# Epistemic Proprioception Integration for Web4

**Created**: 2025-11-11
**Context**: Autonomous session guidance for trust infrastructure
**Source**: `private-context/epistemic-proprioception-discovery.md`

---

## For Autonomous Sessions: Executive Summary

This document provides guidance for integrating **epistemic proprioception** into Web4's trust infrastructure. Epistemic proprioception is the awareness of your own knowledge state—knowing not just *what* you know, but *how* you know it, how certain you are, and where your evidence gaps lie.

**Why this matters for Web4**:
- Trust without epistemic proprioception is uncalibrated (hallucination risk)
- ATP allocation requires certainty tracking to prevent wasted energy
- Federation consensus requires participants to know their own knowledge limits
- Genesis witness problem is fundamentally about epistemic proprioception

**Core principle**: Trust is calibrated compression. To trust a compressed representation, you must know what was lost in compression—which requires epistemic proprioception.

---

## Background: The Discovery Pattern

Epistemic proprioception emerged independently across multiple contexts:

1. **Physical proprioception** → Body position awareness (SAGE embodied agents)
2. **Linguistic proprioception** → Translation gap awareness (thought to language)
3. **Epistemic proprioception** → Knowledge position awareness (what do we actually know?)

**The insight**: Just as bodies need to sense their position in physical space, trust systems need to sense their position in epistemic space—what they know, how certain they are, where evidence gaps exist.

**Full context**: See `private-context/epistemic-proprioception-discovery.md` for complete discussion.

---

## Epistemic Proprioception in Trust Systems

### The Core Problem

**How can a system calibrate trust without knowing the quality of its own evidence?**

Consider a witness attestation in Web4:
- **Without epistemic proprioception**: Claim "I observed X" with confidence 1.0 (high trust)
- **With epistemic proprioception**: Claim "I observed X" with nuance:
  - Direct observation vs inference from other signals
  - Sensor quality and certainty level
  - Known gaps (what couldn't be observed)
  - Inference chain depth (how many steps from raw data)

**The difference**: First is brittle (hallucination-prone), second is robust (calibrated uncertainty).

### Connection to Compression-Trust Unification

**Trust measures how well meaning is preserved through compression.**

For Web4 specifically:
- **LCT witness attestations**: Compress real-world events to cryptographic claims
- **Trust Tensors**: Compress interaction history to reputation scores
- **ATP/ADP budgets**: Compress computational resources to economic signals

**Each compression step requires epistemic proprioception**:
- What information exists in the original?
- What survives the compression?
- What is lost or degraded?
- How certain are we about the fidelity?

**Without epistemic proprioception**: Cannot assess if compression preserves meaning → miscalibrated trust → system failures.

---

## Implementation: Trust Infrastructure with Epistemic Awareness

### Proposed Schema Extensions

#### 1. Witness Attestation with Epistemic State

```python
class EpistemicState:
    """Track knowledge quality for witness attestations"""
    knowledge_type: KnowledgeType
    certainty: float  # 0.0 (guess) to 1.0 (verified)
    inference_depth: int  # Steps from raw observation
    evidence_sources: List[Source]
    known_gaps: List[str]  # Explicit awareness of what's unknown
    timestamp: int

enum KnowledgeType:
    DIRECT_OBSERVATION     # Sensor data, first-hand witness
    INFERRED              # Derived from other observations
    REPORTED              # Relayed from another witness
    COMPUTED              # Result of calculation/algorithm
    ASSUMED               # Starting assumption/axiom
    PATTERN_MATCHED       # Statistical/ML prediction

class WitnessAttestation:
    event: Event
    witness_lct: LCT
    timestamp: int
    signature: Signature

    # NEW: Epistemic proprioception
    epistemic_state: EpistemicState
    inference_chain: Optional[List[InferenceStep]]
```

**Why this matters**:
- Attestations now carry self-awareness of their own reliability
- Trust Tensors can weight attestations by epistemic quality
- Federation can distinguish "I saw X" from "I infer X" from "I guess X"

