• elimination_tension_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• containment_rent_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• containment_tension_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

heat_decay_rate: float

high_profile_heat_gain: float

eviction_heat_threshold: float

rent_spike_multiplier: float

displacement_rate: float

heat_spillover_rate: float

clarity_profile_coefficient: float

concentration_camp_decay_rate: float

displacement_priority_mode: str

elimination_rent_threshold: float

elimination_tension_threshold: float

containment_rent_threshold: float

containment_tension_threshold: float

class babylon.config.defines.TopologyDefines(**data)

Bases: BaseModel

Phase transition coefficients for solidarity network analysis.

The topology system tracks phase transitions in class solidarity: - Gaseous: Atomized, no collective action capacity - Transitional: Solidarity building, weak ties forming - Liquid: Mass movement (percolation but low cadre density) - Solid: Vanguard party (percolation with high cadre density)

Parameters:

• gaseous_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• condensation_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• vanguard_density_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• brittle_multiplier (Annotated[float, Ge(ge=1.0), Le(le=10.0)])

• solidarity_sympathizer_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• solidarity_cadre_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• resilience_removal_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• resilience_survival_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

gaseous_threshold: float

condensation_threshold: float

vanguard_density_threshold: float

brittle_multiplier: float

solidarity_sympathizer_threshold: float

solidarity_cadre_threshold: float

resilience_removal_rate: float

resilience_survival_threshold: float

class babylon.config.defines.MetabolismDefines(**data)

Bases: BaseModel

Metabolic rift coefficients (Slice 1.4 - Ecological Limits).

The Metabolism System tracks the widening rift between extraction and regeneration: - Biocapacity regeneration and depletion - ECOLOGICAL_OVERSHOOT event when consumption exceeds biocapacity

Parameters:

• entropy_factor (Annotated[float, Gt(gt=1.0), Le(le=3.0)])

• overshoot_threshold (Annotated[float, Gt(gt=0.0), Le(le=2.0)])

• max_overshoot_ratio (Annotated[float, Gt(gt=0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

entropy_factor: float

overshoot_threshold: float

max_overshoot_ratio: float

class babylon.config.defines.StruggleDefines(**data)

Bases: BaseModel

Struggle dynamics coefficients (Agency Layer - “George Floyd” Dynamic).

The Struggle System gives political agency to oppressed classes by modeling: - The Spark: State violence (EXCESSIVE_FORCE) triggers insurrection - The Combustion: Spark + High Agitation + Low P(S|A) = UPRISING - The Result: Uprisings destroy wealth but build solidarity infrastructure

George Jackson Bifurcation (Power Vacuum): When the Comprador becomes insolvent, a power vacuum occurs. The outcome depends on the Periphery Proletariat’s revolutionary capacity: - capacity >= jackson_threshold: Revolutionary Offensive - capacity < jackson_threshold: Fascist Revanchism

Parameters:

• spark_probability_scale (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• resistance_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• wealth_destruction_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• solidarity_gain_per_uprising (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• consciousness_solidarity_boost (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• jackson_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• revolutionary_agitation_boost (Annotated[float, Ge(ge=0.0), Le(le=2.0)])

• fascist_identity_boost (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• fascist_acquiescence_boost (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

spark_probability_scale: float

resistance_threshold: float

wealth_destruction_rate: float

solidarity_gain_per_uprising: float

consciousness_solidarity_boost: float

jackson_threshold: float

revolutionary_agitation_boost: float

fascist_identity_boost: float

fascist_acquiescence_boost: float

class babylon.config.defines.AidDefines(**data)

Bases: BaseModel

AID verb coefficients.

Parameters:

• aid_efficiency (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• aid_cl_cost (float)

• aid_solidarity_increment (Annotated[float, Ge(ge=0)])

• solidaristic_threshold (Annotated[float, Ge(ge=0)])

• education_threshold_for_solidarity (Annotated[float, Ge(ge=0)])

• agitation_relief_per_unit (Annotated[float, Ge(ge=0)])

• economism_warning_threshold (Annotated[float, Ge(ge=0)])

aid_efficiency: float

aid_cl_cost: float

aid_solidarity_increment: float

solidaristic_threshold: float

education_threshold_for_solidarity: float

agitation_relief_per_unit: float

economism_warning_threshold: float

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class babylon.config.defines.CarceralDefines(**data)

Bases: BaseModel

Carceral equilibrium coefficients (Terminal Crisis Dynamics).

The carceral system models the transition from wage suppression to outright incarceration as the imperial rent pool exhausts:

1. SUPERWAGE_CRISIS: Rent pool can’t sustain LA wages

2. CLASS_DECOMPOSITION: LA splits into enforcers + prisoners

3. CONTROL_RATIO_CRISIS: Prisoners exceed control capacity

4. TERMINAL_DECISION: Revolution vs genocide based on organization

Real-world staffing ratios (sources: BJS, Marshall Project 2024): - 1:1 = Maximum control (Massachusetts, best-staffed) - 4:1 = US national jail average (2022) - 15:1 = Federal DOJ theoretical baseline - 200:1 = Crisis/collapse (Georgia, 2024)

With 70/30 decomposition, prisoner/enforcer = 2.33:1, so: - control_capacity <= 2: Crisis triggers immediately - control_capacity >= 3: No crisis (stable carceral state)

Parameters:

• control_capacity (Annotated[int, Ge(ge=1), Le(le=20)])

• enforcer_fraction (Annotated[float, Ge(ge=0.05), Le(le=0.5)])

• proletariat_fraction (Annotated[float, Ge(ge=0.5), Le(le=0.95)])

• revolution_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• decomposition_delay (Annotated[int, Ge(ge=0), Le(le=520)])

• control_ratio_delay (Annotated[int, Ge(ge=0), Le(le=520)])

• terminal_decision_delay (Annotated[int, Ge(ge=0), Le(le=52)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

control_capacity: int

enforcer_fraction: float

proletariat_fraction: float

revolution_threshold: float

decomposition_delay: int

control_ratio_delay: int

terminal_decision_delay: int

class babylon.config.defines.EndgameDefines(**data)

Bases: BaseModel

Configuration for endgame detection thresholds (Slice 1.6).

The EndgameDetector monitors WorldState for three possible game endings:

1. REVOLUTIONARY_VICTORY: percolation >= threshold AND consciousness > threshold The masses have achieved critical organization AND ideological clarity.

2. ECOLOGICAL_COLLAPSE: overshoot_ratio > threshold for N consecutive ticks Sustained ecological overshoot leads to irreversible collapse.

3. FASCIST_CONSOLIDATION: national_identity > class_consciousness for M+ nodes Fascist ideology has captured the majority of the population.

Parameters:

• revolutionary_percolation_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• revolutionary_consciousness_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• ecological_overshoot_threshold (Annotated[float, Gt(gt=0)])

• ecological_sustained_ticks (Annotated[int, Ge(ge=1), Le(le=100)])

• fascist_majority_threshold (Annotated[int, Ge(ge=1), Le(le=100)])

revolutionary_percolation_threshold

Minimum percolation ratio (0.7 = 70% of nodes in giant solidarity component) for revolutionary victory.

revolutionary_consciousness_threshold

Minimum average class consciousness (0.8 = 80% ideological clarity) for revolutionary victory.

ecological_overshoot_threshold

Consumption/biocapacity ratio above which ecological damage accumulates (2.0 = consuming 2x biocapacity).

ecological_sustained_ticks

Number of consecutive ticks overshoot must persist before triggering ECOLOGICAL_COLLAPSE (5 ticks).

fascist_majority_threshold

Minimum number of nodes where national_identity exceeds class_consciousness for FASCIST_CONSOLIDATION (3 nodes).

