Ternary Consciousness Reference

API reference for the ternary consciousness model (Feature 034). For conceptual background, see Ternary Consciousness Model.

TernaryConsciousness Model 

class TernaryConsciousness

A point in the 2-simplex representing community consciousness. Three components (r, l, f) sum to 1.0.

Defined in babylon.models.entities.consciousness.

Field	Type	Default	Description
`r`	`Probability`	0.3	Revolutionary consciousness [0, 1]
`l`	`Probability`	0.6	Liberal consciousness [0, 1]
`f`	`Probability`	0.1	Fascist consciousness [0, 1]
`contestation_stored`	`float \| None`	`None`	Legacy contestation value (None = use Shannon entropy)

Simplex constraint: r + l + f = 1.0 (tolerance: 1e-4). Violations raise ValueError.

Computed properties:

Property	Type	Description
`collective_identity`	`float`	Equals `r`. Backward-compatible with old scalar field.
`dominant_tendency`	`ConsciousnessTendency`	Argmax of `(r, l, f)`. Ties broken: liberal > revolutionary > fascist.
`ideological_contestation`	`float`	If `contestation_stored` is set, returns that value (legacy path). Otherwise, normalized Shannon entropy: `H(r, l, f) / log(3)` in [0, 1].
`assimilation_ratio`	`float`	`f / (l + f)`. Position along the liberal-fascist base. Returns 0.5 when `l + f < 1e-6` (fully revolutionary).

Construction paths:

Native: TernaryConsciousness(r=0.5, l=0.3, f=0.2) — direct simplex coordinates. Any leftover computed field names are stripped.
Legacy: TernaryConsciousness(collective_identity=0.5, dominant_tendency=LIBERAL, ideological_contestation=0.4) — converts r = collective_identity, splits remaining (1 - r) between l and f based on dominant_tendency, stores ideological_contestation in contestation_stored.
Default: TernaryConsciousness() — sets contestation_stored=0.2 and uses field defaults (r=0.3, l=0.6, f=0.1).

CommunityConsciousness Alias 

CommunityConsciousness in babylon.models.entities.community is a type alias:

CommunityConsciousness = TernaryConsciousness

All code using CommunityConsciousness transparently uses the ternary model.

SubstrateFloor 

class SubstrateFloor

Per-community-type minimum revolutionary consciousness with provenance metadata.

Defined in babylon.models.entities.consciousness.

Field	Type	Description
`community_type`	`CommunityType`	Which community this floor applies to
`floor_value`	`Probability`	Minimum `r` regardless of org landscape [0, 1]. Default: 0.0.
`confidence`	`ProvenanceLevel`	Data quality indicator. Default: `SYNTHETIC`.
`data_sources`	`list[str]`	Named data sources used for derivation
`computation_method`	`str`	How the floor was derived from proxies

ProvenanceLevel 

class ProvenanceLevel

Data quality indicator for substrate floor computation. StrEnum values:

Value	Meaning
`HIGH`	Derived from 2+ independent proxy data sources
`MEDIUM`	Derived from 1 proxy data source
`LOW`	Estimated from related data, not direct proxy
`SYNTHETIC`	Stipulated placeholder with no data path

SUBSTRATE_FLOOR_DEFAULTS 

Default substrate floors for all 14 community types. Defined in babylon.models.entities.consciousness.

Community	Floor	Confidence	Data Sources
`NEW_AFRIKAN`	0.12	MEDIUM	Vera incarceration rates, Chetty mobility atlas
`FIRST_NATIONS`	0.12	MEDIUM	Vera incarceration rates, Chetty mobility atlas
`INCARCERATED`	0.18	MEDIUM	Vera incarceration rates
`CHICANO`	0.08	LOW	Chetty mobility atlas
`WOMEN`	0.04	LOW	Estimated
`TRANS`	0.06	LOW	Estimated
`DISABLED`	0.03	LOW	Estimated
`QUEER`	0.04	LOW	Estimated
`UNDOCUMENTED`	0.10	LOW	Estimated
`SETTLER`	0.0	HIGH	Structural (hegemonic default)
`PATRIARCHAL`	0.0	HIGH	Structural (hegemonic default)
`YOUTH`	0.0	HIGH	Structural (lifecycle phase)
`ADULT`	0.0	HIGH	Structural (lifecycle phase)
`ELDER`	0.02	LOW	Estimated (generational memory)

OrgContribution 

class OrgContribution

An organization’s weighted contribution to community consciousness. Input to compute_ternary_consciousness().

Defined in babylon.models.entities.consciousness.

Field	Type	Description
`tendency`	`ConsciousnessTendency`	Which simplex vertex this org pulls toward
`membership_density`	`Probability`	Members in community / community population [0, 1]
`cadre_level`	`Probability`	Organizational development level [0, 1]
`cohesion`	`Probability`	Internal organizational cohesion [0, 1]

compute_ternary_consciousness 

compute_ternary_consciousness(community_type, org_landscape, substrate_floor=0.0)

Compute ternary consciousness from organizational landscape.

Defined in babylon.formulas.consciousness.

Parameters:

community_type (CommunityType) – Which community this is for (used for logging)
org_landscape (list[OrgContribution]) – Organizations operating in the community
substrate_floor (float) – Minimum r regardless of org landscape [0, 1]

Returns:

TernaryConsciousness with contestation_stored = None (uses Shannon entropy for ideological_contestation)

Return type:

TernaryConsciousness

Algorithm:

Sum weighted contributions per tendency. Weight w_i = membership_density * cadre_level * cohesion.
Unorganized fraction = max(0, 1 - sum(membership_densities)). Defaults to liberal (Jackson: passive acceptance is liberal hegemony).
Normalize to simplex (r + l + f = 1.0).
Apply substrate floor post-normalization: if r < floor, set r = floor and redistribute (1 - floor) to l and f proportionally.

