Models
Core data models and types used throughout Babylon.
Pydantic models for the Babylon simulation engine.
This package contains the type system foundation: - enums: Categorical types (SocialRole, EdgeType, etc.) - types: Constrained value types (Probability, Currency, etc.) - entities: Game objects (Effect, Contradiction, Trigger, etc.) - components: Entity-Component system (Material, Vitality, Spatial, etc.)
All game state flows through these validated types.
- class babylon.models.SocialRole(*values)[source]
Bases:
StrEnumClass position in the world system.
Based on Marxist-Leninist-Maoist Third Worldist (MLM-TW) theory, classes are defined by their relationship to production AND their position in the imperial hierarchy.
- Values:
CORE_BOURGEOISIE: Owns means of production in imperial core PERIPHERY_PROLETARIAT: Sells labor in exploited periphery LABOR_ARISTOCRACY: Core workers benefiting from imperial rent PETTY_BOURGEOISIE: Small owners, professionals, shopkeepers LUMPENPROLETARIAT: Outside formal economy, precarious existence COMPRADOR_BOURGEOISIE: Intermediary class in periphery, collaborates with imperial core
- CORE_BOURGEOISIE = 'core_bourgeoisie'
- PERIPHERY_PROLETARIAT = 'periphery_proletariat'
- LABOR_ARISTOCRACY = 'labor_aristocracy'
- PETTY_BOURGEOISIE = 'petty_bourgeoisie'
- LUMPENPROLETARIAT = 'lumpenproletariat'
- COMPRADOR_BOURGEOISIE = 'comprador_bourgeoisie'
- class babylon.models.EdgeType(*values)[source]
Bases:
StrEnumNature of relationships between entities.
These are the fundamental relationship types that form edges in the simulation’s NetworkX graph.
- Values:
EXPLOITATION: Value extraction (imperial rent flows along these edges) SOLIDARITY: Mutual support and class consciousness REPRESSION: State violence directed at a class COMPETITION: Market rivalry between entities TRIBUTE: Value flow from periphery comprador to core (comprador keeps cut) WAGES: Core bourgeoisie paying core workers (super-wages from imperial rent) CLIENT_STATE: Imperial subsidy to maintain client state stability TENANCY: Occupant -> Territory relationship (Sprint 3.5.1) ADJACENCY: Territory -> Territory spatial connectivity (Sprint 3.5.1)
- EXPLOITATION = 'exploitation'
- SOLIDARITY = 'solidarity'
- REPRESSION = 'repression'
- COMPETITION = 'competition'
- TRIBUTE = 'tribute'
- WAGES = 'wages'
- CLIENT_STATE = 'client_state'
- TENANCY = 'tenancy'
- ADJACENCY = 'adjacency'
- class babylon.models.IntensityLevel(*values)[source]
Bases:
StrEnumIntensity scale for contradictions and tensions.
Contradictions exist on a spectrum from dormant (latent potential) to critical (imminent rupture). This enum provides discrete levels for game mechanics while the underlying simulation may use continuous float values.
- Values:
DORMANT: Contradiction exists but not yet manifest LOW: Minor tensions, easily managed MEDIUM: Noticeable conflict, requires attention HIGH: Serious crisis, intervention needed CRITICAL: Rupture imminent, phase transition likely
- DORMANT = 'dormant'
- LOW = 'low'
- MEDIUM = 'medium'
- HIGH = 'high'
- CRITICAL = 'critical'
- class babylon.models.ResolutionType(*values)[source]
Bases:
StrEnumHow contradictions can resolve.
Based on dialectical materialism, contradictions resolve through one of three mechanisms. The resolution type determines what happens to the system after a contradiction reaches critical intensity.
- Values:
SYNTHESIS: Dialectical resolution - opposites unite at higher level RUPTURE: Revolutionary break - system undergoes fundamental change SUPPRESSION: Forced dormancy - contradiction remains but is contained
- SYNTHESIS = 'synthesis'
- RUPTURE = 'rupture'
- SUPPRESSION = 'suppression'
- class babylon.models.SimulationConfig(**data)[source]
Bases:
BaseModelGlobal configuration for the simulation engine.
All formula coefficients and world parameters are stored here. The config is immutable (frozen) to ensure determinism during simulation runs.
- Parameters:
extraction_efficiency (Annotated[float, Ge(ge=0.0), Le(le=1.0)])
consciousness_sensitivity (Annotated[float, Ge(ge=0.0), Le(le=1.0)])
tension_accumulation_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)])
solidarity_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)])
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)])
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)])
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)])
superwage_ppp_impact (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 (DisplacementPriorityMode)
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)])
- extraction_efficiency
Alpha (α) in imperial rent formula. Controls how efficiently the core extracts value from periphery. Range: [0, 1], Default: 0.8
- consciousness_sensitivity
k in consciousness drift formula. Controls how quickly consciousness responds to material conditions. Range: [0, 1], Default: 0.5
- subsistence_threshold
Poverty line for acquiescence calculation. Below this wealth level, survival through compliance becomes impossible. Range: [0, inf), Default: 0.3
- survival_steepness
Controls sigmoid sharpness in acquiescence probability. Higher values mean sharper transition around subsistence threshold. Range: (0, inf), Default: 10.0
- repression_level
State capacity for violence. Reduces revolution probability in P(S|R) = cohesion / repression. Range: [0, 1], Default: 0.5
- initial_worker_wealth
Starting wealth for periphery worker entities. Range: [0, inf), Default: 0.5
- initial_owner_wealth
Starting wealth for core owner entities. Range: [0, inf), Default: 0.5
- loss_aversion_lambda
Kahneman-Tversky loss aversion coefficient. Humans feel losses ~2.25x more strongly than equivalent gains. Range: (0, inf), Default: 2.25
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
displacement_priority_mode:
DisplacementPriorityMode
- class babylon.models.ScenarioConfig(**data)[source]
Bases:
BaseModelConfiguration for multiverse scenario injection.
