EIS_ESL_PNC_CEC_INFMOD.txt

#!/usr/bin/env python3
"""
Intent Inference Module (INFMOD) for EIS/ESL/PNC/CEC v6
========================================================
This module performs *hypothesis-level* intent inference
based on structural signals from the ESLedger.

It does NOT:
- assert intent
- assert agency
- assert truth

It DOES:
- generate competing hypotheses
- attach explicit evidence
- propagate uncertainty
- maintain epistemic separation
"""

from dataclasses import dataclass
from typing import List, Dict, Optional
from datetime import datetime

# ------------------------------------------------------------
# INTENT HYPOTHESIS DATA MODEL
# ------------------------------------------------------------

@dataclass
class IntentHypothesis:
    agent: str                     # e.g., "institutional", "network", "emergent", "unknown"
    incentive: str                 # e.g., "reputation_protection", "narrative_control"
    causal_graph: Dict             # minimal DAG of event relationships
    probability: float             # 0–1 weight, not a verdict
    uncertainty_factors: List[str] # explicit epistemic humility
    evidence: List[str]            # concrete observations supporting the hypothesis


# ------------------------------------------------------------
# INTENT INFERENCE ENGINE
# ------------------------------------------------------------

class IntentInferenceEngine:
    def __init__(self, structural_layer):
        """
        structural_layer: an ESLedger instance or compatible interface
        """
        self.structural = structural_layer

    # --------------------------------------------------------
    # MAIN ENTRY POINT
    # --------------------------------------------------------
    def infer(self, target: str) -> List[IntentHypothesis]:
        """
        Generate competing intent hypotheses for a given entity or term.
        Returns a list of IntentHypothesis objects.
        """

        metrics = self._gather_structural_metrics(target)
        incentive_models = self._build_incentive_models(metrics)
        hypotheses = self._generate_hypotheses(metrics, incentive_models)

        return hypotheses

    # --------------------------------------------------------
    # STEP 1 — STRUCTURAL METRIC EXTRACTION
    # --------------------------------------------------------
    def _gather_structural_metrics(self, target: str) -> Dict:
        """
        Pulls structural signals from the ESLedger.
        These are *signals*, not interpretations.
        """

        metrics = {
            "suppression_score": self.structural.get_entity_suppression(target),
            "coordination_likelihood": self._avg_claim_field(target, "coordination_likelihood"),
            "negation_density": self._negation_density(target),
            "temporal_pattern": self._temporal_pattern(target),
            "entity_presence": self._entity_presence(target),
        }

        return metrics

    def _avg_claim_field(self, target: str, field: str) -> float:
        vals = []
        for cid, claim in self.structural.claims.items():
            if target.lower() in claim["text"].lower():
                vals.append(claim.get(field, 0.0))
        return sum(vals) / len(vals) if vals else 0.0

    def _negation_density(self, target: str) -> float:
        neg = 0
        total = 0
        for cid, claim in self.structural.claims.items():
            if target.lower() in claim["text"].lower():
                total += 1
                if any(n in claim["text"].lower() for n in ["not", "never", "no "]):
                    neg += 1
        return neg / total if total else 0.0

    def _temporal_pattern(self, target: str) -> Dict:
        timestamps = []
        for cid, claim in self.structural.claims.items():
            if target.lower() in claim["text"].lower():
                try:
                    timestamps.append(datetime.fromisoformat(claim["timestamp"].replace("Z", "+00:00")))
                except:
                    pass
        timestamps.sort()
        return {"count": len(timestamps), "first": timestamps[0] if timestamps else None}

    def _entity_presence(self, target: str) -> int:
        return sum(1 for cid, claim in self.structural.claims.items() if target.lower() in claim["text"].lower())

    # --------------------------------------------------------
    # STEP 2 — INCENTIVE MODELING
    # --------------------------------------------------------
    def _build_incentive_models(self, metrics: Dict) -> List[Dict]:
        """
        Creates abstract incentive models based on structural signals.
        These are NOT intent claims — they are interpretive scaffolds.
        """

        models = []

        # Institutional incentive model
        if metrics["suppression_score"] > 0.4 or metrics["coordination_likelihood"] > 0.5:
            models.append({
                "agent": "institutional",
                "incentive": "narrative_control",
                "weight": 0.4 + metrics["coordination_likelihood"] * 0.3,
                "uncertainties": ["no direct evidence of agency"],
            })

        # Network incentive model
        if metrics["coordination_likelihood"] > 0.3:
            models.append({
                "agent": "network",
                "incentive": "signal_amplification",
                "weight": 0.3 + metrics["coordination_likelihood"] * 0.2,
                "uncertainties": ["coordination may be emergent"],
            })

        # Emergent systemic model
        models.append({
            "agent": "emergent",
            "incentive": "incentive_alignment",
            "weight": 0.2 + metrics["negation_density"] * 0.2,
            "uncertainties": ["emergent patterns mimic intent"],
        })

        # Unknown agent model
        models.append({
            "agent": "unknown",
            "incentive": "unclear",
            "weight": 0.1,
            "uncertainties": ["insufficient structural signal"],
        })

        return models

    # --------------------------------------------------------
    # STEP 3 — HYPOTHESIS GENERATION
    # --------------------------------------------------------
    def _generate_hypotheses(self, metrics: Dict, models: List[Dict]) -> List[IntentHypothesis]:
        hypotheses = []

        for model in models:
            evidence = self._collect_evidence(metrics, model)

            hypotheses.append(
                IntentHypothesis(
                    agent=model["agent"],
                    incentive=model["incentive"],
                    causal_graph=self._build_causal_graph(metrics),
                    probability=min(1.0, model["weight"]),
                    uncertainty_factors=model["uncertainties"],
                    evidence=evidence
                )
            )

        return hypotheses

    def _collect_evidence(self, metrics: Dict, model: Dict) -> List[str]:
        evidence = []

        if metrics["suppression_score"] > 0.4:
            evidence.append(f"High suppression_score: {metrics['suppression_score']:.2f}")

        if metrics["coordination_likelihood"] > 0.3:
            evidence.append(f"Elevated coordination_likelihood: {metrics['coordination_likelihood']:.2f}")

        if metrics["negation_density"] > 0.2:
            evidence.append(f"Negation density suggests contested narrative: {metrics['negation_density']:.2f}")

        if metrics["entity_presence"] > 10:
            evidence.append(f"High entity presence: {metrics['entity_presence']} mentions")

        return evidence or ["No strong evidence — hypothesis weak"]

    def _build_causal_graph(self, metrics: Dict) -> Dict:
        """
        Minimal DAG: structural signals → incentive model
        """
        return {
            "suppression_score": metrics["suppression_score"],
            "coordination_likelihood": metrics["coordination_likelihood"],
            "negation_density": metrics["negation_density"],
            "leads_to": "incentive_hypothesis"
        }