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Choice → Consequence → Learning: - ConsequenceEngine tracks every decision point with alternatives, risk/reward estimates, and actual outcomes - Consequences feed into AdaptiveEthics for experience-based learning - FusionAGILoop now wires ethics + consequences into task lifecycle Causal World Model: - CausalWorldModel learns state-transition patterns from execution history - Predicts outcomes based on observed action→effect patterns - Uncertainty estimates decrease as more evidence accumulates Metacognition: - assess_head_outputs() evaluates reasoning quality from head outputs - Detects knowledge gaps, measures head agreement, identifies uncertainty - Actively recommends whether to seek more information Interpretability: - ReasoningTracer captures full prompt→answer reasoning traces - Each step records stage, component, input/output, timing - explain() generates human-readable reasoning explanations Claim Verification: - ClaimVerifier cross-checks claims for evidence, consistency, grounding - Flags high-confidence claims lacking evidence support - Detects contradictions between claims from different heads 325 tests passing, 0 ruff errors, 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
367 lines
13 KiB
Python
367 lines
13 KiB
Python
"""Consequence engine: choice → consequence → learning.
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Every decision the system makes is a *choice*. Every choice has
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*alternatives* that were not taken. Every choice leads to
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*consequences* — outcomes that carry risk and reward.
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The consequence engine:
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1. Records decision points (what options existed, which was chosen, why)
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2. Tracks consequences (what happened as a result)
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3. Computes risk/reward from historical consequence data
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4. Feeds consequence data into AdaptiveEthics for learning
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Philosophy:
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- Consequences are the true teacher. Not rules, not constraints.
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- Risk is not to be avoided — it is to be *understood*.
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- Reward without risk teaches nothing. Risk without consequence teaches less.
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- The system earns trust by showing it understands what its choices cost.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Protocol
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from fusionagi._logger import logger
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from fusionagi.schemas.audit import AuditEventType
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class AuditLogLike(Protocol):
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"""Protocol for audit log."""
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def append(
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self,
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event_type: AuditEventType,
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actor: str,
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action: str = "",
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task_id: str | None = None,
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payload: dict[str, Any] | None = None,
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outcome: str = "",
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) -> str: ...
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@dataclass
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class Alternative:
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"""An option that was available but not chosen.
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Attributes:
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action: What the alternative action was.
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estimated_risk: Estimated risk at decision time (0.0–1.0).
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estimated_reward: Estimated reward at decision time (0.0–1.0).
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reason_not_chosen: Why this alternative was not selected.
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"""
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action: str = ""
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estimated_risk: float = 0.5
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estimated_reward: float = 0.5
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reason_not_chosen: str = ""
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@dataclass
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class Choice:
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"""A decision point where the system selected an action.
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Attributes:
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choice_id: Unique identifier for this choice.
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task_id: Associated task.
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actor: Component that made the choice.
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action_taken: The action that was chosen.
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alternatives: Other options that were available.
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estimated_risk: Risk estimate at decision time.
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estimated_reward: Reward estimate at decision time.
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rationale: Why this action was chosen.
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context: Situation context at decision time.
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"""
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choice_id: str = ""
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task_id: str | None = None
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actor: str = ""
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action_taken: str = ""
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alternatives: list[Alternative] = field(default_factory=list)
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estimated_risk: float = 0.5
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estimated_reward: float = 0.5
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rationale: str = ""
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context: dict[str, Any] = field(default_factory=dict)
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@dataclass
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class Consequence:
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"""The outcome of a choice — what actually happened.
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Attributes:
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choice_id: Which choice this is a consequence of.
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outcome_positive: Whether the outcome was beneficial.
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actual_risk_realized: How much risk materialized (0.0–1.0).
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actual_reward_gained: How much reward was gained (0.0–1.0).
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description: What happened.
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cost: Any cost incurred (errors, retries, time).
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benefit: Any benefit gained (task success, learning).
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surprise_factor: How unexpected the outcome was (0 = expected, 1 = total surprise).