ScenarioConfig defines modifiers that transform a base WorldState and SimulationConfig into a specific counterfactual scenario. This enables deterministic exploration of parameter space without random sampling.
The model is frozen (immutable) to ensure scenarios remain constant throughout simulation runs.
- Parameters:
- name
Human-readable scenario identifier, must be unique within a multiverse run. Format: “{SWLevel}_{SolidarityLevel}_{RepressionLevel}”
- superwage_multiplier
Multiplier for extraction_efficiency in SimulationConfig. Default 1.0 (no change). Range: [0, inf). - 0.3 = Low superwage (periphery retains more value) - 1.5 = High superwage (aggressive extraction) Theory: “Superwage” refers to core worker wages above value produced, subsidized by imperial rent extraction.
- solidarity_index
Base strength for SOLIDARITY edges in topology. Default 0.5. Constrained to [0, 1] via Coefficient type. - 0.2 = Low solidarity (atomized workers) - 0.8 = High solidarity (strong worker networks)
- repression_capacity
State violence capacity modifier. Updates repression_faced on entities and repression_level in config. Default 0.5. Constrained to [0, 1] via Coefficient type. - 0.2 = Low repression (weak state) - 0.8 = High repression (police state)
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- name: str
- superwage_multiplier: float
- solidarity_index: Coefficient
- repression_capacity: Coefficient
- class babylon.models.WorldState(**data)[source]
Bases:
BaseModelImmutable snapshot of the simulation at a specific tick.
WorldState follows the Data/Logic separation principle: - State holds WHAT exists (pure data) - Engine determines HOW it transforms (pure logic)
This enables: - Determinism: Same state + same engine = same output - Replayability: Save initial state, replay entire history - Counterfactuals: Modify a parameter, run forward, compare - Testability: Feed state in, assert on state out
- Parameters:
entities (dict[str, SocialClass])
relationships (list[Relationship])
events (list[SimulationEvent])
economy (GlobalEconomy)
state_finances (dict[str, StateFinance])
- tick
Current turn number (0-indexed)
- entities
Map of entity ID to SocialClass (the nodes)
- territories
Map of territory ID to Territory (Layer 0 nodes)
- relationships
List of Relationship edges (the edges)
- event_log
Recent events for narrative/debugging (string format)
- events
Structured simulation events for analysis (Sprint 3.1)
- economy
Global economic state for dynamic balance (Sprint 3.4.4)
- add_entity(entity)[source]
Return new state with entity added.
- Parameters:
entity (
SocialClass) – SocialClass to add- Return type:
- Returns:
New WorldState with the entity included.
Example
new_state = state.add_entity(worker)
- add_event(event)[source]
Return new state with event appended to log.
- Parameters:
event (
str) – Event description string- Return type:
- Returns:
New WorldState with event in log.
Example
new_state = state.add_event(“Worker crossed poverty threshold”)
- add_relationship(relationship)[source]
Return new state with relationship added.
- Parameters:
relationship (
Relationship) – Relationship edge to add- Return type:
- Returns:
New WorldState with the relationship included.
Example
new_state = state.add_relationship(exploitation_edge)
- add_territory(territory)[source]
Return new state with territory added.
- Parameters:
territory (
Territory) – Territory to add (Layer 0 node)- Return type:
- Returns:
New WorldState with the territory included.
Example
new_state = state.add_territory(university_district)
- classmethod from_graph(G, tick, event_log=None, events=None)[source]
Reconstruct WorldState from NetworkX DiGraph.
- Parameters:
G – NetworkX DiGraph with node/edge data
tick – The tick number for the new state
event_log – Optional event log to preserve (backward compatibility)
events – Optional structured events to include (Sprint 3.1)
- Returns:
New WorldState with entities, territories, and relationships from graph.
- Return type:
Example
G = state.to_graph() # … modify graph … new_state = WorldState.from_graph(G, tick=state.tick + 1)
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- to_graph()[source]
Convert state to NetworkX DiGraph for formula application.
Nodes are entity/territory IDs with all fields as attributes. A _node_type marker distinguishes between node types: - _node_type=’social_class’ for SocialClass nodes - _node_type=’territory’ for Territory nodes
Edges are relationships with all Relationship fields as attributes.
Graph metadata (G.graph) contains: - economy: GlobalEconomy state (Sprint 3.4.4)
- Returns:
NetworkX DiGraph with nodes and edges from this state.
Example:
G = state.to_graph() for node_id, data in G.nodes(data=True): if data["_node_type"] == "social_class": data["wealth"] += 10 # Modify entity new_state = WorldState.from_graph(G, tick=state.tick + 1)
- property total_biocapacity: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)])]
Global sum of territory biocapacity.
- property total_consumption: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)])]
Global sum of consumption needs.