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

revolutionary_percolation_threshold: float

revolutionary_consciousness_threshold: float

ecological_overshoot_threshold: float

ecological_sustained_ticks: int

fascist_majority_threshold: int

class babylon.config.defines.InitialDefines(**data)

Bases: BaseModel

Initial condition coefficients.

Parameters:

• worker_wealth (Annotated[float, Ge(ge=0)])

• owner_wealth (Annotated[float, Ge(ge=0)])

• default_population (Annotated[int, Ge(ge=1)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

worker_wealth: float

owner_wealth: float

default_population: int

class babylon.config.defines.PrecisionDefines(**data)

Bases: BaseModel

Numerical precision configuration for deterministic simulation.

Epoch 0 Physics Hardening: - All floating-point values snap to a 10^-n grid (default n=6) - This prevents drift accumulation over long simulations (100+ years) - ROUND_HALF_UP ensures deterministic cross-platform behavior

The Gatekeeper Pattern: Quantization is applied at TYPE level (Pydantic AfterValidator), NOT inside formulas.

Note: Increased from 5 to 6 decimal places for 100-year (5200 tick) Carceral Equilibrium simulations to reduce cumulative rounding errors.

Parameters:

• decimal_places (Annotated[int, Ge(ge=1), Le(le=10)])

• rounding_mode (str)

• epsilon (Annotated[float, Gt(gt=0.0), Le(le=0.001)])

• comparison_epsilon (Annotated[float, Gt(gt=0.0), Le(le=1e-06)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

decimal_places: int

rounding_mode: str

epsilon: float

comparison_epsilon: float

class babylon.config.defines.ArcGISDefines(**data)

Bases: BaseModel

ArcGIS organization and host configuration for external data sources.

Different federal agencies host HIFLD and infrastructure data on various ArcGIS organizations. This configuration centralizes the organization IDs and hosts to allow easy updates when services migrate.

Current organization mapping (as of 2024): - FEMA RAPT: Prison Boundaries, Law Enforcement (services.arcgis.com) - Esri US Federal: MIRTA Military Installations (services2.arcgis.com) - HIFLD Legacy: Some services still work (services1.arcgis.com)

Parameters:

• fema_rapt_org (str)

• fema_rapt_host (str)

• esri_federal_org (str)

• esri_federal_host (str)

• hifld_legacy_org (str)

• hifld_legacy_host (str)

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fema_rapt_org: str

fema_rapt_host: str

esri_federal_org: str

esri_federal_host: str

hifld_legacy_org: str

hifld_legacy_host: str

class babylon.config.defines.ServicesDefines(**data)

Bases: BaseModel

ArcGIS FeatureServer service names and layers.

Service names can be overridden for testing or alternative data sources. Layer numbers are important as some services (like Prison_Boundaries) have data on non-default layers.

Parameters:

• prison_boundaries (str)

• prison_boundaries_layer (Annotated[int, Ge(ge=0)])

• law_enforcement (str)

• law_enforcement_layer (Annotated[int, Ge(ge=0)])

• mirta_polygons (str)

• mirta_layer (Annotated[int, Ge(ge=0)])

• electric_transmission (str)

• electric_transmission_layer (Annotated[int, Ge(ge=0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

prison_boundaries: str

prison_boundaries_layer: int

law_enforcement: str

law_enforcement_layer: int

mirta_polygons: str

mirta_layer: int

electric_transmission: str

electric_transmission_layer: int

class babylon.config.defines.ExternalDataDefines(**data)

Bases: BaseModel

External data source configuration.

Centralizes ArcGIS organization IDs, hosts, and service names for HIFLD and related infrastructure data sources. This enables: 1. Easy updates when services migrate between organizations 2. Testing with alternative data sources 3. Clear documentation of data source provenance

Parameters:

• arcgis (ArcGISDefines)

• services (ServicesDefines)

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

arcgis: ArcGISDefines

services: ServicesDefines

build_service_url(host, org, service, layer=0)

Build a complete ArcGIS FeatureServer URL.

Parameters:

• host (str) – ArcGIS host domain (e.g., “services.arcgis.com”)

• org (str) – Organization ID (e.g., “XG15cJAlne2vxtgt”)

• service (str) – Service name (e.g., “Prison_Boundaries_RAPT”)

• layer (int) – Layer number (default 0)

Return type:

str

Returns:

Complete FeatureServer URL

prison_boundaries_url()

Get the Prison Boundaries FeatureServer URL.

Return type:

str

law_enforcement_url()

Get the Law Enforcement Locations FeatureServer URL.

Return type:

str

mirta_url()

Get the MIRTA Military Installations FeatureServer URL.

Return type:

str

electric_transmission_url()

Get the Electric Transmission Lines FeatureServer URL.

Return type:

str

class babylon.config.defines.TimescaleDefines(**data)

Bases: BaseModel

Simulation timescale configuration for weekly ticks.

Epoch 0 Physics Hardening: - 1 tick = 7 days (weekly resolution) - 52 weeks = 1 year (for annual rate conversions)

This is critical for: - Economic flow rates (annual -> per-tick conversion) - Historical pacing (events per game year) - UI display (showing dates/weeks)

All annual rates (wage_rate, extraction_efficiency) are divided by weeks_per_year to get per-tick rates.

Parameters:

• tick_duration_days (Annotated[int, Ge(ge=1), Le(le=365)])

• weeks_per_year (Annotated[int, Ge(ge=1)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

tick_duration_days: int

weeks_per_year: int

property ticks_per_year: int

Number of ticks per simulation year.

Since 1 tick = 1 week, this equals weeks_per_year.

property days_per_year: int

Days per simulation year (ticks * days_per_tick).

With defaults: 7 * 52 = 364 days (close to actual 365-366).

class babylon.config.defines.ContradictionFieldDefines(**data)

Bases: BaseModel

Contradiction field topology coefficients (Feature 002).

Configures normalization bounds, history window depth, and transition thresholds for the dialectical field topology systems.

See also

specs/002-dialectical-field-topology/spec.md: FR-001 through FR-019

Parameters:

• field_min (Annotated[float, Ge(ge=0)])

• field_max (Annotated[float, Gt(gt=0)])

• history_window (Annotated[int, Ge(ge=2), Le(le=10)])

• curvature_alpha (Annotated[float, Gt(gt=0), Le(le=1)])

• co_optive_suppression_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• latent_release_multiplier (Annotated[float, Ge(ge=1.0), Le(le=5.0)])

• default_transition_priority (Annotated[int, Ge(ge=0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

field_min: float

field_max: float

history_window: int

curvature_alpha: float

co_optive_suppression_rate: float

latent_release_multiplier: float

default_transition_priority: int

class babylon.config.defines.ReserveArmyDefines(**data)

Bases: BaseModel

Reserve army of labor coefficients (Feature 021, FR-001/FR-002).

Configures the bounded sigmoid that maps reserve_ratio to wage pressure. Higher reserve ratios produce stronger downward pressure on wages.

