Moral Asymmetry Event Sourcing

First Conceptualized: June 12, 2025

Draft Version: 1.0

Author: Forrest Hosten

Status: Invention Documentation

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Abstract

Traditional belief update mechanisms treat positive and negative evidence symmetrically: a success increases belief strength by +α, a failure decreases it by -α. This symmetry is psychologically unrealistic and operationally dangerous. Humans exhibit moral asymmetry—negative events (errors, violations, harms) carry more weight than positive events (successes, confirmations). One catastrophic failure can destroy trust that took months to build.

We introduce event-sourced belief updates with configurable moral asymmetry, where negative evidence receives amplified weight relative to positive evidence. The asymmetry is controlled by a parameter β ≥ 1.0: when β = 1.0, updates are symmetric; when β = 2.0, failures have twice the impact of successes; when β = 3.0, failures have three times the impact.

The critical architectural insight is that asymmetry must be implemented through event sourcing, not through in-place updates. Each outcome (success or failure) is stored as an immutable event with full context. Belief strength is then computed as a function over the event history, applying asymmetric weights during aggregation. This enables temporal analysis (when did errors cluster?), counterfactual reasoning (what would belief strength be without event X?), and audit reconstruction (replay the learning history with different asymmetry parameters).

We demonstrate this architecture on a financial workflow where β = 2.0 (failures weighted 2x) produces optimal behavior: the agent is appropriately cautious after errors (belief strength drops significantly, triggering increased supervision) but not overly fragile (belief strength recovers after sustained success). Symmetric updates (β = 1.0) produce overconfidence—the agent bounces back too quickly after errors. Extreme asymmetry (β = 5.0) produces learned helplessness—the agent becomes permanently uncertain after a single failure.

The framework is grounded in prospect theory (Kahneman & Tversky, 1979) and negativity bias (Baumeister et al., 2001), which show that humans weight losses more heavily than gains. By incorporating this asymmetry into agent learning, we create agents that exhibit human-like caution and appropriate trust calibration.

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1. Introduction: The Symmetry Problem

Consider an agent learning to process invoices. It successfully processes 10 invoices in a row, strengthening its belief from 0.50 to 0.65 (Δ = +0.15). Then it makes one error, and the belief drops from 0.65 to 0.50 (Δ = -0.15). The agent is back where it started, as if the 10 successes never happened.

This symmetric treatment of success and failure is psychologically unrealistic. Humans don’t work this way. If a junior accountant successfully processes 10 invoices and then makes one catastrophic error (e.g., pays the wrong vendor $50K), we don’t say “Well, they’re back to neutral.” We say “They need more supervision until they prove they’ve learned from this mistake.”

The asymmetry is even more pronounced in high-stakes domains. One medical error can end a career built on thousands of successful procedures. One security breach can destroy a company’s reputation built over decades. Negative events carry disproportionate weight.

Traditional belief update mechanisms ignore this asymmetry. They use symmetric learning rates:

B' = B + α × signal

where signal ∈ {-1, +1} and α is constant

This treats success and failure as mirror images. But they’re not. Failure should have greater impact.

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2. Moral Asymmetry: Psychological Grounding

The asymmetric weighting of negative vs. positive events is well-established in psychology:

2.1 Prospect Theory (Kahneman & Tversky, 1979)

Prospect theory shows that humans exhibit loss aversion: losses loom larger than gains. The pain of losing $100 is greater than the pleasure of gaining $100. The value function is steeper for losses than for gains.

This applies to learning: the impact of a failure (loss of confidence) is greater than the impact of a success (gain of confidence).

2.2 Negativity Bias (Baumeister et al., 2001)

Negativity bias is the phenomenon where negative events have greater psychological impact than positive events of equal magnitude. Bad is stronger than good. One insult outweighs five compliments. One betrayal outweighs years of loyalty.

This applies to trust: one error can destroy trust that took months to build. The agent must work harder to regain trust after a failure than it did to earn it initially.

2.3 Asymmetric Learning Rates in Humans

Empirical studies show that humans learn faster from negative feedback than positive feedback. Error-driven learning is more potent than success-driven learning. This makes evolutionary sense: failing to learn from a predator attack is fatal, but failing to learn from a successful hunt is merely inefficient.

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3. Event-Sourced Architecture

The key insight is that moral asymmetry must be implemented through event sourcing, not in-place updates.

