ExposureConcentrationScenario
Robotics scenarios · Implementation: mlsysim/engine/scenario_definitions/robotics/supervision.py.
Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.
encounter_share of a site’s machine-person encounters fall on user_share of its people: their per-person exposure against the site average, and everyone else’s.
Notes
Equation: Selected-group exposure is encounter_share/user_share; other-group exposure uses the complementary shares.
Assumptions: Population and encounter shares describe the same exposure period, with user_share between zero and one.
Excludes: Severity differences among encounters and causal interpretation of group membership.
References
Derivation: Selected-group exposure is encounter_share/user_share; other-group exposure uses the complementary shares. This is declared scenario accounting rather than a fitted empirical law.
Examples
>>> from mlsysim import Scenarios
>>> scenario = Scenarios.WarehouseCrossingExposure
>>> value = scenario.concentration
>>> round(float(value), 3)
8.0Attributes
| Name | Description |
|---|---|
| concentration | Encounter exposure per member of the selected group relative to the population mean. |
| encounter_share | |
| rest_ratio | Encounter exposure per person outside the group relative to the population mean. |
| user_share |
concentration
engine.scenario_types.ExposureConcentrationScenario.concentration
Encounter exposure per member of the selected group relative to the population mean.
Equation: encounter_share / user_share.
rest_ratio
engine.scenario_types.ExposureConcentrationScenario.rest_ratio
Encounter exposure per person outside the group relative to the population mean.
Equation: (1 - encounter_share) / (1 - user_share).