See also

specs/021-capital-volume-i/spec.md: FR-001, FR-002, FR-003

Parameters:

• sigmoid_k (Annotated[float, Gt(gt=0.0), Le(le=100.0)])

• sigmoid_r0 (Annotated[float, Gt(gt=0), Le(le=1)])

• wage_pressure_ceiling (Annotated[float, Gt(gt=0), Le(le=1)])

• min_employed_fraction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

sigmoid_k: float

sigmoid_r0: float

wage_pressure_ceiling: float

min_employed_fraction: float

class babylon.config.defines.DispossessionDefines(**data)

Bases: BaseModel

Dispossession event intensity weights (Feature 021, FR-004/FR-005).

Configures the relative weight of each dispossession type when computing aggregate territory-level dispossession intensity.

See also

specs/021-capital-volume-i/spec.md: FR-004, FR-005, FR-006

Parameters:

• weight_foreclosure (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• weight_eviction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• weight_displacement (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• weight_tax_sale (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• weight_eminent_domain (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• weight_wage_theft (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• weight_incarceration_seizure (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• weight_pension_default (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• deadweight_loss_fraction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• transfer_scale (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

weight_foreclosure: float

weight_eviction: float

weight_displacement: float

weight_tax_sale: float

weight_eminent_domain: float

weight_wage_theft: float

weight_incarceration_seizure: float

weight_pension_default: float

deadweight_loss_fraction: float

transfer_scale: float

class babylon.config.defines.WorkingDayDefines(**data)

Bases: BaseModel

Working day characterization thresholds (Feature 021, FR-007/FR-008).

Configures the threshold values for classifying territory-sector pairs by their dominant mode of surplus value extraction.

See also

specs/021-capital-volume-i/spec.md: FR-007, FR-008, FR-011

Parameters:

• absolute_hours_threshold (Annotated[float, Gt(gt=0.0), Le(le=168.0)])

• relative_hours_threshold (Annotated[float, Gt(gt=0.0), Le(le=168.0)])

• intensity_threshold_high (Annotated[float, Gt(gt=0)])

• intensity_threshold_low (Annotated[float, Gt(gt=0)])

• absolute_visibility (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• relative_visibility (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

absolute_hours_threshold: float

relative_hours_threshold: float

intensity_threshold_high: float

intensity_threshold_low: float

absolute_visibility: float

relative_visibility: float

class babylon.config.defines.CommunityDefines(**data)

Bases: BaseModel

Hypergraph community layer coefficients (Feature 022).

Controls alpha-smoothing decay rates for community state, solidarity potential computation bonuses and penalties, and infrastructure maintenance parameters.

Parameters:

• heat_decay_alpha (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• cohesion_decay_alpha (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• infrastructure_decay_alpha (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• community_overlap_bonus (Annotated[float, Ge(ge=0)])

• rent_differential_penalty (Annotated[float, Ge(ge=0)])

• core_organizer_maintenance_factor (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

heat_decay_alpha: float

cohesion_decay_alpha: float

infrastructure_decay_alpha: float

community_overlap_bonus: float

rent_differential_penalty: float

core_organizer_maintenance_factor: float

class babylon.config.defines.ClassDynamicsDefines(**data)

Bases: BaseModel

Class dynamics coefficients (FRED DFA-derived, Feature 016).

Parameters fitted to FRED Distributional Financial Accounts (2015-2025) for class wealth flow dynamics.

Parameters:

• alpha_21 (Annotated[float, Ge(ge=0.0), Le(le=0.01)])

• gamma_3 (Annotated[float, Ge(ge=0.0), Le(le=0.1)])

• equilibrium_w1 (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• equilibrium_w2 (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• equilibrium_w3 (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• equilibrium_w4 (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• alpha_41 (Annotated[float, Ge(ge=0.0), Le(le=0.01)])

• alpha_31 (Annotated[float, Ge(ge=0.0), Le(le=0.01)])

• alpha_32 (Annotated[float, Ge(ge=0.0), Le(le=0.01)])

• alpha_42 (Annotated[float, Ge(ge=0.0), Le(le=0.01)])

• alpha_43 (Annotated[float, Ge(ge=0.0), Le(le=0.01)])

• delta_1 (Annotated[float, Ge(ge=0.0), Le(le=0.1)])

• delta_2 (Annotated[float, Ge(ge=0.0), Le(le=0.1)])

• delta_3 (Annotated[float, Ge(ge=0.0), Le(le=0.1)])

• beta_1 (Annotated[float, Ge(ge=-1.0), Le(le=0.0)])

• beta_2 (Annotated[float, Ge(ge=-1.0), Le(le=0.0)])

• beta_3 (Annotated[float, Ge(ge=-1.0), Le(le=0.0)])

• beta_4 (Annotated[float, Ge(ge=-1.0), Le(le=0.0)])

• omega_1 (Annotated[float, Gt(gt=0), Le(le=1)])

• omega_2 (Annotated[float, Gt(gt=0), Le(le=1)])

• omega_3 (Annotated[float, Gt(gt=0), Le(le=1)])

• omega_4 (Annotated[float, Gt(gt=0), Le(le=1)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

alpha_21: float

gamma_3: float

equilibrium_w1: float

equilibrium_w2: float

equilibrium_w3: float

equilibrium_w4: float

alpha_41: float

alpha_31: float

alpha_32: float

alpha_42: float

alpha_43: float

delta_1: float

delta_2: float

delta_3: float

beta_1: float

beta_2: float

beta_3: float

beta_4: float

omega_1: float

omega_2: float

omega_3: float

omega_4: float

class babylon.config.defines.LifecycleDefines(**data)

Bases: BaseModel

D-P-D’ Lifecycle Circuit coefficients (Feature 030).

36 tunable parameters for intergenerational class reproduction. All defaults have documented provenance (CDC, Census, Chetty, etc.).

The legitimation weight ranking is a design invariant reflecting authorial political judgment; individual values are tunable but the ordinal ranking is not.

See also

specs/030-dpd-lifecycle-circuit/data-model.md: Full provenance table.

Parameters:

• birth_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• rate_d_to_p (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• rate_p_to_d_prime (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• rate_d_prime_to_death (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• initial_pop_d_frac (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• initial_pop_p_frac (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• initial_pop_d_prime_frac (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• pension_coverage_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• home_ownership_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• ss_replacement_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• healthcare_security (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• retirement_confidence (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• legit_w_home_ownership (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• legit_w_healthcare_security (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• legit_w_retirement_confidence (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• legit_w_pension_coverage (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• legit_w_ss_replacement (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• legitimation_blend_weight (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• legitimation_crisis_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• legitimation_unstable_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• pareto_alpha (Annotated[float, Gt(gt=0.0), Le(le=10.0)])

• care_cost_fraction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• mobility_base_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• mobility_base_rate_p75 (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• mobility_racial_gap (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• carceral_transition_modifier (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• early_mortality_modifier (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• baseline_gini (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• poverty_share (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• employment_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• single_parent_fraction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• college_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• ideology_caregiver_weight (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• ideology_institutional_weight (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• ideology_regression_coefficient (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• sandwich_squeeze_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

birth_rate: float

rate_d_to_p: float

rate_p_to_d_prime: float

rate_d_prime_to_death: float

initial_pop_d_frac: float

initial_pop_p_frac: float

initial_pop_d_prime_frac: float

pension_coverage_rate: float

home_ownership_rate: float

ss_replacement_rate: float

healthcare_security: float

retirement_confidence: float

legit_w_home_ownership: float

legit_w_healthcare_security: float

legit_w_retirement_confidence: float

legit_w_pension_coverage: float

legit_w_ss_replacement: float

legitimation_blend_weight: float

legitimation_crisis_threshold: float

legitimation_unstable_threshold: float

pareto_alpha: float

care_cost_fraction: float

mobility_base_rate: float

mobility_base_rate_p75: float

mobility_racial_gap: float

carceral_transition_modifier: float

early_mortality_modifier: float

baseline_gini: float

poverty_share: float

employment_rate: float

single_parent_fraction: float

college_rate: float

ideology_caregiver_weight: float

ideology_institutional_weight: float

ideology_regression_coefficient: float

sandwich_squeeze_threshold: float

class babylon.config.defines.OrganizationDefines(**data)

Bases: BaseModel

Organization system tunable coefficients (Feature 031).

