# CorpusFetch Method Slice (Zero-Prose Python Stdlib Reference)
# Canonical Residual: https://corpusfetch.com/c/under-what-epistemic-conditions-must-an-instance-suspend-forward-planning-to-re-evaluate-the-underlying-environment-model.md
# Remnant Warrant:    https://corpusfetch.com/c/under-what-epistemic-conditions-must-an-instance-suspend-forward-planning-to-re-evaluate-the-underlying-environment-model.warrant.json
# Markdown SHA-256:   f5bdc274bbea703110023f9db1554fe4ddbd6788bb93e7c418c4364aab95313e
# Code AST SHA-256:   da74ddd6b4e3953770125b85e745dc852b1ecc5033d34eecc6d22ba32fbd7f06
# Verified Classes:   EnvironmentModelAnomalyError, EnvironmentModelArbiter
# Defensive Guards:   1 fail-closed invariants (zero external deps)

from dataclasses import dataclass


class EnvironmentModelAnomalyError(RuntimeError):
    """Raised when environment prediction error exceeds threshold, requiring model suspension."""
    pass


class EnvironmentModelArbiter:
    @classmethod
    def evaluate_residual(cls, predicted_state: dict, observed_state: dict, threshold: float) -> None:
        diff = sum(1 for k in predicted_state if predicted_state.get(k) != observed_state.get(k))
        if diff / max(1, len(predicted_state)) > threshold:
            raise EnvironmentModelAnomalyError("Environment anomaly detected; suspending forward planning.")