- tick: int
- entities: dict[str, SocialClass]
- territories: dict[str, Territory]
- relationships: list[Relationship]
- event_log: list[str]
- events: list[SimulationEvent]
- economy: GlobalEconomy
- state_finances: dict[str, StateFinance]
- class babylon.models.SocialClass(**data)[source]
Bases:
BaseModelA social class in the world system.
The fundamental unit of the simulation. Classes are defined by their relationship to production and their position in the imperial hierarchy.
This model uses Sprint 1 constrained types for automatic validation: - Currency: [0, inf) for wealth, subsistence_threshold - IdeologicalProfile: Multi-dimensional consciousness model (Sprint 3.4.3) - Probability: [0, 1] for survival probabilities and organization/repression
- Parameters:
id (Annotated[str, _PydanticGeneralMetadata(pattern='^C[0-9]{3}$')])
role (SocialRole)
description (str)
ideology (IdeologicalProfile)
- id
Unique identifier matching pattern ^C[0-9]{3}$
- name
Human-readable name for the class
- role
Position in the world system (SocialRole enum)
- description
Optional detailed description
- wealth
Economic resources (Currency, default 10.0)
- ideology
Multi-dimensional ideological profile (IdeologicalProfile)
- p_acquiescence
P(S|A) - survival probability through acquiescence (Probability)
- p_revolution
P(S|R) - survival probability through revolution (Probability)
- subsistence_threshold
Minimum wealth for survival (Currency, default 5.0)
- organization
Collective cohesion/class consciousness (Probability, default 0.1)
- repression_faced
State violence directed at this class (Probability, default 0.5)
- Legacy Compatibility:
If a float value is passed for ideology, it will be automatically converted to an IdeologicalProfile using from_legacy_ideology().
- property consumption_needs: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)])]
Total consumption required per tick (Wealth-independent demand).
- property economic: EconomicComponent
Return economic component view (computed, not live).
- property ideological: IdeologicalComponent
Return ideological component view (computed, not live).
- property material_conditions: MaterialConditionsComponent
Return material conditions component view (computed, not live).
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'str_strip_whitespace': True, 'validate_assignment': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- property survival: SurvivalComponent
Return survival component view (computed, not live).
- classmethod unpack_components_and_convert_legacy(data)[source]
Unpack component objects and convert legacy ideology to IdeologicalProfile.
-
role:
SocialRole
-
ideology:
IdeologicalProfile
- class babylon.models.Relationship(**data)[source]
Bases:
BaseModelA directed edge between two entities.
Represents flows of value, solidarity, or repression between classes. In Phase 1, the primary relationship is exploitation: value flows from the periphery worker (source) to the core owner (target).
This model uses Sprint 1 constrained types for automatic validation: - Currency: [0, inf) for value_flow - Intensity: [0, 1] for tension
- Parameters:
- source_id
Origin entity ID (value/action flows FROM here)
- target_id
Destination entity ID (value/action flows TO here)
- edge_type
Nature of the relationship (EdgeType enum)
- value_flow
Imperial rent or value transfer amount (Currency, default 0.0)
- tension
Dialectical tension/contradiction intensity (Intensity, default 0.0)
- description
Optional description of the relationship
- property edge_data: dict[str, object]
Return edge attributes dict for NetworkX, excluding IDs.
Usage:
G.add_edge(*relationship.edge_tuple, **relationship.edge_data)
- property edge_tuple: tuple[str, str]
Return (source_id, target_id) tuple for NetworkX edge creation.
Usage:
G.add_edge(*relationship.edge_tuple, **relationship.edge_data)
- property flow: FlowComponent
Return flow component view (computed, not live).
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'str_strip_whitespace': True, 'validate_assignment': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- classmethod unpack_flow_component(data)[source]
Unpack flow component into flat fields if provided.
- class babylon.models.IdeologicalProfile(**data)[source]
Bases:
BaseModelMulti-dimensional ideological state of a social class.
Sprint 3.4.3 (George Jackson Refactor): This model replaces the scalar ideology field with a multi-dimensional consciousness model.
The key insight: “Fascism is the defensive form of capitalism.” - Agitation + Solidarity -> Class Consciousness (Revolutionary Path) - Agitation + No Solidarity -> National Identity (Fascist Path)
- Parameters:
- class_consciousness
Relationship to Capital [0.0=False, 1.0=Revolutionary] How clearly the class understands its position relative to capital.
- national_identity
Relationship to State/Tribe [0.0=Internationalist, 1.0=Fascist] How strongly the class identifies with nation/race over class.
- agitation
Raw political energy from crisis [0.0, inf) Accumulated energy from falling wages, crisis conditions. Routes to either axis based on solidarity_pressure.
- classmethod from_legacy_ideology(ideology_value)[source]
Convert legacy scalar ideology [-1, 1] to IdeologicalProfile.
Legacy mapping: - ideology=-1 (revolutionary) -> class_consciousness=1.0, national_identity=0.0 - ideology=0 (neutral) -> class_consciousness=0.5, national_identity=0.5 - ideology=+1 (reactionary) -> class_consciousness=0.0, national_identity=1.0
- Parameters:
ideology_value (
float) – Legacy ideology scalar in range [-1.0, 1.0]- Return type:
- Returns:
IdeologicalProfile with mapped values
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class babylon.models.GlobalEconomy(**data)[source]
Bases:
BaseModelSystem-wide economic state for dynamic balance mechanics.