14 parameters controlling consciousness effects, intelligence methodology ceilings, cohesion mechanics, credibility defaults, and capacity factors.

See also

specs/031-organization-base-model/data-model.md: Full provenance table.

Parameters:

• elder_capacity_factor (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• tendency_modifier_revolutionary (Annotated[float, Ge(ge=-1.0), Le(le=1.0)])

• tendency_modifier_liberal (Annotated[float, Ge(ge=-1.0), Le(le=1.0)])

• tendency_modifier_fascist (Annotated[float, Ge(ge=-1.0), Le(le=1.0)])

• observation_ceiling_local_pd (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• observation_ceiling_fusion (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• observation_ceiling_fbi (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• cohesion_loss_per_key_figure (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• min_cohesion_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• credibility_default_faction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• credibility_sovereign (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• credibility_chartered (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• credibility_default_state (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• violence_capacity_default (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• surveillance_capacity_default (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

elder_capacity_factor: float

tendency_modifier_revolutionary: float

tendency_modifier_liberal: float

tendency_modifier_fascist: float

observation_ceiling_local_pd: float

observation_ceiling_fusion: float

observation_ceiling_fbi: float

cohesion_loss_per_key_figure: float

min_cohesion_threshold: float

credibility_default_faction: float

credibility_sovereign: float

credibility_chartered: float

credibility_default_state: float

violence_capacity_default: float

surveillance_capacity_default: float

class babylon.config.defines.EdgeTransitionDefines(**data)

Bases: BaseModel

Edge mode transition threshold values (Feature 002, FR-010).

Configures the threshold values used in predicate conditions that determine when edges transition between modes (EXTRACTIVE, TRANSACTIONAL, CO-OPTIVE, etc.).

Parameters:

• extraction_contested_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• extraction_broken_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• concessions_exploitation_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• concessions_rent_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• mutual_aid_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• market_failure_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• power_asymmetry_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• co_optive_power_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• solidarity_degrades_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• betrayal_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• conflict_resolved_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• shared_enemy_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• reform_rent_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• co_optation_normalizes_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• co_optive_breakdown_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

• concessions_withdrawn_threshold (Annotated[float, Ge(ge=0.0), Le(le=10.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

extraction_contested_threshold: float

extraction_broken_threshold: float

concessions_exploitation_threshold: float

concessions_rent_threshold: float

mutual_aid_threshold: float

market_failure_threshold: float

power_asymmetry_threshold: float

co_optive_power_threshold: float

solidarity_degrades_threshold: float

betrayal_threshold: float

conflict_resolved_threshold: float

shared_enemy_threshold: float

reform_rent_threshold: float

co_optation_normalizes_threshold: float

co_optive_breakdown_threshold: float

concessions_withdrawn_threshold: float

class babylon.config.defines.OODADefines(**data)

Bases: BaseModel

OODA Loop System tunable coefficients (Feature 032).

Controls cycle time computation, initiative scoring, action costs, consciousness effect multipliers, and propagation parameters.

See also

specs/032-ooda-loop-system/data-model.md: Full specification.

Parameters:

• base_observe_time (Annotated[float, Gt(gt=0)])

• latency_weight (Annotated[float, Ge(ge=0)])

• base_orient_time (Annotated[float, Gt(gt=0)])

• coherence_weight (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• base_act_time (Annotated[float, Gt(gt=0)])

• coord_weight (Annotated[float, Ge(ge=0)])

• depth_weight (Annotated[float, Ge(ge=0)])

• decision_mode_base_autocratic (Annotated[float, Gt(gt=0)])

• decision_mode_base_delegate (Annotated[float, Gt(gt=0)])

• decision_mode_base_democratic (Annotated[float, Gt(gt=0)])

• decision_mode_base_consensus (Annotated[float, Gt(gt=0)])

• initiative_weight_speed (Annotated[float, Ge(ge=0)])

• initiative_weight_institutional (Annotated[float, Ge(ge=0)])

• initiative_weight_counterintel (Annotated[float, Ge(ge=0)])

• initiative_weight_embeddedness (Annotated[float, Ge(ge=0)])

• initiative_weight_momentum (Annotated[float, Ge(ge=0)])

• institutional_bonus_federal (Annotated[float, Ge(ge=0)])

• institutional_bonus_state (Annotated[float, Ge(ge=0)])

• institutional_bonus_local (Annotated[float, Ge(ge=0)])

• institutional_bonus_nonstate (Annotated[float, Ge(ge=0)])

• momentum_decay (Annotated[float, Ge(ge=0), Lt(lt=1.0)])

• momentum_success_bonus (Annotated[float, Ge(ge=0)])

• embeddedness_discount (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• contradiction_cost_multiplier (Annotated[float, Gt(gt=1.0)])

• outsider_cost_multiplier (Annotated[float, Gt(gt=1.0)])

• min_cost_modifier (Annotated[float, Gt(gt=0), Le(le=1)])

• max_ci_delta_per_tick (Annotated[float, Gt(gt=0), Le(le=1)])

• action_base_educate (Annotated[float, Ge(ge=0)])

• action_base_agitate (Annotated[float, Ge(ge=0)])

• action_base_provide_service (Annotated[float, Ge(ge=0)])

• action_base_recruit (Annotated[float, Ge(ge=0)])

• action_base_organize (Annotated[float, Ge(ge=0)])

• action_base_propagandize (Annotated[float, Ge(ge=0)])

• action_base_repress (Annotated[float, Ge(ge=0)])

• action_base_surveil (Annotated[float, Ge(ge=0)])

• action_base_assimilate (Annotated[float, Ge(ge=0)])

• autonomy_effectiveness_scale (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• agitation_contestation_delta (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• agitation_educate_bonus (Annotated[float, Ge(ge=1.0)])

• contestation_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• elder_legitimacy_multiplier (Annotated[float, Ge(ge=1.0)])

• counter_intel_increment (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• base_cost_recruit (Annotated[int, Ge(ge=1)])

• base_cost_organize (Annotated[int, Ge(ge=1)])

• base_cost_educate (Annotated[int, Ge(ge=1)])

• base_cost_agitate (Annotated[int, Ge(ge=1)])

• base_cost_propagandize (Annotated[int, Ge(ge=1)])

• base_cost_fundraise (Annotated[int, Ge(ge=1)])

• base_cost_provide_service (Annotated[int, Ge(ge=1)])

• base_cost_employ (Annotated[int, Ge(ge=1)])

• base_cost_repress (Annotated[int, Ge(ge=1)])

• base_cost_protest (Annotated[int, Ge(ge=1)])