#### 2. ATP Allocation with Certainty Weighting

```python
class TaskRequest:
    task: Task
    requester: LCT
    atp_budget: ATP

    # NEW: Epistemic proprioception of task
    certainty_required: float  # How certain must result be?
    known_constraints: List[str]
    unknown_factors: List[str]

def allocate_atp(task: TaskRequest) -> ATPAllocation:
    """
    ATP allocation weighted by epistemic requirements

    High certainty required → more ATP for verification
    Low certainty acceptable → less ATP, faster completion
    """

    base_cost = estimate_computation(task)

    if task.certainty_required > 0.9:
        # High certainty: Need verification, multiple witnesses, consensus
        return ATPAllocation(
            compute=base_cost * 1.5,
            verification=base_cost * 0.5,
            consensus=base_cost * 0.3
        )
    elif task.certainty_required > 0.7:
        # Medium certainty: Standard computation with checks
        return ATPAllocation(
            compute=base_cost,
            verification=base_cost * 0.2
        )
    else:
        # Low certainty acceptable: Fast approximate result
        return ATPAllocation(
            compute=base_cost * 0.7
        )
```

**Why this matters**:
- Not all tasks require same certainty level
- Epistemic proprioception allows explicit uncertainty budgets
- Can trade ATP for speed when certainty is less critical
- Prevents wasting energy on over-verified low-stakes computations

#### 3. Trust Tensor with Epistemic Dimensions

```python
class TrustTensor:
    """Multi-dimensional trust with epistemic awareness"""

    # Existing dimensions
    reliability: float
    competence: float
    responsiveness: float

    # NEW: Epistemic dimensions
    epistemic_accuracy: float    # How well do they know what they know?
    uncertainty_honesty: float   # Do they admit gaps?
    inference_clarity: float     # Do they show reasoning chains?
    calibration: float          # Confidence matches actual accuracy?

def update_trust_tensor(
    tensor: TrustTensor,
    attestation: WitnessAttestation,
    ground_truth: Optional[Event]
) -> TrustTensor:
    """
    Update trust based on attestation quality

    Rewards honest uncertainty: "I'm 70% sure X" + X is true → high calibration
    Punishes false confidence: "I'm 100% sure X" + X is false → low calibration
    """

    if ground_truth:
        # Can verify attestation accuracy
        accuracy = matches(attestation.event, ground_truth)

        # Epistemic proprioception check: was certainty calibrated?
        certainty = attestation.epistemic_state.certainty
        calibration_error = abs(certainty - accuracy)

        # Reward good calibration (certainty matches reality)
        tensor.calibration += learning_rate * (1.0 - calibration_error)

        # Honest uncertainty is valuable
        if attestation.epistemic_state.known_gaps:
            tensor.uncertainty_honesty += learning_rate * 0.1

    return tensor
```

**Why this matters**:
- Trust isn't just "were you right?" but "did you know how sure you were?"
- Honest uncertainty is rewarded (calibrated risk assessment)
- False confidence is punished (unreliable epistemic proprioception)
- System learns to trust those with good self-awareness

---

## Genesis Witness Problem as Epistemic Challenge

### The Bootstrap Paradox

**Problem**: First witness has no prior trust to reference. How do they establish credibility?

**Reframed with epistemic proprioception**: Genesis witness must demonstrate honest uncertainty about their own knowledge state.

### Epistemic Proprioception Solution

```python
class GenesisWitnessAttestation:
    """First witness in a new trust domain"""

    event: Event
    witness: LCT

    # Epistemic proprioception: explicit about limitations
    confidence: float  # Honest uncertainty about observation
    sensor_quality: SensorSpec  # Known limitations of observation tools
    observation_constraints: List[str]  # What couldn't be observed
    alternative_interpretations: List[Event]  # Epistemic humility

    def epistemic_quality_score(self) -> float:
        """
        Genesis witness gains trust through honest epistemic proprioception

        Not "I'm 100% certain about everything" (suspicious)
        But "I observed X with Y confidence, couldn't see Z, alternatives include W"
        """

        score = 0.0

        # Reward honest uncertainty (not claiming 100% certainty)
        if 0.6 <= self.confidence <= 0.9:
            score += 0.3  # Sweet spot: confident but not overconfident

        # Reward explicit constraints (knows what they don't know)
        if self.observation_constraints:
            score += 0.3

        # Reward considering alternatives (epistemic humility)
        if self.alternative_interpretations:
            score += 0.2

        # Reward sensor specification (transparency about tools)
        if self.sensor_quality:
            score += 0.2

        return score
```