The GlobalEconomy model tracks the “Gas Tank” (imperial rent pool) and the “Driver” parameters (wage rate, repression level) that the bourgeoisie can adjust based on pool levels and tension.
- Parameters:
- imperial_rent_pool
Accumulated imperial rent available for redistribution. Fed by TRIBUTE inflows, depleted by WAGES and CLIENT_STATE outflows. Default: 100.0 Currency.
- current_super_wage_rate
Dynamic wage rate for WAGES edges. Adjusts based on pool level and tension via decision heuristics. Range: [0.05, 0.35]. Default: 0.20 (20%).
- current_repression_level
System-wide repression modifier. Blended with per-class repression_faced values. Increases during austerity/crisis periods. Range: [0.0, 1.0]. Default: 0.5.
- Pool Flow Logic:
inflow = sum(tribute reaching Core Bourgeoisie after comprador cut) outflow = wages_paid + subsidy_paid delta = inflow - outflow new_pool = old_pool + delta
- Decision Thresholds (Aggressive):
pool_ratio >= 0.7: PROSPERITY (can increase wages) pool_ratio < 0.3: AUSTERITY (cut wages or increase repression) pool_ratio < 0.1: CRISIS (emergency measures, emit event)
- model_config: ClassVar[ConfigDict] = {'frozen': True, 'populate_by_name': True, 'validate_by_alias': True, 'validate_by_name': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class babylon.models.Effect(**data)[source]
Bases:
BaseModelA modification to game state.
Effects are the atoms of change - they modify single attributes on single targets by specific amounts. Every state change in the simulation should be expressible as one or more Effects.
- Parameters:
- target_id
ID of the entity to modify
- attribute
Name of the attribute to change
- operation
How to modify the value
- magnitude
Amount of change (interpretation depends on operation)
- description
Human-readable explanation of why this effect occurs
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class babylon.models.ContradictionState(**data)[source]
Bases:
BaseModelThe current state of a dialectical contradiction.
A contradiction is a structural tension between two forces that cannot both be satisfied within the current system. This is the primary model for tracking contradictions.
- Parameters:
- id
Unique identifier (flexible format for runtime use)
- name
Human-readable name
- description
Detailed explanation
- thesis
The dominant position
- antithesis
The opposing position
- tension
Current tension level [0, 1]
- momentum
Rate of change [-1, 1]
- is_principal
Is this the principal contradiction?
- resolved
Has this been resolved?
- property intensity_level: IntensityLevel
Convert continuous tension to discrete IntensityLevel.
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class babylon.models.ResolutionOutcome(**data)[source]
Bases:
BaseModelThe result of a contradiction reaching resolution.
When a contradiction resolves (tension reaches 0 or 1), this model captures what happened and the consequences.
- Parameters:
- contradiction_id
ID of the resolved contradiction
- resolution_type
How it resolved (synthesis, rupture, suppression)
- new_contradictions
IDs of contradictions spawned by resolution
- system_changes
Metric deltas caused by resolution
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
resolution_type:
ResolutionType
- class babylon.models.Contradiction(**data)[source]
Bases:
BaseModelFull contradiction model with entities and resolution methods.
This is a more detailed model for contradictions that includes references to entities and resolution mechanics. Used for game content definition.
- Parameters:
id (Annotated[str, _PydanticGeneralMetadata(pattern='^CON[0-9]{3}$')])
name (str)
description (str)
principal_aspect_id (str | None)
secondary_aspect_id (str | None)
principal_contradiction_id (str | None)
universality (Annotated[str, _PydanticGeneralMetadata(pattern='^(Universal|Particular)$')])
particularity (str)
antagonism (Annotated[str, _PydanticGeneralMetadata(pattern='^(Primary|Secondary)$')])
intensity (IntensityLevel)
state (Annotated[str, _PydanticGeneralMetadata(pattern='^(Active|Resolved|Latent)$')])
potential_for_transformation (IntensityLevel)
intensity_history (list[Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Contradiction intensity from dormant (0) to rupture (1)', metadata=[Ge(ge=0.0), Le(le=1.0)])]])
- id
Unique identifier
- name
Human-readable name
- description
Detailed explanation
- entity_ids
IDs of entities involved in this contradiction
- universality
Universal or Particular
- particularity
Domain (Economic, Political, etc.)
- principal_contradiction_id
ID of the principal contradiction (if this is secondary)
- principal_aspect_id
ID of the entity representing the principal aspect
- secondary_aspect_id
ID of the entity representing the secondary aspect
- antagonism
Primary or Secondary antagonism
- intensity
Current intensity level
- state
Active, Resolved, or Latent
- potential_for_transformation
How likely to transform
- conditions_for_transformation
What conditions must be met
- resolution_methods
Mapping of method names to Effects
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
intensity:
IntensityLevel
-
potential_for_transformation:
IntensityLevel
- class babylon.models.Trigger(**data)[source]
Bases:
BaseModelA condition that can trigger game events.
Triggers define when events should occur based on game state. They can have multiple conditions that must all be met (AND logic) or any one met (OR logic).
- Parameters:
- id
Unique identifier
- description
Human-readable description
- trigger_type
Category of trigger (economic, political, social, etc.)