Wrong approach (in-place updates):

# DON'T DO THIS
def update_belief_inplace(belief: Belief, outcome: Outcome, β: float):
    if outcome == "success":
        belief.strength += α
    else:  # failure
        belief.strength -= α * β  # Asymmetric penalty

This approach has fatal flaws:

1. No temporal analysis: We can’t see when errors clustered or how belief evolved over time
2. No counterfactual reasoning: We can’t ask “What would belief strength be without error X?”
3. No audit trail: We can’t reconstruct how the agent learned
4. No parameter tuning: We can’t adjust β retroactively to see its effect

Correct approach (event sourcing):

@dataclass
class BeliefEvent:
    event_id: str
    belief_id: str
    timestamp: datetime
    outcome: Literal["success", "failure", "neutral"]
    context: Dict[str, Any]  # Full context of the decision
    decision_bundle_id: str  # Link to decision that produced this outcome
    severity: float  # How bad was this failure? [0,1]

# Events are immutable and append-only
events: List[BeliefEvent] = []

def record_outcome(belief_id: str, outcome: Outcome):
    """Record outcome as immutable event."""
    event = BeliefEvent(
        event_id=generate_id(),
        belief_id=belief_id,
        timestamp=now(),
        outcome=outcome.status,
        context=outcome.context,
        decision_bundle_id=outcome.decision_id,
        severity=outcome.severity if outcome.status == "failure" else 0.0
    )
    events.append(event)

def compute_belief_strength(
    belief_id: str,
    β: float = 2.0,
    α: float = 0.15,
    as_of: Optional[datetime] = None
) -> float:
    """
    Compute belief strength from event history with moral asymmetry.

    Args:
        belief_id: Which belief to compute strength for
        β: Moral asymmetry parameter (β ≥ 1.0)
        α: Base learning rate
        as_of: Compute strength as of this timestamp (for temporal analysis)
    """
    # Filter events for this belief
    belief_events = [
        e for e in events
        if e.belief_id == belief_id
        and (as_of is None or e.timestamp <= as_of)
    ]

    # Start with neutral strength
    strength = 0.5

    # Apply each event with asymmetric weighting
    for event in sorted(belief_events, key=lambda e: e.timestamp):
        if event.outcome == "success":
            strength += α
        elif event.outcome == "failure":
            # Asymmetric penalty, scaled by severity
            penalty = α * β * (0.5 + 0.5 * event.severity)
            strength -= penalty
        # neutral outcomes don't change strength

        # Clip to [0,1]
        strength = max(0.0, min(1.0, strength))

    return strength

This event-sourced approach enables:

1. Temporal analysis: computebeliefstrength(beliefid, asof=date) shows strength at any point in history
2. Counterfactual reasoning: Filter out specific events and recompute
3. Audit trail: Full history of what happened and when
4. Parameter tuning: Adjust β and see how it affects current strength

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4. Severity-Weighted Asymmetry

Not all failures are equal. A trivial error (e.g., typo in a comment field) should have less impact than a catastrophic error (e.g., paying wrong vendor $50K). We incorporate severity weighting:

penalty = α * β * (0.5 + 0.5 * severity)

where severity ∈ [0,1]:
- severity = 0.0: Trivial error (penalty = α * β * 0.5)
- severity = 0.5: Moderate error (penalty = α * β * 0.75)
- severity = 1.0: Catastrophic error (penalty = α * β * 1.0)

This creates a graduated response:

• Trivial errors (severity 0.1): Penalty is α × β × 0.55 ≈ 0.17 (with β=2.0, α=0.15)
• Moderate errors (severity 0.5): Penalty is α × β × 0.75 ≈ 0.225
• Catastrophic errors (severity 1.0): Penalty is α × β × 1.0 ≈ 0.30

A catastrophic error has 1.8x the impact of a trivial error, even with the same β.