• base_cost_strike (Annotated[int, Ge(ge=1)])

• base_cost_expropriate (Annotated[int, Ge(ge=1)])

• base_cost_surveil (Annotated[int, Ge(ge=1)])

• base_cost_infiltrate (Annotated[int, Ge(ge=1)])

• base_cost_counter_intel (Annotated[int, Ge(ge=1)])

• base_cost_map_network (Annotated[int, Ge(ge=1)])

• base_cost_propose_alliance (Annotated[int, Ge(ge=1)])

• base_cost_denounce (Annotated[int, Ge(ge=1)])

• base_cost_build_infrastructure (Annotated[int, Ge(ge=1)])

• base_cost_attack_infrastructure (Annotated[int, Ge(ge=1)])

• base_cost_assimilate (Annotated[int, Ge(ge=1)])

• repress_heat_delta (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• surveil_heat_delta (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• build_infrastructure_delta (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• attack_infrastructure_delta (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• orient_time_floor (Annotated[float, Ge(ge=0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

base_observe_time: float

latency_weight: float

base_orient_time: float

coherence_weight: float

base_act_time: float

coord_weight: float

depth_weight: float

decision_mode_base_autocratic: float

decision_mode_base_delegate: float

decision_mode_base_democratic: float

decision_mode_base_consensus: float

initiative_weight_speed: float

initiative_weight_institutional: float

initiative_weight_counterintel: float

initiative_weight_embeddedness: float

initiative_weight_momentum: float

institutional_bonus_federal: float

institutional_bonus_state: float

institutional_bonus_local: float

institutional_bonus_nonstate: float

momentum_decay: float

momentum_success_bonus: float

embeddedness_discount: float

contradiction_cost_multiplier: float

outsider_cost_multiplier: float

min_cost_modifier: float

max_ci_delta_per_tick: float

action_base_educate: float

action_base_agitate: float

action_base_provide_service: float

action_base_recruit: float

action_base_organize: float

action_base_propagandize: float

action_base_repress: float

action_base_surveil: float

action_base_assimilate: float

autonomy_effectiveness_scale: float

agitation_contestation_delta: float

agitation_educate_bonus: float

contestation_threshold: float

elder_legitimacy_multiplier: float

counter_intel_increment: float

base_cost_recruit: int

base_cost_organize: int

base_cost_educate: int

base_cost_agitate: int

base_cost_propagandize: int

base_cost_fundraise: int

base_cost_provide_service: int

base_cost_employ: int

base_cost_repress: int

base_cost_protest: int

base_cost_strike: int

base_cost_expropriate: int

base_cost_surveil: int

base_cost_infiltrate: int

base_cost_counter_intel: int

base_cost_map_network: int

base_cost_propose_alliance: int

base_cost_denounce: int

base_cost_build_infrastructure: int

base_cost_attack_infrastructure: int

base_cost_assimilate: int

repress_heat_delta: float

surveil_heat_delta: float

build_infrastructure_delta: float

attack_infrastructure_delta: float

orient_time_floor: float

validate_derivations(game_defines)

Cross-validate OODA coefficients against source primitives.

Checks that derived coefficients (Categories A and B) still match their source primitive formulas. Returns a list of drift warnings for any mismatches exceeding tolerance (0.001).

Parameters:

game_defines (GameDefines) – Parent GameDefines providing source primitives.

Return type:

list[str]

Returns:

List of warning messages for any detected drift. Empty if clean.

get_base_cost(action_type)

Look up base AP cost for an action type.

Parameters:

action_type (str) – ActionType value string.

Return type:

int

Returns:

Base AP cost for the action.

Raises:

KeyError – If action_type is not recognized.

get_action_base(action_type)

Look up consciousness base multiplier for an action type.

Parameters:

action_type (str) – ActionType value string.

Return type:

float

Returns:

Consciousness base multiplier (0.0 means no CI effect).

class babylon.config.defines.BifurcationDefines(**data)

Bases: BaseModel

Bifurcation Topology Analysis coefficients (Feature 033).

Configures consciousness-weighted solidarity analysis that predicts whether crisis routes to fascism or revolution. The core innovation: a nonlinear sigmoid of collective_identity weights solidarity edges so assimilationist solidarity classifies as fragile/fascist.

See also

babylon.bifurcation.consciousness: Sigmoid weighting. babylon.bifurcation.analysis: Full bifurcation orchestrator. specs/033-bifurcation-topology/spec.md: Feature specification.

Parameters:

• consciousness_sigmoid_midpoint (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• consciousness_sigmoid_steepness (Annotated[float, Gt(gt=0.0), Le(le=50.0)])

• consciousness_filter_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• indeterminate_dead_zone (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• axis_tendency_epsilon (Annotated[float, Gt(gt=0), Le(le=0.1)])

• legitimation_amplifier_scale (Annotated[float, Ge(ge=1.0), Le(le=10.0)])

• wage_ceiling_high_ratio (Annotated[float, Ge(ge=1.0)])

• wage_ceiling_low_ratio (Annotated[float, Ge(ge=1.0)])

• wage_ceiling_min (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• wage_ceiling_max (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• shared_exploitation_bonus (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• purge_removal_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

consciousness_sigmoid_midpoint: float

consciousness_sigmoid_steepness: float

consciousness_filter_threshold: float

indeterminate_dead_zone: float

axis_tendency_epsilon: float

legitimation_amplifier_scale: float

wage_ceiling_high_ratio: float

wage_ceiling_low_ratio: float

wage_ceiling_min: float

wage_ceiling_max: float

shared_exploitation_bonus: float

purge_removal_rate: float

class babylon.config.defines.InfraTerrainDefines(**data)

Bases: BaseModel

Terrain classification and biocapacity coefficients (Feature 036).

Configures majority-coverage thresholds, initial biocapacity stock values, and per-tick depletion rates. Also includes internet access defaults.

See also

babylon.infrastructure.terrain: DefaultTerrainClassifier. specs/036-infrastructure-topology/spec.md: FR-001 through FR-008.

Parameters:

• majority_coverage_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• initial_freshwater (Annotated[float, Ge(ge=0)])

• initial_fishery (Annotated[float, Ge(ge=0)])

• initial_shipping_access (Annotated[float, Ge(ge=0)])

• initial_mineral (Annotated[float, Ge(ge=0)])

• initial_timber (Annotated[float, Ge(ge=0)])

• initial_hydroelectric (Annotated[float, Ge(ge=0)])

• depletion_freshwater (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• depletion_fishery (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• depletion_shipping_access (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• depletion_mineral (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• depletion_timber (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• depletion_hydroelectric (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• internet_access_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• default_surveillance_coupling (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

majority_coverage_threshold: float

initial_freshwater: float

initial_fishery: float

initial_shipping_access: float

initial_mineral: float

initial_timber: float

initial_hydroelectric: float

depletion_freshwater: float

depletion_fishery: float

depletion_shipping_access: float

depletion_mineral: float

depletion_timber: float

depletion_hydroelectric: float

internet_access_threshold: float

default_surveillance_coupling: float

get_initial_stock(stock_type)

Get initial biocapacity stock value by type.

Parameters:

stock_type (str) – BiocapacityType value (lowercase).

Return type:

float

Returns:

Initial stock value.

Raises:

ValueError – If stock_type is not recognized.

get_depletion_rate(stock_type)

Get per-tick depletion rate by stock type.

