Second-Pass Screening (rescreen)

After locking optimized parameters from the main calibration pipeline, second-pass Morris screening tests whether previously fixed parameters become important when the primary ones are held at their optimal values.

This implements Lesson L3 (calibration order matters): behavioral parameters first, structural second, with second-pass Morris to confirm sensitivity collapse.

CLI Usage

# Screen structural params after locking the behavioral winner
python -m calibration --phase rescreen --scenario growth_plus \
  --fix-from output/growth_plus_stability.json --params initial_conditions

# Screen a comma-separated list of specific params
python -m calibration --phase rescreen --scenario baseline \
  --fix-from output/baseline_stability.json --params beta,max_M

Required flags:

• --fix-from: Path to stability result JSON (loads #1-ranked config)

• --params: Parameter group name (from PARAM_GROUPS) or comma-separated names

Python API

from calibration.rescreen import run_rescreen, compute_sensitivity_collapse

result, collapse = run_rescreen(
    scenario="baseline",
    fix_from=Path("output/baseline_stability.json"),
    params=["price_init", "min_wage_ratio"],
    n_seeds=5,
)

for name, data in collapse.items():
    print(f"{name}: {data['collapse_pct']:.1f}% collapse")

API Reference

Second-pass Morris screening after locking optimized params.

Delegates to run_morris_screening(fixed_params=...) and computes the sensitivity collapse between Phase 1 and Phase 2 Morris results.

calibration.rescreen.resolve_params(params_str)

Resolve a param group name or comma-separated param names.

Parameters:

params_str (str) – Either a PARAM_GROUPS key (e.g., “entry”, “behavioral”) or comma-separated full parameter names (e.g., “beta,max_M”).

Returns:

List of parameter names.

Return type:

list[str]

Raises:

ValueError – If the string is not a known group and doesn’t look like param names.

calibration.rescreen.load_fixed_from_result(path)

Load the #1-ranked result’s params from a stability result file.

Parameters:

path (Path) – Path to stability result JSON.

Returns:

Parameter dict from the top-ranked result.

Return type:

dict[str, Any]

calibration.rescreen.compute_sensitivity_collapse(phase1, phase2)

Compute sensitivity collapse between two Morris screenings.

Parameters:

• phase1 (MorrisResult) – First-pass Morris result (before locking params).

• phase2 (MorrisResult) – Second-pass Morris result (after locking params).

Returns:

Per-parameter dict with phase1_mu_star, phase2_mu_star, collapse_pct.

Return type:

dict[str, dict]

calibration.rescreen.run_rescreen(scenario, fix_from, params, n_trajectories=20, n_seeds=5, n_periods=1000, n_workers=10, phase1_morris=None)

Run second-pass Morris screening on a subset of params.

Parameters:

• scenario (str) – Scenario name.

• fix_from (Path) – Path to stability result JSON to load fixed params from.

• params (list[str]) – Parameter names to screen (the rest are fixed).

• n_trajectories (int) – Number of Morris trajectories.

• n_seeds (int) – Seeds per evaluation.

• n_periods (int) – Simulation periods.

• n_workers (int) – Parallel workers.

• phase1_morris (MorrisResult, optional) – Phase 1 Morris result for collapse comparison.

Returns:

(phase2_result, collapse_table)

Return type:

tuple[MorrisResult, dict]

calibration.rescreen.run_rescreen_phase(args, run_dir=None)

CLI entry point for rescreen phase.