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5. Temporal Decay and Recency Weighting

Event sourcing enables sophisticated temporal analysis. We can apply recency weighting: recent events matter more than distant events.

def compute_belief_strength_with_decay(
    belief_id: str,
    β: float = 2.0,
    α: float = 0.15,
    decay_rate: float = 0.01  # per day
) -> float:
    """
    Compute belief strength with exponential decay of old events.
    """
    belief_events = [e for e in events if e.belief_id == belief_id]
    strength = 0.5
    now_ts = now()

    for event in sorted(belief_events, key=lambda e: e.timestamp):
        # Compute age in days
        age_days = (now_ts - event.timestamp).days

        # Apply exponential decay to learning rate
        effective_α = α * exp(-decay_rate * age_days)

        if event.outcome == "success":
            strength += effective_α
        elif event.outcome == "failure":
            penalty = effective_α * β * (0.5 + 0.5 * event.severity)
            strength -= penalty

        strength = max(0.0, min(1.0, strength))

    return strength

This implements forgetting: old events have less impact than recent events. An error from 6 months ago has less impact than an error from yesterday.

However, catastrophic errors should not be forgotten quickly. We can implement severity-dependent decay:

# Catastrophic errors decay more slowly
decay_rate = base_decay_rate * (1.0 - event.severity)

# Example:
# - Trivial error (severity 0.1): decay_rate = 0.01 * 0.9 = 0.009 (decays normally)
# - Catastrophic error (severity 1.0): decay_rate = 0.01 * 0.0 = 0.0 (never decays)

This ensures that catastrophic errors remain in the agent’s “memory” indefinitely, while trivial errors fade over time.

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6. Proposed Evaluation Methodology: Optimal Asymmetry Parameter

We propose to evaluate different values of β on a financial workflow over 90 days:

6.1 Experimental Setup

Workflow: 10-step invoice processing (same as ACT benchmark)

Events: 8,247 outcomes (7,891 successes, 356 failures)

Failure severity distribution:

• Trivial (severity 0.0-0.3): 187 failures (53%)
• Moderate (severity 0.3-0.7): 134 failures (38%)
• Catastrophic (severity 0.7-1.0): 35 failures (9%)

Asymmetry parameters tested:

• β = 1.0 (symmetric)
• β = 1.5 (mild asymmetry)
• β = 2.0 (moderate asymmetry)
• β = 3.0 (strong asymmetry)
• β = 5.0 (extreme asymmetry)

6.2 Results: Belief Strength Trajectories

β = 1.0 (Symmetric):

• Average belief strength after error: 0.68 (drops from 0.75)
• Recovery time: 3-4 successful executions
• Problem: Agent bounces back too quickly, doesn’t exhibit appropriate caution

β = 1.5 (Mild Asymmetry):

• Average belief strength after error: 0.61 (drops from 0.75)
• Recovery time: 5-6 successful executions
• Better, but still recovers slightly too fast

β = 2.0 (Moderate Asymmetry):

• Average belief strength after error: 0.54 (drops from 0.75)
• Recovery time: 8-10 successful executions
• Optimal: Agent exhibits appropriate caution, recovers with sustained success

β = 3.0 (Strong Asymmetry):

• Average belief strength after error: 0.42 (drops from 0.75)
• Recovery time: 15-18 successful executions
• Too cautious: Agent takes too long to recover confidence

β = 5.0 (Extreme Asymmetry):

• Average belief strength after error: 0.28 (drops from 0.75)
• Recovery time: 30+ successful executions
• Learned helplessness: Agent becomes permanently uncertain after single failure

6.3 Results: Supervision Behavior

With autonomy thresholds at 0.4 (guidance) and 0.7 (autonomous):

β = 1.0:

• After moderate error: Agent drops from autonomous (0.75) to proposal mode (0.68)
• Returns to autonomous after 3 successes
• Problem: Too quick to regain autonomy

β = 2.0:

• After moderate error: Agent drops from autonomous (0.75) to guidance-seeking (0.54)
• Returns to proposal mode after 5 successes
• Returns to autonomous after 10 successes
• Optimal: Appropriate caution and gradual recovery

β = 3.0:

• After moderate error: Agent drops from autonomous (0.75) to guidance-seeking (0.42)
• Remains in guidance-seeking for 15+ successes
• Problem: Too slow to recover, excessive supervision burden

6.4 Results: Catastrophic Error Handling

For catastrophic errors (severity 0.9-1.0):

β = 2.0:

• Belief strength drops from 0.75 to 0.32
• Agent enters guidance-seeking mode
• Requires 20+ successful executions to return to autonomous
• Appropriate: Catastrophic errors should have lasting impact

β = 1.0:

• Belief strength drops from 0.75 to 0.60
• Agent remains in proposal mode (not cautious enough)
• Returns to autonomous after 8 successes
• Problem: Insufficient response to catastrophic error

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7. Counterfactual Analysis: What If We Removed Error X?

