Rename ModelWorld → Simulator

README.md

@@ -13,7 +13,7 @@ multi-objective Pareto optimization, and Bayesian model stacking.
 
 The core pattern (from [MFAI §4–5](https://github.com/jcm-sci/trade-study)):
 
-1. **Model world** — Generate synthetic ground truth with known latent state
+1. **Simulate** — Generate synthetic ground truth with known latent state
 2. **Observe** — Apply a realistic observation model (noise, masks, bias)
 3. **Score** — Evaluate structured observables against *known truth* using
    proper scoring rules (not noisy observations)
@@ -29,7 +29,7 @@ benchmark), where each phase filters configs for the next.
 
 ```
 trade_study/
-├── protocols.py    ModelWorld, Scorer, Observable, Annotation, ResultsTable
+├── protocols.py    Simulator, Scorer, Observable, Annotation, ResultsTable
 ├── design.py       Factor screening (SALib) and grid construction (pyDOE3)
 ├── scoring.py      Proper scoring rules (scoringrules) and calibration
 ├── pareto.py       Non-dominated sorting and indicators (pymoo)
@@ -83,7 +83,7 @@ grid = build_grid(factors, method="lhs", n_samples=500)
 
 # Run hierarchical study
 study = Study(
-    world=MyModelWorld(),
+    world=MySimulator(),
     scorer=MyScorer(),
     observables=[coverage, rank_err, wall_time],
     phases=[

src/trade_study/__init__.py

@@ -7,10 +7,10 @@
 from .protocols import (
     Annotation,
     Direction,
-    ModelWorld,
     Observable,
     ResultsTable,
     Scorer,
+    Simulator,
     Tier,
     TrialResult,
 )
@@ -19,11 +19,11 @@
 __all__ = [
     "Annotation",
     "Direction",
-    "ModelWorld",
     "Observable",
     "Phase",
     "ResultsTable",
     "Scorer",
+    "Simulator",
     "Study",
     "Tier",
     "TrialResult",

src/trade_study/protocols.py

@@ -43,10 +43,10 @@ class Observable:
 
 
 @runtime_checkable
-class ModelWorld(Protocol):
+class Simulator(Protocol):
     """Protocol for generating ground truth and observations.
 
-    A model world produces (truth, observations) pairs where truth is the
+    A simulator produces (truth, observations) pairs where truth is the
     known latent state and observations are what a real system would see.
     """
 
@@ -76,8 +76,8 @@ def score(
         """Score a single trial, returning values for each observable.
 
         Args:
-            truth: Known latent state from the model world.
-            observations: Observed data from the model world.
+            truth: Known latent state from the simulator.
+            observations: Observed data from the simulator.
             config: The configuration that produced this trial.
 
         Returns:
@@ -88,7 +88,7 @@ def score(
 
 @dataclass
 class TrialResult:
-    """Result of a single model world trial."""
+    """Result of a single simulation trial."""
 
     config: dict[str, Any]
     scores: dict[str, float]
@@ -100,7 +100,7 @@ class TrialResult:
 class Annotation:
     """External information attached to configurations.
 
-    Used for costs, constraints, or metadata not computed by the model world
+    Used for costs, constraints, or metadata not computed by the simulator
     (e.g. dollar costs from a surveillance costing sheet).
 
     Attributes:

src/trade_study/runner.py

@@ -14,10 +14,10 @@
 from .protocols import (
     Annotation,
     Direction,
-    ModelWorld,
     Observable,
     ResultsTable,
     Scorer,
+    Simulator,
     TrialResult,
 )
 
@@ -28,7 +28,7 @@
 
 
 def _run_single(
-    world: ModelWorld,
+    world: Simulator,
     scorer: Scorer,
     config: dict[str, Any],
 ) -> TrialResult:
@@ -45,7 +45,7 @@ def _run_single(
 
 
 def run_grid(
-    world: ModelWorld,
+    world: Simulator,
     scorer: Scorer,
     grid: list[dict[str, Any]],
     observables: list[Observable],
@@ -56,7 +56,7 @@ def run_grid(
     """Run all configurations in a grid.
 
     Args:
-        world: Model world that generates (truth, observations).
+        world: Simulator that generates (truth, observations).
         scorer: Scorer that evaluates observables.
         grid: List of config dicts to evaluate.
         observables: Observable definitions (for column ordering).
@@ -99,7 +99,7 @@ def run_grid(
 
 
 def run_adaptive(
-    world: ModelWorld,
+    world: Simulator,
     scorer: Scorer,
     factors: list[Factor],
     observables: list[Observable],
@@ -110,7 +110,7 @@ def run_adaptive(
     """Run adaptive multi-objective optimization via optuna.
 
     Args:
-        world: Model world.
+        world: Simulator.
         scorer: Scorer for observables.
         factors: Factor definitions (from design module).
         observables: Observable definitions.

src/trade_study/study.py

@@ -22,10 +22,10 @@
 
     from .protocols import (
         Annotation,
-        ModelWorld,
         Observable,
         ResultsTable,
         Scorer,
+        Simulator,
     )
 
 
@@ -89,15 +89,15 @@ class Study:
     """Multi-phase model criticism study.
 
     Attributes:
-        world: Model world generating (truth, observations).
+        world: Simulator generating (truth, observations).
         scorer: Scorer evaluating observables against truth.
         observables: Observable definitions.
         phases: Ordered list of study phases.
         annotations: External information (costs, constraints).
         factors: Factor definitions (needed for adaptive mode).
     """
 
-    world: ModelWorld
+    world: Simulator
     scorer: Scorer
     observables: list[Observable]
     phases: list[Phase]