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, Ge(ge=0.0), Ge(ge=0.0)])
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.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, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
exploitation_rent (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
tribute_flow (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
wages_paid (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
solidarity_strength (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].

exploitation_tension: Probability

exploitation_rent: Currency

tribute_flow: Currency

wages_paid: Currency

solidarity_strength: Probability

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, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
cadre_density (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
num_components (Annotated[int, Ge(ge=0)])
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: Probability

cadre_density: Probability

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 (Annotated[int, Ge(ge=0)])
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_super_wage_rate (Annotated[float, Ge(ge=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)
overshoot_ratio (Annotated[float, Ge(ge=0)])
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.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 (Annotated[int, Ge(ge=0)])
outcome (Literal['SURVIVED', 'DIED', 'ERROR'])
final_p_w_wealth (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
final_p_c_wealth (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
final_c_b_wealth (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
final_c_w_wealth (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
max_tension (Annotated[float, Ge(ge=0.0), Le(le=1.0), Ge(ge=0.0), Le(le=1.0)])
crossover_tick (Annotated[int | None, Ge(ge=0)])
cumulative_rent (Annotated[float, Ge(ge=0.0), Ge(ge=0.0)])
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