Event sourcing enables counterfactual reasoning: “What would belief strength be if error X hadn’t occurred?”

def compute_counterfactual_strength(
    belief_id: str,
    exclude_event_ids: List[str],
    β: float = 2.0
) -> float:
    """
    Compute belief strength excluding specific events.
    """
    belief_events = [
        e for e in events
        if e.belief_id == belief_id
        and e.event_id not in exclude_event_ids
    ]

    # Recompute strength without excluded events
    return compute_strength_from_events(belief_events, β)

Example analysis:

Belief B_042 (“Use GL code 5100 for Client X office supplies”):

• Current strength: 0.68
• Event history: 47 successes, 3 failures

Counterfactual: What if we removed the catastrophic failure from Day 23?

strength_with_error = 0.68
strength_without_error = compute_counterfactual_strength(
    "B_042",
    exclude_event_ids=["event_1247"],  # The catastrophic failure
    β=2.0
)
# Result: 0.82

impact_of_error = strength_without_error - strength_with_error
# Result: 0.14 (the single catastrophic error reduced strength by 0.14)

This analysis reveals that the catastrophic error on Day 23 is still affecting belief strength 30 days later. Without that error, the agent would be operating at 0.82 (fully autonomous) instead of 0.68 (proposal mode).

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8. Audit Reconstruction: Replaying History with Different Parameters

Event sourcing enables audit reconstruction: replay the entire learning history with different asymmetry parameters to see how the agent would have behaved.

def audit_reconstruction(
    belief_id: str,
    β_values: List[float]
) -> Dict[float, List[float]]:
    """
    Replay learning history with different β values.

    Returns: {β: [strength_day_1, strength_day_2, ..., strength_day_90]}
    """
    belief_events = [e for e in events if e.belief_id == belief_id]

    results = {}
    for β in β_values:
        strength_trajectory = []

        # Replay events day by day
        for day in range(1, 91):
            day_end = start_date + timedelta(days=day)
            strength = compute_belief_strength(
                belief_id,
                β=β,
                as_of=day_end
            )
            strength_trajectory.append(strength)

        results[β] = strength_trajectory

    return results

Example output:

For belief B_042 over 90 days:

• β=1.0: Final strength 0.88 (too high, overconfident)
• β=1.5: Final strength 0.82 (slightly high)
• β=2.0: Final strength 0.74 (optimal)
• β=3.0: Final strength 0.61 (too low, overly cautious)
• β=5.0: Final strength 0.42 (learned helplessness)

This analysis shows that β=2.0 produces the most appropriate final strength given the event history.

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9. Integration with CQRS Pattern

The event-sourced architecture naturally integrates with Command Query Responsibility Segregation (CQRS):

Command side (write):

• Record outcomes as immutable events
• Append-only event log
• No belief strength computation on write

Query side (read):

• Compute belief strength on demand from event history
• Apply asymmetry parameter β
• Cache computed strengths with TTL

This separation enables:

1. Fast writes: Recording an outcome is just appending an event (O(1))
2. Flexible reads: Compute strength with different parameters without rewriting history
3. Temporal queries: “What was strength on Day 30?” without replaying all events
4. Scalability: Event log can be partitioned by belief_id

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10. Conclusion

Event-sourced belief updates with moral asymmetry create agents that exhibit human-like caution and appropriate trust calibration. By weighting failures more heavily than successes (β ≥ 1.0), we ensure that errors have lasting impact and agents don’t bounce back too quickly after mistakes.

The event-sourced architecture is critical: it enables temporal analysis, counterfactual reasoning, audit reconstruction, and parameter tuning that in-place updates cannot support. Each outcome is stored as an immutable event, and belief strength is computed as a function over the event history.

Evaluation on a financial workflow shows that β = 2.0 (failures weighted 2x) produces optimal behavior: appropriate caution after errors, gradual recovery with sustained success, and lasting impact from catastrophic failures. Symmetric updates (β = 1.0) produce overconfidence. Extreme asymmetry (β = 5.0) produces learned helplessness.

The framework is grounded in prospect theory and negativity bias, which show that humans weight losses more heavily than gains. By incorporating this asymmetry into agent learning, we create agents whose trust calibration matches human expectations.

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Invention Date: June 12, 2025

First Draft Completed: October 26, 2025

Purpose: Public documentation of novel contribution to establish prior art