babylon.models.metrics

Metrics data models for simulation observation and analysis.

These Pydantic models define the contract for MetricsCollector output, enabling unified metrics collection between the parameter sweeper and dashboard components.

Sprint 4.1: Phase 4 Dashboard/Sweeper unification. Sprint 4.1B: Expose meaningful metrics (economy drivers, topology, differentials).

Classes

EdgeMetrics(**data)

Metrics snapshot for relationship edges at a specific tick.

EntityMetrics(**data)

Metrics snapshot for a single entity at a specific tick.

SweepSummary(**data)

Summary statistics for a completed simulation run.

TickMetrics(**data)

Complete metrics snapshot for a single simulation tick.

TopologySummary(**data)

Topology metrics summary for phase transition detection.
class babylon.models.metrics.EntityMetrics(**data)[source]

Bases: BaseModel

Metrics snapshot for a single entity at a specific tick.

Captures wealth, consciousness, and survival probabilities for analysis and visualization.

Parameters:

  • wealth (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • consciousness (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • national_identity (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • agitation (float)

  • p_acquiescence (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • p_revolution (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • organization (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • population (int)

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

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

wealth: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
consciousness: 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)]), AfterValidator(func=quantize)]
national_identity: 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)]), AfterValidator(func=quantize)]
agitation: float
p_acquiescence: 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)]), AfterValidator(func=quantize)]
p_revolution: 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)]), AfterValidator(func=quantize)]
organization: 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)]), AfterValidator(func=quantize)]
population: int
class babylon.models.metrics.EdgeMetrics(**data)[source]

Bases: BaseModel

Metrics snapshot for relationship edges at a specific tick.

Captures tension, value flows, and solidarity strength for analysis and visualization.

Parameters:

  • exploitation_tension (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • exploitation_rent (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • tribute_flow (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • wages_paid (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • solidarity_strength (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

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

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

exploitation_tension: 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)]), AfterValidator(func=quantize)]
exploitation_rent: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
tribute_flow: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
wages_paid: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
solidarity_strength: 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)]), AfterValidator(func=quantize)]
class babylon.models.metrics.TopologySummary(**data)[source]

Bases: BaseModel

Topology metrics summary for phase transition detection.

Captures the topological phase state of the simulation, including percolation ratio, cadre density, and phase classification.

Parameters:

  • percolation_ratio (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • cadre_density (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • num_components (int)

  • phase (Literal['gaseous', 'transitional', 'liquid', 'solid'])

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

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

percolation_ratio: 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)]), AfterValidator(func=quantize)]
cadre_density: 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)]), AfterValidator(func=quantize)]
num_components: int
phase: Literal['gaseous', 'transitional', 'liquid', 'solid']
class babylon.models.metrics.TickMetrics(**data)[source]

Bases: BaseModel

Complete metrics snapshot for a single simulation tick.

Aggregates entity and edge metrics for comprehensive tick analysis.

Parameters:

  • tick (int)

  • p_w (EntityMetrics | None)

  • p_c (EntityMetrics | None)

  • c_b (EntityMetrics | None)

  • c_w (EntityMetrics | None)

  • edges (EdgeMetrics)

  • imperial_rent_pool (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • global_tension (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • current_super_wage_rate (float)

  • current_repression_level (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • pool_ratio (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • topology (TopologySummary | None)

  • consciousness_gap (float)

  • wealth_gap (float)

  • overshoot_ratio (float)

  • total_biocapacity (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • total_consumption (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

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: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
global_tension: 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)]), AfterValidator(func=quantize)]
current_super_wage_rate: float
current_repression_level: 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)]), AfterValidator(func=quantize)]
pool_ratio: 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)]), AfterValidator(func=quantize)]
topology: TopologySummary | None
consciousness_gap: float
wealth_gap: float
overshoot_ratio: float
total_biocapacity: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
total_consumption: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
class babylon.models.metrics.SweepSummary(**data)[source]

Bases: BaseModel

Summary statistics for a completed simulation run.

Aggregates metrics across all ticks for parameter sweep analysis.

Parameters:

  • ticks_survived (int)

  • outcome (Literal['SURVIVED', 'DIED', 'ERROR'])

  • final_p_w_wealth (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • final_p_c_wealth (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • final_c_b_wealth (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • final_c_w_wealth (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • max_tension (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • crossover_tick (int | None)

  • cumulative_rent (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • peak_p_w_consciousness (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

  • peak_c_w_consciousness (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)]), AfterValidator(func=~babylon.kernel.math.quantize)])

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: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
final_p_c_wealth: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
final_c_b_wealth: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
final_c_w_wealth: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
max_tension: 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)]), AfterValidator(func=quantize)]
crossover_tick: int | None
cumulative_rent: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Non-negative economic value (wealth, wages, rent, GDP)', metadata=[Ge(ge=0.0)]), AfterValidator(func=quantize)]
peak_p_w_consciousness: 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)]), AfterValidator(func=quantize)]
peak_c_w_consciousness: 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)]), AfterValidator(func=quantize)]