# ParaEval
A workbench to adjudicate parametric trigger decisions against multi-source evidence. It makes the decision path explicit and expandable so reviewers can trace Situation -> Task -> Action -> Result for each case.
Overview
ParaEval is an evaluation workbench for parametric insurance trigger decisions. It normalizes heterogeneous evidence sources, applies deterministic decision algorithms, and surfaces disagreement as first-class basis risk. Heterogeneous data sources (gauges, satellite imagery, weather APIs) are normalized into a common schema for comparison and adjudication. A deterministic decision algorithm evaluates trigger conditions against normalized evidence, with Zod schemas mirroring future Pydantic models for a clean TS-to-Python bridge. Near-threshold disagreements across sources are surfaced as first-class basis risk, highlighting why sources diverge and how that affects confidence. The engine is regression-tested against golden cases with 31 unit tests covering algorithm branches.
Problem
Policies trigger on index values (gauges, satellite, APIs) rather than on verified loss. Near-threshold disagreements across sources create ambiguity and hidden basis risk without a shared reasoning frame.
Approach
Normalize heterogeneous sources, apply a deterministic decision algorithm, surface disagreement as first-class basis risk, and regression-test the engine against golden cases. The contract layer (Zod) mirrors future Pydantic models for a clean TS<->Python bridge.
Impact
- * Transparent rule-trace and narrative output suitable for review memos and audits.
- * Explicit basis-risk classification highlights why sources diverge and how that affects confidence.
- * 31 unit tests cover algorithm branches to prevent silent regressions as cases and rules evolve.
Architecture
ParaEval is an evaluation workbench for parametric insurance trigger decisions. It normalizes heterogeneous evidence sources, applies deterministic decision algorithms, and surfaces disagreement as first-class basis risk.