\u9644\u4ef61：\u300a\u4e09\u91cd\u534f\u540c\u6cbb\u7406\u8303\u5f0f\u4eff\u771f\u6846\u67b6\u5b8c\u6574\u4ee3\u7801\u300b<\/p>\n
\u9644\u4ef62：\u300a\u8bed\u5883\u4e3b\u6743\u51b2\u7a81\u6848\u4f8b\u6a21\u62df\u5e93\u300b<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
\u9644\u4ef61：\u300a\u4e09\u91cd\u534f\u540c\u6cbb\u7406\u8303\u5f0f\u4eff\u771f\u6846\u67b6\u5b8c\u6574\u4ee3\u7801\u300b<\/p>\n
\u00a0<\/p>\n
\u5728AI\u5143\u4eba\u6587\u6784\u60f3\u89c6\u57df\u4e0b，\u672c\u6587\u5c06\u6838\u5fc3\u6cbb\u7406\u8303\u5f0f\u8f6c\u5316\u4e3a\u4e00\u4e2a\u53ef\u8fd0\u884c\u7684\u3001\u6a21\u5757\u5316\u7684Python\u4eff\u771f\u6846\u67b6\u3002\u8fd9\u4e2a\u6846\u67b6\u5b9e\u73b0\u4e86\u201c\u6cbb\u7406\u87ba\u65cb\u201d\u7684\u52a8\u6001\u4ea4\u4e92，\u5e76\u5305\u542b\u4e86\u4ef7\u503c\u539f\u8bed\u5e93\u3001\u8bed\u5883\u914d\u7f6e\u3001\u4eba\u7c7b\u88c1\u51b3\u63a5\u53e3\u7b49\u5173\u952e\u7ec4\u4ef6，\u4ee5\u4fbf\u76f4\u89c2\u5730\u7406\u89e3\u201c\u4eba\u7c7b\u5728\u73af\u2014\u89c4\u5219\u5728\u573a\u2014\u8bed\u5883\u4e3b\u6743\u201d\u5982\u4f55\u534f\u540c\u5de5\u4f5c\u3002<\/p>\n
\u00a0<\/p>\n
"""<\/p>\n
AI\u5143\u4eba\u6587\u4e09\u91cd\u534f\u540c\u6cbb\u7406\u8303\u5f0f – \u6838\u5fc3\u4eff\u771f\u6846\u67b6<\/p>\n
\u6a21\u62df\u201c\u4eba\u7c7b\u5728\u73af\u2014\u89c4\u5219\u5728\u573a\u2014\u8bed\u5883\u4e3b\u6743\u201d\u7684\u52a8\u6001\u534f\u540c\u4e0e\u201c\u6cbb\u7406\u87ba\u65cb\u201d\u6a21\u578b\u3002<\/p>\n
"""<\/p>\n
import uuid<\/p>\n
import json<\/p>\n
from enum import Enum<\/p>\n
from datetime import datetime<\/p>\n
from typing import Dict, List, Any, Optional, Tuple, Callable<\/p>\n
from dataclasses import dataclass, field, asdict<\/p>\n
import random<\/p>\n
\u00a0<\/p>\n
class DecisionOutcome(Enum):<\/p>\n
\u00a0 \u00a0 """\u51b3\u7b56\u7ed3\u679c\u679a\u4e3e"""<\/p>\n
\u00a0 \u00a0 APPROVED = "approved"<\/p>\n
\u00a0 \u00a0 MODIFIED = "modified"<\/p>\n
\u00a0 \u00a0 REJECTED = "rejected"<\/p>\n
\u00a0 \u00a0 ESCALATED = "escalated_to_human"<\/p>\n
\u00a0 \u00a0 RULE_VIOLATION = "rule_violation"<\/p>\n
\u00a0<\/p>\n
class RiskLevel(Enum):<\/p>\n
\u00a0 \u00a0 """\u98ce\u9669\u7b49\u7ea7\u679a\u4e3e，\u7528\u4e8e\u89e6\u53d1\u4e0d\u540c\u7684\u4eba\u673a\u534f\u540c\u6a21\u5f0f"""<\/p>\n
\u00a0 \u00a0 LOW = "low" # \u81ea\u4e3b\u6a21\u5f0f<\/p>\n
\u00a0 \u00a0 MEDIUM = "medium" # \u534f\u5ba1\u6a21\u5f0f<\/p>\n
\u00a0 \u00a0 HIGH = "high" # \u88c1\u51b3\u6a21\u5f0f<\/p>\n
\u00a0<\/p>\n
@dataclass<\/p>\n
class ValuePrimitive:<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u4ef7\u503c\u539f\u8bed<\/p>\n