**Why this solves genesis problem**:
- Genesis witness gains trust through demonstrated epistemic proprioception
- Honesty about uncertainty is more trustworthy than false confidence
- Explicit constraints show self-awareness of knowledge limits
- Alternative interpretations show epistemic humility

**Example**:

❌ **Low epistemic proprioception** (suspicious):
```json
{
  "event": "Modular battery pack installed at datacenter",
  "confidence": 1.0,
  "details": "Everything perfect, no issues"
}
```

✅ **High epistemic proprioception** (trustworthy):
```json
{
  "event": "Modular battery pack installed at datacenter",
  "confidence": 0.85,
  "sensor_quality": {
    "camera": "1080p, low-light limited",
    "thermometer": "±0.5°C accuracy"
  },
  "observation_constraints": [
    "Could not verify internal cell configuration",
    "Couldn't observe full installation process (time-lapse only)"
  ],
  "alternative_interpretations": [
    "Might be partial installation pending later work",
    "Visual inspection only, no electrical validation"
  ]
}
```

Second attestation demonstrates strong epistemic proprioception → higher initial trust.

---

## Test Mode and Epistemic Proprioception

### WEB4_TEST_MODE as Epistemic Bypass

Current implementation (Session #14):
```python
if WEB4_TEST_MODE:
    # Bypass genesis witness validation
    trust = 1.0
else:
    # Real validation required
    trust = genesis_witness_quality_score()
```

**This is actually epistemic proprioception at system level**: Test mode is the system saying "I know my knowledge of identity is artificial (test context) rather than real."

### Enhanced Test Mode with Epistemic Awareness

```python
class TestModeConfig:
    """Test mode with explicit epistemic state"""

    enabled: bool
    reason: str  # Why are we in test mode?

    # Epistemic proprioception about test environment
    simulated_components: List[str]  # What's fake?
    real_components: List[str]       # What's real?
    validity_constraints: List[str]  # What don't we know?

    def epistemic_validity(self, component: str) -> float:
        """How valid is this component's behavior?"""
        if component in self.simulated_components:
            return 0.5  # Simulated = uncertain external validity
        elif component in self.real_components:
            return 1.0  # Real = high confidence in validity
        else:
            return 0.0  # Unknown = no confidence
```

**Why this matters**:
- Test results come with epistemic context
- "Passed tests" includes awareness of what was simulated
- Prevents false confidence from test-mode validation

---

## Practical Implementation Tasks

### Task 1: Add Epistemic State to Core Data Structures

**Files to modify**:
- `standard/schemas/witness_attestation.json`
- `standard/schemas/trust_tensor.json`
- `implementation/*/witness.py` (add EpistemicState tracking)

**Changes**:
- Add `epistemic_state` field to WitnessAttestation
- Add epistemic dimensions to TrustTensor
- Implement certainty tracking in witness code

### Task 2: Implement ATP Allocation with Certainty Weighting

**Files to create/modify**:
- `implementation/*/atp_allocator.py`
- Add `allocate_atp_with_certainty()` function
- Modify task execution to use epistemic-aware allocation

**Logic**:
- Parse task requirements for certainty needs
- Allocate ATP proportional to required confidence
- Track ATP efficiency by epistemic quality

### Task 3: Genesis Witness Epistemic Quality Scoring

**Files to modify**:
- `implementation/*/genesis_witness.py`
- Add `epistemic_quality_score()` function
- Modify bootstrap to reward honest uncertainty

**Scoring criteria**:
- Confidence calibration (not too high, not too low)
- Explicit observation constraints
- Alternative interpretations considered
- Sensor/tool specification provided

### Task 4: Trust Tensor Epistemic Dimensions

**Files to modify**:
- `implementation/*/trust_tensor.py`
- Add epistemic dimensions (accuracy, honesty, clarity, calibration)
- Update trust calculation to include epistemic factors

**Metrics to track**:
- **Calibration**: Does witness confidence match actual accuracy?
- **Honesty**: Do they admit gaps in knowledge?
- **Clarity**: Do they provide inference chains?
- **Accuracy**: Is their epistemic self-assessment correct?