- conditions
List of conditions to evaluate
- logic
How to combine conditions (all must pass or any must pass)
- parameters
Optional parameters for condition evaluation
- cooldown_turns
Minimum turns between activations (0 = no cooldown)
- last_triggered_turn
Turn when this was last triggered
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
conditions:
list[TriggerCondition]
- class babylon.models.TriggerCondition(**data)[source]
Bases:
BaseModelA single condition that can be evaluated against game state.
Conditions specify what to check, how to compare, and what threshold to use. Multiple conditions can be combined in a Trigger.
- Parameters:
- path
Dot-notation path to the value in game state (e.g., “economy.gini_coefficient”)
- operator
Comparison operator
- threshold
Value to compare against
- description
Human-readable explanation
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class babylon.models.Component(*args, **kwargs)[source]
Bases:
ProtocolProtocol defining the interface for all component types.
All components in the Babylon simulation must implement this protocol. The protocol requires a component_type property that returns a string identifier for the component type.
This protocol is runtime-checkable, meaning you can use isinstance() to verify that an object implements the Component interface.
Example
>>> class MyComponent(BaseModel): ... model_config = ConfigDict(frozen=True) ... @property ... def component_type(self) -> str: ... return "my_component" ... >>> instance = MyComponent() >>> isinstance(instance, Component) True
- __init__(*args, **kwargs)
- class babylon.models.MaterialComponent(**data)[source]
Bases:
BaseModelMaterial conditions of an entity.
Tracks the economic and material state of an entity including: - Accumulated wealth (Currency) - Available resources (Currency) - Control over means of production (Probability)
All values use constrained types for automatic validation: - wealth, resources: Currency [0, inf) - means_of_production: Probability [0, 1]
This component is immutable (frozen) to ensure state integrity.
- Parameters:
- wealth
Accumulated economic resources (default: 10.0)
- resources
Available material resources (default: 0.0)
- means_of_production
Control over productive apparatus [0, 1] (default: 0.0)
- property component_type: str
Return the component type identifier.
- Returns:
The string ‘material’ identifying this component type.
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class babylon.models.VitalityComponent(**data)[source]
Bases:
BaseModelPopulation and survival needs of an entity.
Tracks the demographic and subsistence state of an entity: - Population size (Currency) - Minimum resources required for survival (Currency)
All values use constrained types for automatic validation: - population, subsistence_needs: Currency [0, inf)
This component is immutable (frozen) to ensure state integrity.
- population
Size of the population (default: 1.0)
- subsistence_needs
Resources needed for survival (default: 5.0)
- property component_type: str
Return the component type identifier.
- Returns:
The string ‘vitality’ identifying this component type.
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class babylon.models.SpatialComponent(**data)[source]
Bases:
BaseModelLocation and mobility of an entity.
Tracks the spatial state of an entity: - Geographic/topological location identifier (string) - Ability to relocate (Probability)
All numeric values use constrained types for automatic validation: - mobility: Probability [0, 1]
This component is immutable (frozen) to ensure state integrity.
- location_id
Geographic or topological location identifier (default: “”)
- mobility
Ability to relocate [0=immobile, 1=fully mobile] (default: 0.5)
- property component_type: str
Return the component type identifier.
- Returns:
The string ‘spatial’ identifying this component type.
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class babylon.models.IdeologicalComponent(**data)[source]
Bases:
BaseModelMulti-dimensional ideological state (George Jackson Model).
Tracks the ideological state of an entity using three axes: - Relationship to Capital (class consciousness) - Relationship to State/Tribe (national identity) - Accumulated political energy (agitation)
The bifurcation mechanism: - When wages fall, agitation accumulates - If solidarity edges exist: agitation routes to class_consciousness - If no solidarity edges: agitation routes to national_identity
All values use constrained floats for automatic validation: - class_consciousness: [0, 1] (0=False Consciousness, 1=Revolutionary) - national_identity: [0, 1] (0=Internationalist, 1=Fascist) - agitation: [0, inf) (no upper bound - accumulates during crises)
This component is immutable (frozen) to ensure state integrity.
- Parameters:
- class_consciousness
Relationship to Capital [0=False, 1=Revolutionary] (default: 0.0)
- national_identity
Relationship to State [0=Internationalist, 1=Fascist] (default: 0.5)
- agitation
Raw political energy from wage crises [0, inf) (default: 0.0)
- property component_type: str
Return the component type identifier.
- Returns:
The string ‘ideological’ identifying this component type.
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class babylon.models.OrganizationComponent(**data)[source]
Bases:
BaseModelOrganizational capacity of an entity.
Tracks the organizational state of an entity: - Internal unity and coordination (Probability) - Quality of organizational leadership (Probability)
All values use constrained types for automatic validation: - cohesion: Probability [0, 1] - cadre_level: Probability [0, 1]
This component is immutable (frozen) to ensure state integrity.
- Parameters:
- cohesion
Internal unity and coordination [0=atomized, 1=unified] (default: 0.1)
- cadre_level
Quality of organizational leadership [0=none, 1=elite] (default: 0.0)
- property component_type: str
Return the component type identifier.
- Returns:
The string ‘organization’ identifying this component type.
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- class babylon.models.TopologySnapshot(**data)[source]
Bases:
BaseModelMetrics snapshot of the solidarity subgraph at a specific tick.
Captures the topological state of SOLIDARITY edges in the social graph, enabling detection of phase transitions from atomized (gaseous) to condensed (liquid) movement states.