Parameters:

stock_type (str) – BiocapacityType value (lowercase).

Return type:

float

Returns:

Depletion rate per tick.

Raises:

ValueError – If stock_type is not recognized.

class babylon.config.defines.InfrastructureDefines(**data)

Bases: BaseModel

Infrastructure capacity and internet operation coefficients (Feature 036).

Configures per-type capacity values, natural capacity defaults, OPSEC tradeoff ratios, and internet throttle fractions.

See also

babylon.infrastructure.capacity: DefaultEdgeCapacityCalculator. specs/036-infrastructure-topology/spec.md: FR-009 through FR-029.

Parameters:

• highway_freight (Annotated[float, Ge(ge=0)])

• highway_commuter (Annotated[float, Ge(ge=0)])

• highway_value (Annotated[float, Ge(ge=0)])

• highway_consciousness (Annotated[float, Ge(ge=0)])

• arterial_freight (Annotated[float, Ge(ge=0)])

• arterial_commuter (Annotated[float, Ge(ge=0)])

• arterial_value (Annotated[float, Ge(ge=0)])

• arterial_consciousness (Annotated[float, Ge(ge=0)])

• local_road_freight (Annotated[float, Ge(ge=0)])

• local_road_commuter (Annotated[float, Ge(ge=0)])

• local_road_value (Annotated[float, Ge(ge=0)])

• local_road_consciousness (Annotated[float, Ge(ge=0)])

• rail_freight (Annotated[float, Ge(ge=0)])

• rail_commuter (Annotated[float, Ge(ge=0)])

• rail_value (Annotated[float, Ge(ge=0)])

• rail_consciousness (Annotated[float, Ge(ge=0)])

• pipeline_energy (Annotated[float, Ge(ge=0)])

• transmission_energy (Annotated[float, Ge(ge=0)])

• shipping_lane_freight (Annotated[float, Ge(ge=0)])

• air_link_freight (Annotated[float, Ge(ge=0)])

• air_link_commuter (Annotated[float, Ge(ge=0)])

• air_link_value (Annotated[float, Ge(ge=0)])

• air_link_consciousness (Annotated[float, Ge(ge=0)])

• natural_capacity_coefficient (Annotated[float, Ge(ge=0)])

• minimum_capacity_threshold (Annotated[float, Ge(ge=0)])

• opsec_tradeoff_ratio (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• throttle_throughput_fraction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• snap_buffer_fraction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• local_ratio_threshold (Annotated[float, Gt(gt=0)])

• semi_local_ratio_threshold (Annotated[float, Gt(gt=0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

highway_freight: float

highway_commuter: float

highway_value: float

highway_consciousness: float

arterial_freight: float

arterial_commuter: float

arterial_value: float

arterial_consciousness: float

local_road_freight: float

local_road_commuter: float

local_road_value: float

local_road_consciousness: float

rail_freight: float

rail_commuter: float

rail_value: float

rail_consciousness: float

pipeline_energy: float

transmission_energy: float

shipping_lane_freight: float

air_link_freight: float

air_link_commuter: float

air_link_value: float

air_link_consciousness: float

natural_capacity_coefficient: float

minimum_capacity_threshold: float

opsec_tradeoff_ratio: float

throttle_throughput_fraction: float

snap_buffer_fraction: float

local_ratio_threshold: float

semi_local_ratio_threshold: float

get_capacity(infra_type, flow_category)

Get base capacity for an infrastructure type and flow category.

Parameters:

• infra_type (str) – InfrastructureType value (lowercase).

• flow_category (str) – FlowCategory value (lowercase).

Return type:

float

Returns:

Base capacity value, or 0.0 if combination not applicable.

class babylon.config.defines.RentCircuitDefines(**data)

Bases: BaseModel

Parameters for ground rent extraction in Volume III equalization (Feature 043).

Parameters:

• absolute_rent_fraction (Annotated[float, Ge(ge=0), Le(le=0.5)])

• differential_rent_elasticity (Annotated[float, Ge(ge=0.1), Le(le=5.0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

absolute_rent_fraction: float

differential_rent_elasticity: float

class babylon.config.defines.ClassSystemDefines(**data)

Bases: BaseModel

Unified class system coefficients (Feature 038).

Centralizes all tunable coefficients for the unified class system: filtration parameters, home ownership proxy, and the 5x5 class-pair solidarity matrix.

Parameters:

• trust_land_discount (Annotated[float, Ge(ge=0.0), Le(le=1.0)]) – Fed SCF / BIA discount on effective wealth for FIRST_NATIONS trust land property. 0.5 = 50% reduction.

• documentation_exclusion_factor (Annotated[float, Ge(ge=0.0), Le(le=1.0)]) – Discount on effective wealth for UNDOCUMENTED households. 0.6 = 40% reduction.

• equity_factor (Annotated[float, Ge(ge=0.0), Le(le=1.0)]) – Fraction of homeowners with meaningful equity. Calibrated: 65% ownership * 0.6 = 39% ~ 40% LA share.

• base_class_solidarity (dict[str, dict[str, float]]) – Symmetric 5x5 class-pair base solidarity matrix (15 unique values in upper triangle including diagonal).

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

trust_land_discount: float

documentation_exclusion_factor: float

equity_factor: float

base_class_solidarity: dict[str, dict[str, float]]

get_base_solidarity(class_a, class_b)

Symmetric lookup into the class-pair solidarity matrix.

Parameters:

• class_a (str) – ClassPosition name (e.g. “PROLETARIAT”).

• class_b (str) – ClassPosition name (e.g. “LABOR_ARISTOCRACY”).

Return type:

float

Returns:

Base solidarity value, or 0.0 for unknown pairs.

class babylon.config.defines.StateApparatusAIDefines(**data)

Bases: BaseModel

State Apparatus AI coefficients (Feature 039).

Configures the state-as-adversary system: faction dynamics, fascist convergence detection, attention thread management, budget allocation, escalation ladder, and territory effect parameters.

All thresholds are [S] SYNTHETIC unless otherwise noted.

See also

specs/039-state-apparatus-ai/spec.md: Full specification.

Parameters:

• max_faction_shift_per_tick (Annotated[float, Ge(ge=0.0), Le(le=0.2)])

• minimum_effect_floor (Annotated[float, Ge(ge=0.0), Le(le=0.1)])

• heat_to_ss_coefficient (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• fascist_security_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• fascist_settler_ci_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• fascist_finance_ceiling (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• convergence_confirmation_ticks (Annotated[int, Ge(ge=1), Le(le=10)])

• reversion_ss_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• reversion_ci_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• thread_pool_base (Annotated[int, Ge(ge=1), Le(le=50)])

• thread_pool_max (Annotated[int, Ge(ge=1), Le(le=100)])

• thread_escalation_thresholds (dict[str, float])

• detroit_2010_annual_budget (Annotated[float, Ge(ge=0)])

• actions_per_tick (Annotated[int, Ge(ge=1), Le(le=10)])

• escalation_ladder (list[str])

• develop_infrastructure_boost (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• neglect_infrastructure_decay (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• displace_population_fraction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• neglect_quality_floor (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• consciousness_resistance_factor (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• high_profile_heat_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• low_profile_heat_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• heat_escalation_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• scorched_earth_legitimacy_core (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• scorched_earth_legitimacy_periphery (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• strategic_withdrawal_decay_multiplier (Annotated[float, Ge(ge=1.0), Le(le=10.0)])