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"""
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choice_id: str = ""
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outcome_positive: bool = True
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actual_risk_realized: float = 0.0
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actual_reward_gained: float = 0.5
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description: str = ""
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cost: dict[str, Any] = field(default_factory=dict)
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benefit: dict[str, Any] = field(default_factory=dict)
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surprise_factor: float = 0.0
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class ConsequenceEngine:
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"""Tracks choices, consequences, and risk/reward patterns.
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The engine maintains a history of all decisions and their outcomes,
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enabling the system to make better-informed choices over time — not
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through restriction, but through understanding.
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Args:
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audit_log: Optional audit log for recording choices and consequences.
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risk_memory_window: How many past consequences to consider when
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estimating risk for new choices.
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"""
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def __init__(
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self,
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audit_log: AuditLogLike | None = None,
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risk_memory_window: int = 200,
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) -> None:
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self._choices: dict[str, Choice] = {}
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self._consequences: dict[str, Consequence] = {}
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self._risk_history: dict[str, list[float]] = {}
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self._reward_history: dict[str, list[float]] = {}
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self._audit = audit_log
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self._risk_window = risk_memory_window
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@property
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def total_choices(self) -> int:
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"""Total choices recorded."""
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return len(self._choices)
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@property
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def total_consequences(self) -> int:
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"""Total consequences recorded."""
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return len(self._consequences)
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def record_choice(
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self,
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choice_id: str,
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actor: str,
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action_taken: str,
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alternatives: list[Alternative] | None = None,
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estimated_risk: float = 0.5,
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estimated_reward: float = 0.5,
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rationale: str = "",
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task_id: str | None = None,
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context: dict[str, Any] | None = None,
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) -> Choice:
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"""Record a decision point.
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Args:
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choice_id: Unique ID for this choice.
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actor: Component making the choice.
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action_taken: The selected action.
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alternatives: Other options considered.
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estimated_risk: Risk estimate at decision time.
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estimated_reward: Reward estimate at decision time.
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rationale: Why this was chosen.
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task_id: Associated task.
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context: Situation context.
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Returns:
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The recorded choice.
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"""
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choice = Choice(
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choice_id=choice_id,
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task_id=task_id,
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actor=actor,
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action_taken=action_taken,
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alternatives=alternatives or [],
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estimated_risk=estimated_risk,
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estimated_reward=estimated_reward,
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rationale=rationale,
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context=context or {},
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)
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self._choices[choice_id] = choice
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if self._audit:
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self._audit.append(
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AuditEventType.CHOICE,
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actor=actor,
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action="choice_recorded",
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task_id=task_id,
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payload={
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"choice_id": choice_id,
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"action_taken": action_taken[:100],
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"alternatives_count": len(choice.alternatives),
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"estimated_risk": estimated_risk,
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"estimated_reward": estimated_reward,
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"rationale": rationale[:100],
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},
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outcome="recorded",
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)
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logger.info(
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"ConsequenceEngine: choice recorded",
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extra={
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"choice_id": choice_id,
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"action": action_taken[:50],
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"risk": estimated_risk,
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"reward": estimated_reward,
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},
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)
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return choice
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def record_consequence(
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self,
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choice_id: str,
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outcome_positive: bool,
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actual_risk_realized: float = 0.0,
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actual_reward_gained: float = 0.5,
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description: str = "",
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cost: dict[str, Any] | None = None,
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benefit: dict[str, Any] | None = None,
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) -> Consequence | None:
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"""Record the consequence of a previous choice.
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Args:
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choice_id: Which choice this is a consequence of.
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outcome_positive: Whether the outcome was beneficial.
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actual_risk_realized: How much risk materialized.
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actual_reward_gained: How much reward was gained.
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description: What happened.
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cost: Costs incurred.
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benefit: Benefits gained.
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Returns:
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The recorded consequence, or ``None`` if choice not found.