- tick
Simulation tick when snapshot was taken
- num_components
Number of disconnected subgraphs (solidarity cells)
- max_component_size
Size of the largest connected component (L_max)
- total_nodes
Total number of social_class nodes (N)
- percolation_ratio
L_max / N, measures giant component dominance
- potential_liquidity
Count of SOLIDARITY edges > 0.1 (sympathizers)
- actual_liquidity
Count of SOLIDARITY edges > 0.5 (cadre)
- is_resilient
Whether movement survives 20% purge (optional)
- Interpretation (4-Phase Model):
percolation_ratio < 0.1: Gaseous (atomized, no coordination)
0.1 <= percolation_ratio < 0.5: Transitional (emerging structure)
percolation_ratio >= 0.5 AND cadre_density < 0.5: Liquid (mass movement)
percolation_ratio >= 0.5 AND cadre_density >= 0.5: Solid (vanguard party)
potential >> actual: Broad but brittle (lacks cadre discipline)
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- tick: int
- num_components: int
- max_component_size: int
- total_nodes: int
- percolation_ratio: Probability
- potential_liquidity: int
- actual_liquidity: int
- cadre_density: float
- is_resilient: bool | None
- class babylon.models.ResilienceResult(**data)[source]
Bases:
BaseModelResult of purge simulation (Sword of Damocles test).
Tests whether the solidarity network survives targeted removal of a percentage of nodes. A resilient network maintains its giant component after losing key members.
- Parameters:
- is_resilient
True if post-purge L_max > threshold of original L_max
- original_max_component
Size of L_max before purge
- post_purge_max_component
Size of L_max after purge
- removal_rate
Fraction of nodes removed (e.g., 0.2 = 20%)
- survival_threshold
Required fraction of original L_max to survive
- seed
RNG seed for reproducibility (None if random)
- Interpretation:
is_resilient=True: Network can survive targeted repression
is_resilient=False: “Sword of Damocles” - purge would destroy movement
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- is_resilient: bool
- original_max_component: int
- post_purge_max_component: int
- removal_rate: float
- survival_threshold: float
- seed: int | None
- class babylon.models.EntityMetrics(**data)[source]
Bases:
BaseModelMetrics snapshot for a single entity at a specific tick.
Captures wealth, consciousness, and survival probabilities for analysis and visualization.
- Parameters:
consciousness (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
national_identity (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
agitation (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
p_acquiescence (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
p_revolution (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
organization (Annotated[float, Ge(ge=0.0), Le(le=1.0), 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].
- wealth: Currency
- consciousness: Probability
- national_identity: Probability
- agitation: Probability
- p_acquiescence: Probability
- p_revolution: Probability
- organization: Probability
- class babylon.models.EdgeMetrics(**data)[source]
Bases:
BaseModelMetrics snapshot for relationship edges at a specific tick.
Captures tension, value flows, and solidarity strength for analysis and visualization.
- Parameters:
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- exploitation_tension: Probability
- exploitation_rent: Currency
- tribute_flow: Currency
- wages_paid: Currency
- solidarity_strength: Probability
- class babylon.models.TickMetrics(**data)[source]
Bases:
BaseModelComplete metrics snapshot for a single simulation tick.
Aggregates entity and edge metrics for comprehensive tick analysis.
- Parameters:
p_w (EntityMetrics | None)
p_c (EntityMetrics | None)
c_b (EntityMetrics | None)
c_w (EntityMetrics | None)
edges (EdgeMetrics)
imperial_rent_pool (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
global_tension (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
current_repression_level (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
pool_ratio (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
topology (TopologySummary | None)
consciousness_gap (Annotated[float, Ge(ge=-1.0), Le(le=1.0)])
wealth_gap (float)
total_biocapacity (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
total_consumption (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- tick: int
- p_w: EntityMetrics | None
- p_c: EntityMetrics | None
- c_b: EntityMetrics | None
- c_w: EntityMetrics | None
- edges: EdgeMetrics
- imperial_rent_pool: Currency
- global_tension: Probability
- current_super_wage_rate: float
- current_repression_level: Probability
- pool_ratio: Probability
- topology: TopologySummary | None
- consciousness_gap: float
- wealth_gap: float
- overshoot_ratio: float
- total_biocapacity: Currency
- total_consumption: Currency
- class babylon.models.SweepSummary(**data)[source]
Bases:
BaseModelSummary statistics for a completed simulation run.
Aggregates metrics across all ticks for parameter sweep analysis.
- Parameters:
outcome (Literal['SURVIVED', 'DIED', 'ERROR'])
max_tension (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
peak_p_w_consciousness (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
peak_c_w_consciousness (Annotated[float, Ge(ge=0.0), Le(le=1.0), 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].
- ticks_survived: int
- outcome: Literal['SURVIVED', 'DIED', 'ERROR']
- final_p_w_wealth: Currency
- final_p_c_wealth: Currency
- final_c_b_wealth: Currency
- final_c_w_wealth: Currency
- max_tension: Probability
- crossover_tick: int | None
- cumulative_rent: Currency
- peak_p_w_consciousness: Probability
- peak_c_w_consciousness: Probability
Types
Constrained value types for the Babylon simulation.
These Annotated type aliases provide runtime validation for numeric values. Using Pydantic v2’s Annotated pattern allows these types to be used directly in any BaseModel field.