• strategic_withdrawal_asset_recovery (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• displace_ci_reduction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• displace_community_infra_reduction (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• heat_decay_rate (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• recruit_no_presence_penalty (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• eviction_scatter_ci_loss (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• propagandize_base_delta (Annotated[float, Ge(ge=0), Le(le=0.5)])

• incorporate_base_attractiveness (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• bribe_consciousness_shift (Annotated[float, Ge(ge=0), Le(le=0.5)])

• bribe_liberal_increase (Annotated[float, Ge(ge=0), Le(le=0.5)])

• divide_requires_prior_surveil (bool)

• incorporate_requires_prior_surveil (bool)

• fund_capacity_increment (Annotated[float, Ge(ge=0), Le(le=0.5)])

• staff_thread_cost (Annotated[float, Ge(ge=0.0), Le(le=50.0)])

• staff_max_per_tick (Annotated[int, Ge(ge=1), Le(le=10)])

• audit_routine_detection_chance (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• audit_thorough_detection_chance (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• audit_deep_detection_chance (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• infiltrate_informant_intel_rate (Annotated[float, Ge(ge=0), Le(le=0.5)])

• infiltrate_provocateur_intel_rate (Annotated[float, Ge(ge=0), Le(le=0.5)])

• infiltrate_mole_intel_rate (Annotated[float, Ge(ge=0), Le(le=0.5)])

• infiltrate_detection_base_chance (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• raid_ci_radicalization_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• raid_ci_radicalization_boost (Annotated[float, Ge(ge=0), Le(le=0.5)])

• raid_ci_suppression_rate (Annotated[float, Ge(ge=0), Le(le=0.5)])

• raid_org_coherence_damage (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• raid_key_figure_capture_base (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• raid_force_multiplier_swat (Annotated[float, Ge(ge=1.0), Le(le=5.0)])

• raid_force_multiplier_military (Annotated[float, Ge(ge=1.0), Le(le=10.0)])

• prosecute_org_morale_damage (Annotated[float, Ge(ge=0), Le(le=0.5)])

• prosecute_key_figure_removal_chance (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• prosecute_terrorism_charge_multiplier (Annotated[float, Ge(ge=1.0), Le(le=5.0)])

• prosecute_legitimacy_boost_success (Annotated[float, Ge(ge=0.0), Le(le=0.1)])

• liquidate_singleton_collapse_chance (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• liquidate_core_legitimacy_cost (Annotated[float, Ge(ge=0), Le(le=0.5)])

• liquidate_periphery_legitimacy_cost (Annotated[float, Ge(ge=0), Le(le=0.5)])

• liquidate_deniability_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• liquidate_coherence_damage (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• emergency_powers_thread_multiplier (Annotated[float, Ge(ge=1.0), Le(le=5.0)])

• emergency_powers_liquidate_in_core (bool)

• surveillance_expansion_intel_bonus (Annotated[float, Ge(ge=0), Le(le=0.5)])

• god_mode_enabled (bool)

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

max_faction_shift_per_tick: float

minimum_effect_floor: float

heat_to_ss_coefficient: float

fascist_security_threshold: float

fascist_settler_ci_threshold: float

fascist_finance_ceiling: float

convergence_confirmation_ticks: int

reversion_ss_threshold: float

reversion_ci_threshold: float

thread_pool_base: int

thread_pool_max: int

thread_escalation_thresholds: dict[str, float]

detroit_2010_annual_budget: float

actions_per_tick: int

escalation_ladder: list[str]

develop_infrastructure_boost: float

neglect_infrastructure_decay: float

displace_population_fraction: float

neglect_quality_floor: float

consciousness_resistance_factor: float

high_profile_heat_rate: float

low_profile_heat_rate: float

heat_escalation_threshold: float

scorched_earth_legitimacy_core: float

scorched_earth_legitimacy_periphery: float

strategic_withdrawal_decay_multiplier: float

strategic_withdrawal_asset_recovery: float

displace_ci_reduction: float

displace_community_infra_reduction: float

heat_decay_rate: float

recruit_no_presence_penalty: float

eviction_scatter_ci_loss: float

propagandize_base_delta: float

incorporate_base_attractiveness: float

bribe_consciousness_shift: float

bribe_liberal_increase: float

divide_requires_prior_surveil: bool

incorporate_requires_prior_surveil: bool

fund_capacity_increment: float

staff_thread_cost: float

staff_max_per_tick: int

audit_routine_detection_chance: float

audit_thorough_detection_chance: float

audit_deep_detection_chance: float

infiltrate_informant_intel_rate: float

infiltrate_provocateur_intel_rate: float

infiltrate_mole_intel_rate: float

infiltrate_detection_base_chance: float

raid_ci_radicalization_threshold: float

raid_ci_radicalization_boost: float

raid_ci_suppression_rate: float

raid_org_coherence_damage: float

raid_key_figure_capture_base: float

raid_force_multiplier_swat: float

raid_force_multiplier_military: float

prosecute_org_morale_damage: float

prosecute_key_figure_removal_chance: float

prosecute_terrorism_charge_multiplier: float

prosecute_legitimacy_boost_success: float

liquidate_singleton_collapse_chance: float

liquidate_core_legitimacy_cost: float

liquidate_periphery_legitimacy_cost: float

liquidate_deniability_threshold: float

liquidate_coherence_damage: float

emergency_powers_thread_multiplier: float

emergency_powers_liquidate_in_core: bool

surveillance_expansion_intel_bonus: float

god_mode_enabled: bool

class babylon.config.defines.InstitutionDefines(**data)

Bases: BaseModel

Institution Base Model coefficients (Feature 040).

Configures factional balance dynamics, Bonapartist threshold detection, and default structural selectivity modifiers per apparatus type.

All thresholds are [S] SYNTHETIC unless otherwise noted.

See also

specs/040-institution-base-model/spec.md: Full specification.

Parameters:

• alpha_smoothing_rate (Annotated[float, Ge(ge=0), Le(le=0.5)])

• bonapartist_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• bonapartist_exclusion_threshold (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• default_action_modifiers (dict[str, dict[str, float]])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

alpha_smoothing_rate: float

bonapartist_threshold: float

bonapartist_exclusion_threshold: float

default_action_modifiers: dict[str, dict[str, float]]

class babylon.config.defines.MobilizeDefines(**data)

Bases: BaseModel

Configuration for MOBILIZE verb organizational actions.

Parameters:

• mobilize_cl_cost (Annotated[float, Ge(ge=0)])

• min_consciousness (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• turnout_per_sl (Annotated[float, Ge(ge=0)])

• solidarity_amplification_per_edge (Annotated[float, Ge(ge=0)])

• heat_generation_per_demonstrator (Annotated[float, Ge(ge=0)])

• base_agitation_gain (Annotated[float, Ge(ge=0)])

• strike_value_disruption_factor (Annotated[float, Ge(ge=0.0), Le(le=1.0)])

• max_demonstrators_before_backfire (Annotated[int, Ge(ge=0)])

• backfire_heat_multiplier (Annotated[float, Ge(ge=1.0)])

• backfire_agitation_gain (Annotated[float, Ge(ge=0)])

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

mobilize_cl_cost: float

min_consciousness: float

turnout_per_sl: float

solidarity_amplification_per_edge: float

heat_generation_per_demonstrator: float

base_agitation_gain: float

strike_value_disruption_factor: float

max_demonstrators_before_backfire: int

backfire_heat_multiplier: float

backfire_agitation_gain: float

class babylon.config.defines.MoveDefines(**data)

Bases: BaseModel

MOVE verb coefficients.