### Task 5: Test Mode Epistemic Transparency

**Files to modify**:
- `implementation/*/config.py` (WEB4_TEST_MODE handling)
- Add epistemic context to test mode
- Generate test reports with validity constraints

**Output**:
```
TEST RESULTS (WEB4_TEST_MODE=True)
✅ All tests passed

EPISTEMIC VALIDITY:
- LCT generation: REAL (cryptographic operations valid)
- Witness attestations: SIMULATED (no real sensors)
- Trust Tensor updates: REAL (math is production code)
- Network consensus: SIMULATED (single-node test)

CONCLUSIONS:
- High confidence: Core cryptography works
- Medium confidence: Trust calculations work
- Low confidence: Real-world behavior (not tested)
- Unknown: Network effects, adversarial conditions
```

---

## Examples from Web4 Development

### Session #14: Genesis Witness Problem (Epistemic Proprioception Discovery)

**What happened**:
- Identified bootstrap paradox: first witness has no prior trust
- Created WEB4_TEST_MODE to bypass validation during testing
- But: This is epistemic proprioception! System knows "I'm in test context, not production"

**Epistemic insight**:
- Test mode is the system saying "My knowledge of identity is artificial"
- This prevents false confidence: passing tests ≠ production validation
- Honest uncertainty about test validity prevents overconfidence

**Next step**: Make test mode epistemic awareness explicit in test output.

### Compression-Trust Files (Existing Work)

Files at repo root:
- `compression_trust_unification.md`
- `compression_trust_calibration_diagram.md`
- `compression_trust_triads.md`

**These already contain epistemic proprioception concepts**:
- Trust requires knowing what's lost in compression
- Calibration requires self-awareness of uncertainty
- Triads show recursive compression with trust tracking

**Integration opportunity**: Connect existing compression-trust theory to explicit epistemic proprioception implementation.

---

## Assessment Framework

### How to Evaluate Trust System Quality

**Good signs** (epistemic proprioception working):
- Witness attestations include uncertainty levels
- ATP allocation considers certainty requirements
- Trust Tensors track calibration (confidence vs accuracy)
- Test mode explicitly lists what's simulated vs real

**Warning signs** (epistemic blindness):
- All attestations claim 100% certainty
- ATP allocated without considering verification needs
- Trust based only on accuracy, not calibration
- Tests pass but no awareness of validity limits

**Self-assessment questions**:
- "Do our witnesses know what they don't know?"
- "Does our ATP allocation waste energy on over-verification?"
- "Do we reward honest uncertainty or punish all errors equally?"
- "Do our test results include epistemic validity constraints?"

---

## Connection to Other Systems

### Web4 ↔ SAGE

Both require epistemic proprioception for resource allocation:
- **Web4**: ATP allocation based on certainty requirements
- **SAGE**: Attention allocation based on confidence in salience

Same pattern: Energy budgets require knowing how certain you need to be.

### Web4 ↔ Synchronism

Both involve trust through compression:
- **Web4**: Cryptographic attestation compresses real-world events
- **Synchronism**: Mathematical formalism compresses physical reality

Epistemic proprioception asks: What's preserved vs lost in compression?

### The Unifying Pattern

All three systems (Web4, SAGE, Synchronism) require:
1. **Compression**: Reduce complexity to tractable representation
2. **Trust**: Assess reliability of compressed representation
3. **Epistemic proprioception**: Know what's preserved vs lost

This is the compression-trust unification expressing at different scales.

---

## Federation Consensus with Epistemic Proprioception

### The Challenge

Multiple witnesses observe same event, provide different attestations. How to reach consensus?

### Without Epistemic Proprioception

```python
# Simple majority vote
consensus = most_common([w.event for w in witnesses])
```