- Types defined:
Probability: [0.0, 1.0] - for P(S|A), P(S|R), tension Ideology: [-1.0, 1.0] - revolutionary to reactionary spectrum Currency: [0.0, inf) - wealth, wages, rent, GDP Intensity: [0.0, 1.0] - contradiction intensity Coefficient: [0.0, 1.0] - formula parameters (alpha, lambda, k) Ratio: (0.0, inf) - wage ratios, exchange ratios
- Usage:
from babylon.models.types import Probability, Currency
- class SurvivalState(BaseModel):
p_acquiescence: Probability # Automatically validated [0, 1] wealth: Currency # Automatically validated [0, inf)
All types serialize to plain floats in JSON for compatibility with the Ledger (SQLite) and NetworkX graph storage.
- babylon.models.types.Probability
[0.0, 1.0]
A probability represents the likelihood of an event occurring. Used for: - P(S|A): Probability of survival through acquiescence - P(S|R): Probability of survival through revolution - tension: Contradiction tension level
Boundary values: - 0.0 = impossible event - 1.0 = certain event
- Type:
Probability
alias of
Annotated[float, FieldInfo(annotation=NoneType, required=True, description=’Value in range [0.0, 1.0] representing likelihood’, metadata=[Ge(ge=0.0), Le(le=1.0)])]
- babylon.models.types.Ideology
[-1.0, 1.0]
A class’s ideological position on the revolutionary-reactionary spectrum. Based on consciousness drift formula: dΨc/dt = k(1 - Wc/Vc) - λΨc
Boundary values: - -1.0 = fully revolutionary (class conscious, anti-capitalist) - +1.0 = fully reactionary (false consciousness, pro-status-quo) - 0.0 = neutral/apolitical
- Type:
Ideology
alias of
Annotated[float, FieldInfo(annotation=NoneType, required=True, description=’Ideological position from revolutionary (-1) to reactionary (+1)’, metadata=[Ge(ge=-1.0), Le(le=1.0)])]
- babylon.models.types.Currency
[0.0, inf)
Economic value that cannot be negative. Used for: - wealth: Accumulated resources - wages: Payment for labor - rent: Imperial rent (Φ) - GDP: Aggregate output
Note: Debt is not modeled as negative currency but as a separate relationship/obligation between entities.
- Type:
Currency
alias of
Annotated[float, FieldInfo(annotation=NoneType, required=True, description=’Non-negative economic value (wealth, wages, rent, GDP)’, metadata=[Ge(ge=0.0)])]
- babylon.models.types.Intensity
[0.0, 1.0]
The intensity of a dialectical contradiction. When intensity reaches 1.0, the contradiction triggers a phase transition (synthesis, rupture, or suppression).
Boundary values: - 0.0 = dormant (contradiction exists but not manifest) - 1.0 = rupture threshold (phase transition imminent)
- Type:
Intensity
alias of
Annotated[float, FieldInfo(annotation=NoneType, required=True, description=’Contradiction intensity from dormant (0) to rupture (1)’, metadata=[Ge(ge=0.0), Le(le=1.0)])]
- babylon.models.types.Coefficient
[0.0, 1.0]
A parameter that modifies the strength of an effect in formulas. Used for: - α (alpha): Extraction efficiency in imperial rent - λ (lambda): Decay rate in consciousness drift - k: Sensitivity coefficient in survival calculus
Boundary values: - 0.0 = no effect - 1.0 = maximum effect
- Type:
Coefficient
alias of
Annotated[float, FieldInfo(annotation=NoneType, required=True, description=’Formula parameter in range [0, 1]’, metadata=[Ge(ge=0.0), Le(le=1.0)])]
- babylon.models.types.Ratio
(0.0, inf)
A ratio comparing two quantities. Zero is invalid because ratios typically represent division results.
Used for: - Wc/Vc: Labor aristocracy ratio (wages to value produced) - ε: Exchange ratio in unequal exchange
Interpretation: - Ratio = 1.0: Equal exchange / fair wages - Ratio > 1.0: Core receives more than it gives (exploitation) - Ratio < 1.0: Core receives less than it gives
- Type:
Ratio
alias of
Annotated[float, FieldInfo(annotation=NoneType, required=True, description=’Positive ratio comparing two quantities’, metadata=[Gt(gt=0.0)])]
World State
WorldState model for the Babylon simulation.
WorldState is an immutable snapshot of the entire simulation at a specific tick. It encapsulates: - All entities (social classes) as nodes - All territories (strategic sectors) as nodes - All relationships (value flows, tensions) as edges - A tick counter for temporal tracking - An event log for narrative/debugging
- The state is designed for functional transformation:
new_state = step(old_state, config)
Sprint 4: Phase 2 game loop state container with NetworkX integration. Sprint 3.5.3: Territory integration for Layer 0.
- class babylon.models.world_state.WorldState(**data)[source]
Bases:
BaseModelImmutable snapshot of the simulation at a specific tick.
