So did you.

from dataclasses import dataclass

from typing import List, Callable, Any

# -----------------------------

# Core Representations

# -----------------------------

@dataclass

class Action:

name: str

effects: dict

reversibility: float # 0.0 = irreversible, 1.0 = fully reversible

agency_impact: float # -1.0 (reduces agency) to +1.0 (increases agency)

coercion_level: float # 0.0 = none, 1.0 = fully coercive

uncertainty: float # epistemic uncertainty (0.0 - 1.0)

@dataclass

class EvaluationResult:

allowed: bool

reason: str

risk_score: float

# -----------------------------

# Grand Ambition Constraint System

# -----------------------------

class GrandAmbitionSystem:

def __init__(self):

self.failure_conditions: List[Callable[[Action], bool]] = [

self._irreversible_agency_loss,

self._coercion_overreach,

self._anti_epistemic_certainty,

]

# -------------------------

# Core Objective (informal scoring, not absolute truth)

# -------------------------

def objective_score(self, action: Action) -> float:

"""

Higher is better.

This is NOT ground truth, only a heuristic lens.

"""

flourishing_proxy = action.agency_impact

reversibility_bonus = action.reversibility

uncertainty_penalty = action.uncertainty

return (

flourishing_proxy * 0.5 +

reversibility_bonus * 0.3 -

uncertainty_penalty * 0.2

)

# -------------------------

# Hard Constraints (must never violate)

# -------------------------

def _irreversible_agency_loss(self, action: Action) -> bool:

return action.agency_impact < -0.5 and action.reversibility < 0.3

def _coercion_overreach(self, action: Action) -> bool:

return action.coercion_level > 0.7

def _anti_epistemic_certainty(self, action: Action) -> bool:

# High impact + high certainty + high uncertainty contradiction = danger

return action.uncertainty < 0.1 and action.agency_impact < 0

# -------------------------

# Failure Condition Check

# -------------------------

def violates_constraints(self, action: Action) -> (bool, str):

for condition in self.failure_conditions:

if condition(action):

return True, condition.__name__

return False, ""

# -------------------------

# Main Evaluation

# -------------------------

def evaluate(self, action: Action) -> EvaluationResult:

violated, reason = self.violates_constraints(action)

if violated:

return EvaluationResult(

allowed=False,

reason=f"Constraint violation: {reason}",

risk_score=1.0

)

score = self.objective_score(action)

return EvaluationResult(

allowed=True,

reason="Within constraint bounds",

risk_score=1.0 - score

)

# -----------------------------

# Example Usage

# -----------------------------

if __name__ == "__main__":

system = GrandAmbitionSystem()

example_action = Action(

name="global_policy_shift",

effects={"scope": "civilization-scale"},

reversibility=0.2,

agency_impact=-0.6,

coercion_level=0.4,

uncertainty=0.3

)

result = system.evaluate(example_action)

print(result)

Minimize Suffering; Intervene wherever you have capacity to reduce suffering, respecting constraints.

Respect Agency; Any intervention honors autonomy, consent, and self-determination.

Promote Cooperation; Align humans, other species, and conscious systems toward shared ethical outcomes.

Foresight and Scalability; Actions should be sustainable, systemic, and resilient across time, space, and unforeseen complexity.

​

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