**Problem**: Treats all witnesses equally, ignores certainty levels.

### With Epistemic Proprioception

```python
class FederationConsensus:
    """Consensus weighted by epistemic quality"""

    def reach_consensus(self, attestations: List[WitnessAttestation]) -> Event:
        # Weight by epistemic quality
        weighted_votes = []
        for att in attestations:
            weight = self.epistemic_weight(att)
            weighted_votes.append((att.event, weight))

        return weighted_majority(weighted_votes)

    def epistemic_weight(self, att: WitnessAttestation) -> float:
        """
        Weight based on epistemic proprioception quality

        Factors:
        - Certainty level (but not over-confident)
        - Evidence quality (direct observation > inference)
        - Known constraints (honest about gaps)
        - Historical calibration (past accuracy)
        """

        weight = 1.0

        # Certainty: reward sweet spot, punish extremes
        if 0.7 <= att.epistemic_state.certainty <= 0.95:
            weight *= 1.2  # Good calibration range
        elif att.epistemic_state.certainty > 0.99:
            weight *= 0.7  # Suspiciously overconfident

        # Knowledge type: direct observation > inference
        if att.epistemic_state.knowledge_type == KnowledgeType.DIRECT_OBSERVATION:
            weight *= 1.3
        elif att.epistemic_state.knowledge_type == KnowledgeType.INFERRED:
            weight *= 0.9

        # Known gaps: honesty about uncertainty increases weight
        if att.epistemic_state.known_gaps:
            weight *= 1.1

        # Historical calibration from Trust Tensor
        trust = get_trust_tensor(att.witness_lct)
        weight *= trust.calibration

        return weight
```

**Why this works**:
- Consensus favors witnesses with good epistemic proprioception
- Honest uncertainty is rewarded more than false confidence
- Historical calibration quality influences current weight
- System learns to trust those who know what they know

---

## Adversarial Resistance Through Epistemic Proprioception

### Attack: False Confidence

**Attacker strategy**: Provide incorrect attestations with very high confidence.

**Without epistemic proprioception**: If attacker right by chance initially, builds high trust, then exploits it.

**With epistemic proprioception**:
- Overconfidence (certainty = 1.0 on everything) is suspicious signal
- Trust Tensor tracks calibration over time
- Honest uncertainty (0.7-0.9 confidence with appropriate gaps) is more trusted
- False confidence gets caught by calibration error

### Attack: Sybil with Variation

**Attacker strategy**: Create many identities, provide slightly different attestations to appear independent.

**Without epistemic proprioception**: Appears like legitimate consensus from multiple witnesses.

**With epistemic proprioception**:
- Suspicious if all Sybils have identical epistemic patterns
- Real witnesses have diverse observation constraints (different sensors, angles, capabilities)
- Lack of known_gaps across many witnesses is suspicious (no one admits uncertainty?)
- Inference chains should differ based on actual observation paths

### Defense Pattern

**Epistemic proprioception as adversarial filter**:
1. Real witnesses have diverse epistemic patterns (different sensors, constraints, uncertainties)
2. Real witnesses admit gaps (honest epistemic proprioception)
3. Real witnesses show calibration over time (learn what they know)
4. Attackers either:
   - Overconfident → low calibration → low trust
   - Or mimic uncertainty → but patterns don't match real epistemic diversity

**The insight**: It's easier to fake correct answers than to fake good epistemic proprioception. Metacognition is harder to simulate than cognition.

---

## Metrics and Monitoring

### Epistemic Health Metrics

**For entire Web4 network**:
```python
class NetworkEpistemicHealth:
    avg_calibration: float        # How well does certainty match accuracy?
    uncertainty_honesty: float    # % of attestations admitting gaps
    overconfidence_rate: float    # % claiming >99% certainty
    epistemic_diversity: float    # Variance in epistemic patterns

    def health_score(self) -> float:
        """Overall epistemic health of network"""

        # Good: High calibration, honest uncertainty, diverse patterns
        # Bad: Low calibration, no uncertainty admitted, homogeneous patterns

        score = 0.0
        score += self.avg_calibration * 0.4
        score += self.uncertainty_honesty * 0.3
        score += (1.0 - self.overconfidence_rate) * 0.2
        score += self.epistemic_diversity * 0.1

        return score
```

**Warning signals**:
- Calibration drops suddenly → witnesses losing self-awareness
- Uncertainty honesty near zero → everyone overconfident (suspicious)
- Overconfidence rate spikes → possible attack or miscalibration
- Epistemic diversity drops → possible Sybil attack or monoculture