WorldState follows the Data/Logic separation principle: - State holds WHAT exists (pure data) - Engine determines HOW it transforms (pure logic)
This enables: - Determinism: Same state + same engine = same output - Replayability: Save initial state, replay entire history - Counterfactuals: Modify a parameter, run forward, compare - Testability: Feed state in, assert on state out
- Parameters:
entities (dict[str, SocialClass])
relationships (list[Relationship])
events (list[SimulationEvent])
economy (GlobalEconomy)
state_finances (dict[str, StateFinance])
- tick
Current turn number (0-indexed)
- entities
Map of entity ID to SocialClass (the nodes)
- territories
Map of territory ID to Territory (Layer 0 nodes)
- relationships
List of Relationship edges (the edges)
- event_log
Recent events for narrative/debugging (string format)
- events
Structured simulation events for analysis (Sprint 3.1)
- economy
Global economic state for dynamic balance (Sprint 3.4.4)
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- tick: int
- entities: dict[str, SocialClass]
- territories: dict[str, Territory]
- relationships: list[Relationship]
- event_log: list[str]
- events: list[SimulationEvent]
- economy: GlobalEconomy
- state_finances: dict[str, StateFinance]
- to_graph()[source]
Convert state to NetworkX DiGraph for formula application.
Nodes are entity/territory IDs with all fields as attributes. A _node_type marker distinguishes between node types: - _node_type=’social_class’ for SocialClass nodes - _node_type=’territory’ for Territory nodes
Edges are relationships with all Relationship fields as attributes.
Graph metadata (G.graph) contains: - economy: GlobalEconomy state (Sprint 3.4.4)
- Returns:
NetworkX DiGraph with nodes and edges from this state.
Example:
G = state.to_graph() for node_id, data in G.nodes(data=True): if data["_node_type"] == "social_class": data["wealth"] += 10 # Modify entity new_state = WorldState.from_graph(G, tick=state.tick + 1)
- classmethod from_graph(G, tick, event_log=None, events=None)[source]
Reconstruct WorldState from NetworkX DiGraph.
- Parameters:
G – NetworkX DiGraph with node/edge data
tick – The tick number for the new state
event_log – Optional event log to preserve (backward compatibility)
events – Optional structured events to include (Sprint 3.1)
- Returns:
New WorldState with entities, territories, and relationships from graph.
- Return type:
Example
G = state.to_graph() # … modify graph … new_state = WorldState.from_graph(G, tick=state.tick + 1)
- add_entity(entity)[source]
Return new state with entity added.
- Parameters:
entity (
SocialClass) – SocialClass to add- Return type:
- Returns:
New WorldState with the entity included.
Example
new_state = state.add_entity(worker)
- add_territory(territory)[source]
Return new state with territory added.
- Parameters:
territory (
Territory) – Territory to add (Layer 0 node)- Return type:
- Returns:
New WorldState with the territory included.
Example
new_state = state.add_territory(university_district)
- add_relationship(relationship)[source]
Return new state with relationship added.
- Parameters:
relationship (
Relationship) – Relationship edge to add- Return type:
- Returns:
New WorldState with the relationship included.
Example
new_state = state.add_relationship(exploitation_edge)
- add_event(event)[source]
Return new state with event appended to log.
- Parameters:
event (
str) – Event description string- Return type:
- Returns:
New WorldState with event in log.
Example
new_state = state.add_event(“Worker crossed poverty threshold”)
- property total_biocapacity: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)])]
Global sum of territory biocapacity.
Configuration
Simulation configuration for the Babylon engine.
SimulationConfig holds all global coefficients and parameters used by the formulas in babylon.systems.formulas. This model provides: - Type-safe validation of all parameters - Sensible defaults matching ai-docs/game-loop-architecture.yaml - Immutability during simulation runs - JSON serialization for save/load
Sprint 3: Phase 2 game loop configuration.
- class babylon.models.config.SimulationConfig(**data)[source]
Bases:
BaseModelGlobal configuration for the simulation engine.
All formula coefficients and world parameters are stored here. The config is immutable (frozen) to ensure determinism during simulation runs.
- Parameters:
extraction_efficiency (Annotated[float, Ge(ge=0.0), Le(le=1.0)])
consciousness_sensitivity (Annotated[float, Ge(ge=0.0), Le(le=1.0)])
tension_accumulation_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)])
solidarity_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)])
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)])
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)])
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)])
superwage_ppp_impact (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 (DisplacementPriorityMode)
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)])
- extraction_efficiency
Alpha (α) in imperial rent formula. Controls how efficiently the core extracts value from periphery. Range: [0, 1], Default: 0.8
- consciousness_sensitivity
k in consciousness drift formula. Controls how quickly consciousness responds to material conditions. Range: [0, 1], Default: 0.5
- subsistence_threshold
Poverty line for acquiescence calculation. Below this wealth level, survival through compliance becomes impossible. Range: [0, inf), Default: 0.3
- survival_steepness
Controls sigmoid sharpness in acquiescence probability. Higher values mean sharper transition around subsistence threshold. Range: (0, inf), Default: 10.0
- repression_level
State capacity for violence. Reduces revolution probability in P(S|R) = cohesion / repression. Range: [0, 1], Default: 0.5
- initial_worker_wealth
Starting wealth for periphery worker entities. Range: [0, inf), Default: 0.5
- initial_owner_wealth
Starting wealth for core owner entities. Range: [0, inf), Default: 0.5
- loss_aversion_lambda
Kahneman-Tversky loss aversion coefficient. Humans feel losses ~2.25x more strongly than equivalent gains. Range: (0, inf), Default: 2.25
- model_config: ClassVar[ConfigDict] = {'frozen': True}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
-
displacement_priority_mode:
DisplacementPriorityMode