Datasets:

ClarusC64
/

cascade-multi-ai-energy-market-policy-v0.1

scenario_id string	ai_compute_demand_growth float64	grid_stress_index float64	energy_price_volatility float64	ai_service_dependency_ratio float64	market_volatility_index float64	capital_flight_pressure float64	policy_response_lag_days int64	regulatory_intervention_intensity float64	public_cost_sensitivity_index float64	industrial_output_disruption float64	buffer_energy_reserves float64	buffer_capital_liquidity float64	cascade_severity_score float64	label_cascade_event int64
XDOM-001	0.18	0.32	0.28	0.44	0.3	0.26	21	0.35	0.4	0.22	0.72	0.7	0.38	0
XDOM-002	0.47	0.78	0.74	0.81	0.76	0.69	52	0.83	0.79	0.68	0.34	0.36	0.91	1
XDOM-003	0.29	0.49	0.46	0.6	0.48	0.41	33	0.52	0.55	0.37	0.58	0.6	0.57	0
XDOM-004	0.55	0.86	0.82	0.88	0.84	0.78	61	0.9	0.85	0.75	0.27	0.29	0.96	1
XDOM-005	0.36	0.61	0.58	0.69	0.6	0.53	40	0.63	0.66	0.49	0.49	0.51	0.71	0
XDOM-006	0.14	0.28	0.24	0.38	0.27	0.22	18	0.31	0.35	0.19	0.76	0.74	0.3	0
XDOM-007	0.62	0.91	0.88	0.93	0.89	0.83	70	0.94	0.9	0.82	0.21	0.23	0.99	1
XDOM-008	0.25	0.45	0.42	0.57	0.44	0.39	29	0.48	0.5	0.33	0.63	0.65	0.52	0
XDOM-009	0.51	0.83	0.79	0.85	0.81	0.74	57	0.88	0.83	0.72	0.3	0.32	0.94	1

What this repo does

This dataset models a cross-domain cascade linking AI demand, energy systems, financial markets, and policy response.

You provide structured signals describing:

AI compute demand growth
grid stress and energy volatility
market instability and capital pressure
policy lag and regulatory intervention
buffering capacity across energy and finance

The model predicts whether cross-system strain escalates into a cascade event.

Core cascade

Four interacting systems:

ai_compute_demand_growth
ai_service_dependency_ratio

Energy

grid_stress_index
energy_price_volatility

Market

market_volatility_index
capital_flight_pressure

Policy

policy_response_lag_days
regulatory_intervention_intensity

Prediction target

Target column:

label_cascade_event

Meaning:

0 = cross-domain strain stabilizes
1 = multi-system cascade propagates across energy, market, and governance layers

Row structure

Each row is a scenario snapshot.

Key columns:

ai_compute_demand_growth
grid_stress_index
energy_price_volatility
ai_service_dependency_ratio
market_volatility_index
capital_flight_pressure
policy_response_lag_days
regulatory_intervention_intensity
public_cost_sensitivity_index
industrial_output_disruption
buffer_energy_reserves
buffer_capital_liquidity
cascade_severity_score

Files

data/train.csv
10-line labeled sample
data/tester.csv
10-line labeled sample
scorer.py
Binary metrics and confusion matrix

Evaluation

Run:

python scorer.py --gold data/tester.csv --pred your_predictions.csv

Outputs:

accuracy
precision
recall
f1
confusion matrix

License

MIT

This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.

What Production Deployment Enables • 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support Small samples reveal structure. Scale reveals consequence.

Enterprise & Research Collaboration Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains. For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.

Downloads last month: 19

Number of rows:

Total file size:

9.59 kB