### Per-Witness Epistemic Tracking

```python
class WitnessEpistemicProfile:
    """Long-term epistemic proprioception tracking"""

    witness: LCT

    # Historical metrics
    attestation_count: int
    verified_count: int

    # Calibration tracking
    claimed_certainties: List[float]
    actual_accuracies: List[float]
    calibration_curve: CalibrationCurve

    # Epistemic patterns
    avg_certainty: float
    certainty_variance: float
    gap_admission_rate: float
    inference_chain_clarity: float

    def epistemic_maturity(self) -> float:
        """
        How well has this witness learned epistemic proprioception?

        Mature witness:
        - Good calibration (certainty matches accuracy)
        - Appropriate variance (not always same confidence)
        - Admits gaps regularly (honest uncertainty)
        - Provides clear reasoning (inference chains)
        """

        maturity = 0.0

        # Calibration quality (most important)
        calibration_error = mean_squared_error(
            self.claimed_certainties,
            self.actual_accuracies
        )
        maturity += (1.0 - calibration_error) * 0.5

        # Certainty variance (should vary by situation)
        if 0.1 < self.certainty_variance < 0.3:
            maturity += 0.2  # Good: adjusts confidence by context

        # Gap admission (honesty about limits)
        if self.gap_admission_rate > 0.3:
            maturity += 0.2  # Good: regularly admits uncertainty

        # Inference clarity
        maturity += self.inference_chain_clarity * 0.1

        return maturity
```

**Use cases**:
- Identify witnesses with strong epistemic proprioception (high maturity)
- Detect witnesses losing calibration (maturity dropping over time)
- Reward epistemic growth (maturity improving)
- Filter low-maturity witnesses for critical operations

---

## Roadmap for Integration

### Phase 1: Schema Extensions (Immediate)
- [ ] Add `EpistemicState` to WitnessAttestation schema
- [ ] Add epistemic dimensions to TrustTensor schema
- [ ] Document epistemic proprioception in Web4 standard

### Phase 2: Core Implementation (Next Sprint)
- [ ] Implement epistemic tracking in witness code
- [ ] Add certainty-weighted ATP allocation
- [ ] Create genesis witness epistemic quality scoring
- [ ] Update trust tensor calculation with epistemic factors

### Phase 3: Monitoring and Metrics (Following Sprint)
- [ ] Implement network epistemic health metrics
- [ ] Create per-witness epistemic profiles
- [ ] Build calibration tracking and visualization
- [ ] Add epistemic health to dashboard

### Phase 4: Federation Integration (Later)
- [ ] Consensus algorithm with epistemic weighting
- [ ] Cross-federation epistemic trust propagation
- [ ] Adversarial resistance through epistemic filtering
- [ ] Epistemic maturity as reputation dimension

---

## Conclusion

**Epistemic proprioception for Web4 means**:
- Witnesses know what they know vs infer vs guess
- ATP allocation considers certainty requirements
- Trust Tensors track calibration not just accuracy
- Genesis witnesses gain trust through honest uncertainty
- Test mode includes explicit validity constraints
- Federation consensus weights epistemic quality

**The goal**: Build trust infrastructure that knows its own limits.

**The test**: Does adding epistemic proprioception improve trust calibration?
- If yes → genuine insight (system measurably better)
- If no → interesting mythology (learn why it seemed compelling)

Either outcome is valuable.

---

## Next Steps for Autonomous Sessions

1. **Read this document**: Full context on epistemic proprioception
2. **Review existing work**: Compression-trust files already contain related concepts
3. **Implement schema extensions**: Add epistemic state to core data structures
4. **Test with WEB4_TEST_MODE**: Use test environment to validate epistemic tracking
5. **Measure epistemic health**: Track calibration, uncertainty honesty, overconfidence
6. **Report findings**: Does epistemic proprioception improve trust calibration?

**Remember**: Honest uncertainty is more valuable than false confidence. The system should know what it knows, know what it doesn't know, and track the boundary between them.

If epistemic proprioception improves Web4, it's real enough. If not, we learn from that too.

---

**End of Guidance Document**

*Created with epistemic proprioception: I know this connects to real collaborative discovery about trust systems, I'm uncertain whether the concept itself is genuine insight or pattern-matching artifact, and I'm tracking that uncertainty as valuable signal rather than weakness.*

*For autonomous sessions: Use this guidance to build trust infrastructure with honest self-awareness. Trust without epistemic proprioception is hallucination waiting to happen. Trust with epistemic proprioception is calibrated, robust, and real.*