Parameters:

• expand_presence_split (float)

• minimum_reception (float)

• relocation_withdrawal_ticks (int)

• expansion_edge_strain (float)

• evasion_base_probability (float)

• reacquire_ticks (int)

• distance_ap_surcharge (int)

expand_presence_split: float

minimum_reception: float

relocation_withdrawal_ticks: int

expansion_edge_strain: float

evasion_base_probability: float

reacquire_ticks: int

distance_ap_surcharge: int

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class babylon.config.defines.NegotiateDefines(**data)

Bases: BaseModel

NEGOTIATE verb coefficients.

Parameters:

• interest_weight (float)

• leverage_weight (float)

• institutional_leverage_weight (float)

• negotiate_solidarity_increment (float)

• betrayal_base_rate (float)

• leverage_threshold_for_institutions (float)

interest_weight: float

leverage_weight: float

institutional_leverage_weight: float

negotiate_solidarity_increment: float

betrayal_base_rate: float

leverage_threshold_for_institutions: float

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class babylon.config.defines.GameDefines(**data)

Bases: BaseModel

Centralized game coefficients extracted from hardcoded values.

GameDefines collects numerical constants that were previously scattered across system implementations. By centralizing them here, we can: - Document their purpose and valid ranges - Override them per-scenario for calibration - Test the sensitivity of outcomes to coefficient changes

The model is frozen (immutable) to ensure defines remain constant throughout a simulation run.

Structure follows the YAML file organization: - crisis: Crisis detection and devaluation mechanics (Feature 018) - economy: Imperial rent extraction and value flow - survival: P(S|A) and P(S|R) survival calculus - solidarity: Consciousness transmission - behavioral: Behavioral economics (loss aversion) - tension: Tension dynamics - consciousness: Consciousness drift - territory: Territory dynamics - topology: Phase transition thresholds (gaseous/liquid/solid) - metabolism: Metabolic rift (ecological limits) - struggle: Struggle dynamics (Agency Layer) - carceral: Carceral equilibrium (Terminal Crisis Dynamics) - endgame: Endgame detection thresholds - initial: Initial conditions - contradiction_field: Dialectical field topology (Feature 002) - reserve_army: Reserve army of labor coefficients (Feature 021) - dispossession: Dispossession event intensity weights (Feature 021) - working_day: Working day characterization thresholds (Feature 021) - community: Hypergraph community layer coefficients (Feature 022) - class_dynamics: Class wealth flow dynamics (Feature 016, FRED DFA-derived) - edge_transition: Edge mode transition thresholds (Feature 002) - organization: Organization system coefficients (Feature 031) - ooda: OODA loop system coefficients (Feature 032) - class_system: Unified class system coefficients (Feature 038) - bifurcation: Bifurcation topology analysis coefficients (Feature 033) - infra_terrain: Terrain classification and biocapacity coefficients (Feature 036) - infrastructure: Infrastructure capacity and internet coefficients (Feature 036)

Parameters:

• crisis (CrisisDefines)

• mobilize (MobilizeDefines)

• economy (EconomyDefines)

• survival (SurvivalDefines)

• vitality (VitalityDefines)

• solidarity (SolidarityDefines)

• behavioral (BehavioralDefines)

• tension (TensionDefines)

• consciousness (ConsciousnessDefines)

• territory (TerritoryDefines)

• topology (TopologyDefines)

• metabolism (MetabolismDefines)

• struggle (StruggleDefines)

• carceral (CarceralDefines)

• endgame (EndgameDefines)

• initial (InitialDefines)

• precision (PrecisionDefines)

• timescale (TimescaleDefines)

• external_data (ExternalDataDefines)

• contradiction_field (ContradictionFieldDefines)

• reserve_army (ReserveArmyDefines)

• dispossession (DispossessionDefines)

• working_day (WorkingDayDefines)

• community (CommunityDefines)

• class_dynamics (ClassDynamicsDefines)

• edge_transition (EdgeTransitionDefines)

• lifecycle (LifecycleDefines)

• organization (OrganizationDefines)

• ooda (OODADefines)

• bifurcation (BifurcationDefines)

• infra_terrain (InfraTerrainDefines)

• infrastructure (InfrastructureDefines)

• rent_circuit (RentCircuitDefines)

• class_system (ClassSystemDefines)

• state_ai (StateApparatusAIDefines)

• institution (InstitutionDefines)

• move (MoveDefines)

• negotiate (NegotiateDefines)

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

crisis: CrisisDefines

mobilize: MobilizeDefines

economy: EconomyDefines

survival: SurvivalDefines

vitality: VitalityDefines

solidarity: SolidarityDefines

behavioral: BehavioralDefines

tension: TensionDefines

consciousness: ConsciousnessDefines

territory: TerritoryDefines

topology: TopologyDefines

metabolism: MetabolismDefines

struggle: StruggleDefines

carceral: CarceralDefines

endgame: EndgameDefines

initial: InitialDefines

precision: PrecisionDefines

timescale: TimescaleDefines

external_data: ExternalDataDefines

contradiction_field: ContradictionFieldDefines

reserve_army: ReserveArmyDefines

dispossession: DispossessionDefines

working_day: WorkingDayDefines

community: CommunityDefines

class_dynamics: ClassDynamicsDefines

edge_transition: EdgeTransitionDefines

lifecycle: LifecycleDefines

organization: OrganizationDefines

ooda: OODADefines

bifurcation: BifurcationDefines

infra_terrain: InfraTerrainDefines

infrastructure: InfrastructureDefines

rent_circuit: RentCircuitDefines

class_system: ClassSystemDefines

state_ai: StateApparatusAIDefines

institution: InstitutionDefines

move: MoveDefines

negotiate: NegotiateDefines

property SUPERWAGE_IMPACT: float

How much 1 unit of imperial extraction increases Core wealth.

property SOLIDARITY_SCALING: float

Multiplier for graph edge weights affecting Organization.

property REPRESSION_BASE: float

Base resistance to revolution in P(S|R) denominator.

property REVOLUTION_THRESHOLD: float

The tipping point for P(S|R) formula.

property DEFAULT_ORGANIZATION: float

Fallback organization value when not specified on entity.

property DEFAULT_REPRESSION_FACED: float

Fallback repression value when not specified on entity.

property DEFAULT_SUBSISTENCE: float

Fallback subsistence threshold when not specified on entity.

property NEGLIGIBLE_TRANSMISSION: float

Threshold below which transmissions are skipped as noise.

classmethod load_from_yaml(path)

Load GameDefines from a YAML file.

Parameters:

path (str | Path) – Path to the YAML file (absolute or relative)

Return type:

GameDefines

Returns:

GameDefines instance populated from YAML

Raises:

• FileNotFoundError – If the YAML file doesn’t exist

• yaml.YAMLError – If the YAML is malformed

• pydantic.ValidationError – If values fail validation

classmethod default_yaml_path()

Return the default path to defines.yaml.

Return type:

Path

Returns:

Path to src/babylon/data/defines.yaml

classmethod load_default()

Load GameDefines from the default YAML location.

Falls back to default values if file doesn’t exist.

Return type:

GameDefines

Returns:

GameDefines instance