Example:

from babylon.formulas.consciousness import compute_ternary_consciousness
from babylon.models.entities.consciousness import OrgContribution
from babylon.models.enums import CommunityType, ConsciousnessTendency

result = compute_ternary_consciousness(
    community_type=CommunityType.NEW_AFRIKAN,
    org_landscape=[
        OrgContribution(
            tendency=ConsciousnessTendency.REVOLUTIONARY,
            membership_density=0.3,
            cadre_level=0.8,
            cohesion=0.9,
        ),
    ],
    substrate_floor=0.12,
)
print(f"r={result.r}, l={result.l}, f={result.f}")

anisotropic_observation_error 

anisotropic_observation_error(true_consciousness, *, rng_seed=None, r_noise_stddev=0.06, lf_noise_stddev=0.02)

Apply anisotropic noise modeling state surveillance asymmetry.

Defined in babylon.bifurcation.consciousness.

Parameters:

true_consciousness (TernaryConsciousness) – Actual community consciousness position
rng_seed (int | None) – Seed for reproducible noise (None = system entropy)
r_noise_stddev (float) – Gaussian noise std for r component (default: 0.06)
lf_noise_stddev (float) – Gaussian noise std for l/f ratio (default: 0.02)

Returns:

Observed TernaryConsciousness with anisotropic noise applied, clamped to valid simplex point

Return type:

TernaryConsciousness

Noise model:

Perturb r with Gaussian noise (stddev 0.06). Clamp to [0, 1].
Compute observed l/f ratio from perturbed f / (l + f) with Gaussian noise (stddev 0.02). Clamp to [0, 1].
Reconstruct l and f from remaining budget (1 - observed_r) and perturbed ratio.

The r component has ~3x higher observation error than the l/f split, reflecting that revolutionary consciousness is hidden from state surveillance while liberal/fascist expression is legible.

WeightedSolidarityResult 

class WeightedSolidarityResult

Result of consciousness-weighted solidarity computation.

Defined in babylon.bifurcation.types.

Field	Type	Description
`weight`	`float`	Consciousness-weighted solidarity value (>= 0)
`crisis_fragile`	`bool`	`True` if effective CI < crisis-fragile threshold (0.3). Default: `False`.

consciousness_weighted_solidarity 

consciousness_weighted_solidarity(source_id, target_id, graph, H, community_states, defines)

Weight a solidarity edge by consciousness of connected communities.

Defined in babylon.bifurcation.consciousness.

Parameters:

source_id (str) – Source agent node ID
target_id (str) – Target agent node ID
graph (nx.DiGraph) – The simulation DiGraph (for edge attribute access)
H (xgi.Hypergraph) – XGI hypergraph (for community membership lookup)
community_states (dict[CommunityType, CommunityState]) – Current community consciousness data
defines (BifurcationDefines) – Configurable parameters (sigmoid midpoint/steepness)

Returns:

Weighted solidarity with crisis-fragile flag

Return type:

WeightedSolidarityResult

Edges where the effective CI (min of both endpoints) falls below the crisis-fragile threshold (0.3) are marked crisis_fragile = True.

CONSCIOUSNESS_DEFAULTS 

Starting values for all 14 community types. Synthetic data for a Detroit 2010 test case. Defined in babylon.models.entities.community.

All values are native ternary (r, l, f) coordinates. The ideological_contestation is computed as Shannon entropy of the distribution.

Community	r	l	f	Dominant Tendency
`SETTLER`	0.40	0.45	0.15	LIBERAL
`PATRIARCHAL`	0.30	0.525	0.175	LIBERAL
`NEW_AFRIKAN`	0.50	0.50	0.0	LIBERAL
`FIRST_NATIONS`	0.60	0.24	0.16	REVOLUTIONARY
`CHICANO`	0.40	0.45	0.15	LIBERAL
`WOMEN`	0.30	0.525	0.175	LIBERAL
`TRANS`	0.50	0.50	0.0	LIBERAL
`DISABLED`	0.30	0.525	0.175	LIBERAL
`QUEER`	0.40	0.45	0.15	LIBERAL
`UNDOCUMENTED`	0.50	0.50	0.0	LIBERAL
`INCARCERATED`	0.60	0.24	0.16	REVOLUTIONARY
`YOUTH`	0.20	0.60	0.20	LIBERAL
`ADULT`	0.10	0.675	0.225	LIBERAL
`ELDER`	0.30	0.525	0.175	LIBERAL

Notable patterns:

FIRST_NATIONS and INCARCERATED have the highest r (0.6) and are the only communities with REVOLUTIONARY dominant tendency.
NEW_AFRIKAN, TRANS, and UNDOCUMENTED have f = 0.0 — no fascist component, split evenly between revolutionary and liberal.
ADULT has the lowest r (0.1) — fully integrated into the labor market, atomized by liberal hegemony.

Module Cross-References 

Module	Contains
`babylon.models.entities.consciousness`	`TernaryConsciousness`, `SubstrateFloor`, `OrgContribution`, `ProvenanceLevel`, `SUBSTRATE_FLOOR_DEFAULTS`
`babylon.models.entities.community`	`CommunityConsciousness` (alias), `CONSCIOUSNESS_DEFAULTS`
`babylon.formulas.consciousness`	`compute_ternary_consciousness()`
`babylon.bifurcation.consciousness`	`anisotropic_observation_error()`, `consciousness_weighted_solidarity()`
`babylon.bifurcation.types`	`WeightedSolidarityResult`, `BifurcationResult` (updated with assimilation fields)