DetectionDelayLossScenario

Statistics scenarios · Implementation: mlsysim/engine/scenario_definitions/statistics.py.

Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.

A fault that cuts quality_drop of annual_value until it is detected, found after slow_detection or fast_detection, incidents_per_year times a year.

Notes

Equation: Loss is annual value times quality drop times the fraction of a year spent undetected; incident frequency scales savings.

Assumptions: Value accrues uniformly and each incident has the same constant quality loss.

Excludes: Compounding, overlapping incidents, recovery costs, and uncertainty in the declared annual value.

References

Derivation: the scenario composes mlsysim.physics.economics.calc_detection_delay_loss. Their contracts supply the underlying equations; property equations show the remaining unit conversions and accounting.

Examples

>>> from mlsysim import Scenarios
>>> scenario = Scenarios.RecommenderDetectionDelayLoss
>>> value = scenario.slow_loss
>>> str(value.units)
'USD'

Attributes

Name	Description
annual_savings	Expected USD saved in a year at the supplied incident frequency.
annual_value
fast_detection
fast_loss	Per-incident USD loss under the faster detection duration.
full_year_loss	USD lost if the quality drop persisted for an entire year.
incidents_per_year
quality_drop
savings_per_incident	Slow minus fast loss, in USD per incident.
slow_detection
slow_loss	Per-incident USD loss under the slower detection duration.
slow_over_fast	Ratio of slow-detection to nonzero fast-detection loss, dimensionless.

Methods

Name	Description
loss	USD lost while the fault persists for `delay` under a constant value-loss rate.

loss

engine.scenario_types.DetectionDelayLossScenario.loss(delay)

USD lost while the fault persists for delay under a constant value-loss rate.

Equation: calc_detection_delay_loss(annual_value, quality_drop, delay).to('USD').