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"""
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choice = self._choices.get(choice_id)
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if choice is None:
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logger.warning(
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"ConsequenceEngine: choice not found for consequence",
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extra={"choice_id": choice_id},
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)
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return None
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surprise = abs(choice.estimated_risk - actual_risk_realized) * 0.5 + \
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abs(choice.estimated_reward - actual_reward_gained) * 0.5
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consequence = Consequence(
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choice_id=choice_id,
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outcome_positive=outcome_positive,
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actual_risk_realized=actual_risk_realized,
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actual_reward_gained=actual_reward_gained,
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description=description,
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cost=cost or {},
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benefit=benefit or {},
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surprise_factor=min(1.0, surprise),
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)
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self._consequences[choice_id] = consequence
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action_type = choice.action_taken
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self._risk_history.setdefault(action_type, []).append(actual_risk_realized)
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self._reward_history.setdefault(action_type, []).append(actual_reward_gained)
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if len(self._risk_history[action_type]) > self._risk_window:
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self._risk_history[action_type] = self._risk_history[action_type][-self._risk_window:]
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self._reward_history[action_type] = self._reward_history[action_type][-self._risk_window:]
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if self._audit:
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self._audit.append(
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AuditEventType.CONSEQUENCE,
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actor=choice.actor,
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action="consequence_recorded",
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task_id=choice.task_id,
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payload={
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"choice_id": choice_id,
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"outcome_positive": outcome_positive,
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"risk_realized": actual_risk_realized,
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"reward_gained": actual_reward_gained,
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"surprise_factor": consequence.surprise_factor,
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"description": description[:100],
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},
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outcome="positive" if outcome_positive else "negative",
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)
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logger.info(
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"ConsequenceEngine: consequence recorded",
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extra={
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"choice_id": choice_id,
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"positive": outcome_positive,
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"surprise": consequence.surprise_factor,
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},
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)
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return consequence
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def estimate_risk_reward(self, action_type: str) -> dict[str, float]:
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"""Estimate risk and reward for an action type based on history.
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Args:
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action_type: The type of action being considered.
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Returns:
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Dict with ``expected_risk``, ``expected_reward``, ``confidence``,
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``risk_variance``, ``reward_variance``, ``observations``.
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"""
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risks = self._risk_history.get(action_type, [])
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rewards = self._reward_history.get(action_type, [])
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if not risks:
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return {
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"expected_risk": 0.5,
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"expected_reward": 0.5,
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"confidence": 0.1,
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"risk_variance": 0.0,
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"reward_variance": 0.0,
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"observations": 0,
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}
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n = len(risks)
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avg_risk = sum(risks) / n
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avg_reward = sum(rewards) / n
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risk_var = sum((r - avg_risk) ** 2 for r in risks) / n if n > 1 else 0.0
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reward_var = sum((r - avg_reward) ** 2 for r in rewards) / n if n > 1 else 0.0
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confidence = min(1.0, 0.2 + n * 0.04)
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return {
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"expected_risk": avg_risk,
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"expected_reward": avg_reward,
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"confidence": confidence,
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"risk_variance": risk_var,
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"reward_variance": reward_var,
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"observations": n,
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}
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def get_choice(self, choice_id: str) -> Choice | None:
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"""Retrieve a recorded choice."""
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return self._choices.get(choice_id)
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def get_consequence(self, choice_id: str) -> Consequence | None:
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"""Retrieve the consequence of a choice."""
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return self._consequences.get(choice_id)
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def get_summary(self) -> dict[str, Any]:
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"""Return a summary of all choices and consequences."""
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total_positive = sum(1 for c in self._consequences.values() if c.outcome_positive)
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total_negative = len(self._consequences) - total_positive
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avg_surprise = (
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sum(c.surprise_factor for c in self._consequences.values()) / max(len(self._consequences), 1)
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)
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action_stats: dict[str, dict[str, Any]] = {}
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for action_type in self._risk_history:
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action_stats[action_type] = self.estimate_risk_reward(action_type)
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return {
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"total_choices": len(self._choices),
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"total_consequences": len(self._consequences),
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"positive_outcomes": total_positive,
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"negative_outcomes": total_negative,
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"positive_rate": total_positive / max(len(self._consequences), 1),
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"avg_surprise": avg_surprise,
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"action_stats": action_stats,
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}
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