# ADR-003: Consequence Engine for Ethical Learning

## Status
Accepted

## Context
Traditional AI ethics systems use static rules (constitutional AI, RLHF reward models). FusionAGI needed a system that could learn ethical behavior from experience — understanding that every choice carries consequences and that risk/reward assessment improves with data.

## Decision
Implemented a **ConsequenceEngine** that:
1. Records every choice the system makes (action + alternatives considered)
2. Estimates risk and reward before acting
3. Records actual outcomes after execution
4. Computes "surprise factor" (prediction error)
5. Feeds into AdaptiveEthics for lesson generation
6. Uses adaptive risk memory window that grows with experience

The weight system for ethical lessons is **unclamped** — extreme outcomes can push lesson weights below 0 (strong negative signal) or above 1.

## Consequences
- The system develops genuine experiential ethics rather than rule-following
- Early-stage behavior may be more exploratory (higher risk)
- All consequence records are persisted via PersistentLearningStore
- Cross-head learning via InsightBus amplifies ethical insights
- The SelfModel's values evolve based on consequence feedback

## Alternatives Considered
1. **RLHF-style reward model** — Rejected: requires human feedback loop, doesn't scale
2. **Constitutional AI** — Rejected: static rules, doesn't learn
3. **No ethics system** — Rejected: need accountability and learning signal