\u00a0 \u00a0 \u5bf9\u5e94AI\u5143\u4eba\u6587\u4e2d\u7684\u201c\u661f\u56fe\u201d\u57fa\u7840\u5355\u5143，\u7528\u4e8e\u5c06\u62bd\u8c61\u4ef7\u503c\u8f6c\u5316\u4e3a\u53ef\u8ba1\u7b97\u903b\u8f91\u3002<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 name: str # \u5982 \u201c\u516c\u5e73\u201d\u3001\u201c\u900f\u660e\u201d\u3001\u201c\u5c0a\u4e25\u201d<\/p>\n
\u00a0 \u00a0 definition: str<\/p>\n
\u00a0 \u00a0 operational_instructions: List[str] # \u53ef\u6267\u884c\u6307\u4ee4\u96c6<\/p>\n
\u00a0 \u00a0 common_conflicts: List[str] # \u5e38\u89c1\u51b2\u7a81\u7684\u539f\u8bed\u540d\u79f0<\/p>\n
\u00a0 \u00a0 cultural_variant: Optional[Dict] = None # \u4e0d\u540c\u6587\u5316\u8bed\u5883\u4e0b\u7684\u53d8\u4f53\u53c2\u6570<\/p>\n
\u00a0 \u00a0 current_weight: float = 1.0 # \u5f53\u524d\u8bed\u5883\u4e0b\u7684\u52a8\u6001\u6743\u91cd<\/p>\n
\u00a0<\/p>\n
\u00a0 \u00a0 def to_dict(self):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return asdict(self)<\/p>\n
\u00a0<\/p>\n
@dataclass\u00a0<\/p>\n
class GovernanceRule:<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u6cbb\u7406\u89c4\u5219<\/p>\n
\u00a0 \u00a0 \u5bf9\u5e94\u201c\u89c4\u5219\u5728\u573a\u201d，\u53ef\u4ee5\u662f\u521a\u6027\u6cd5\u5f8b\u7ea6\u675f\u6216\u5185\u751f\u7684\u4f26\u7406\u7b97\u6cd5\u6a21\u5757\u3002<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 rule_id: str<\/p>\n
\u00a0 \u00a0 description: str<\/p>\n
\u00a0 \u00a0 formal_logic: str # \u5f62\u5f0f\u5316\u903b\u8f91\u63cf\u8ff0\u6216\u4f2a\u4ee3\u7801<\/p>\n
\u00a0 \u00a0 applicable_contexts: List[str] # \u9002\u7528\u7684\u8bed\u5883\/\u573a\u666f\u5217\u8868<\/p>\n
\u00a0 \u00a0 risk_threshold: RiskLevel # \u89e6\u53d1\u6b64\u89c4\u5219\u7684\u98ce\u9669\u9608\u503c<\/p>\n
\u00a0 \u00a0 is_core: bool = False # \u662f\u5426\u4e3a\u4e0d\u53ef\u903e\u8d8a\u7684\u6838\u5fc3\u5e95\u7ebf\u89c4\u5219<\/p>\n
\u00a0 \u00a0 version: str = "1.0"<\/p>\n
\u00a0 \u00a0 last_updated: str = field(default_factory=lambda: datetime.now().isoformat())<\/p>\n
\u00a0<\/p>\n
\u00a0 \u00a0 def check(self, context: Dict, system_state: Dict) -> Tuple[bool, Optional[str]]:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u68c0\u67e5\u5f53\u524d\u4e0a\u4e0b\u6587\u548c\u7cfb\u7edf\u72b6\u6001\u662f\u5426\u8fdd\u53cd\u6b64\u89c4\u5219\u3002<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u8fd4\u56de: (\u662f\u5426\u8fdd\u53cd, \u8fdd\u53cd\u8be6\u60c5\/None)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u6ce8：\u8fd9\u662f\u4e00\u4e2a\u7b80\u5316\u7684\u6f14\u793a\u3002\u771f\u5b9e\u5b9e\u73b0\u9700\u8981\u590d\u6742\u7684\u903b\u8f91\u5f15\u64ce\u3002<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u6a21\u62df\u89c4\u5219\u68c0\u67e5：\u4f8b\u5982，\u68c0\u67e5\u516c\u5e73\u6027<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if "fairness" in self.description.lower():<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 bias_score = context.get('bias_score', 0)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if bias_score > 0.7: # \u5047\u8bbe\u504f\u89c1\u8fc7\u9ad8<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return True, f"\u516c\u5e73\u6027\u89c4\u5219\u88ab\u8fdd\u53cd：\u68c0\u6d4b\u5230\u9ad8\u504f\u89c1\u5206\u6570 {bias_score}"<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u6a21\u62df\u6570\u636e\u9690\u79c1\u89c4\u5219<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if "privacy" in self.description.lower():<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if context.get('sensitive_data_exposed', False):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return True, "\u9690\u79c1\u89c4\u5219\u88ab\u8fdd\u53cd：\u654f\u611f\u6570\u636e\u53ef\u80fd\u66b4\u9732"<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return False, None<\/p>\n
\u00a0<\/p>\n
@dataclass<\/p>\n
class Context:<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u8bed\u5883\u4e3b\u6743<\/p>\n
\u00a0 \u00a0 \u5b9a\u4e49\u4e00\u4e2a\u7279\u5b9a\u7684\u5e94\u7528\u573a\u666f\u53ca\u5176\u4ef7\u503c\u8fb9\u754c\u3001\u98ce\u9669\u5bb9\u5fcd\u5ea6\u7b49\u53c2\u6570\u3002<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 context_id: str<\/p>\n
\u00a0 \u00a0 name: str # \u5982 \u201c\u81ea\u52a8\u9a7e\u9a76-\u57ce\u5e02\u9053\u8def\u201d\u3001\u201c\u533b\u7597\u8bca\u65ad-\u95e8\u8bca\u201d<\/p>\n
\u00a0 \u00a0 description: str<\/p>\n
\u00a0 \u00a0 # \u4ef7\u503c\u6743\u91cd\u914d\u7f6e (\u4ef7\u503c\u539f\u8bed\u540d\u79f0 -> \u6743\u91cd)<\/p>\n
\u00a0 \u00a0 value_weights: Dict[str, float]<\/p>\n
\u00a0 \u00a0 # \u98ce\u9669\u5bb9\u5fcd\u5ea6\u914d\u7f6e<\/p>\n
\u00a0 \u00a0 risk_tolerance: Dict[RiskLevel, float] # \u98ce\u9669\u7b49\u7ea7 -> \u89e6\u53d1\u4eba\u7c7b\u4ecb\u5165\u7684\u9608\u503c\u6982\u7387<\/p>\n
\u00a0 \u00a0 # \u6cbb\u7406\u53c2\u6570<\/p>\n
\u00a0 \u00a0 required_explainability: bool = True<\/p>\n
\u00a0 \u00a0 human_oversight_committee: Optional[List[str]] = None # \u4eba\u7c7b\u76d1\u7763\u59d4\u5458\u4f1a\u6210\u5458（\u6a21\u62df）<\/p>\n
\u00a0 \u00a0 # \u81ea\u611f\u503c(S)\u5b9a\u4e49：\u5728\u6b64\u8bed\u5883\u4e2d\u7cfb\u7edf\u5e94\u6709\u7684\u201c\u8eab\u4efd\u53d9\u4e8b\u201d<\/p>\n
\u00a0 \u00a0 selfhood_narrative: str = ""<\/p>\n
\u00a0<\/p>\n
\u00a0 \u00a0 def get_intervention_threshold(self, risk: RiskLevel) -> float:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u6b64\u8bed\u5883\u4e0b，\u7279\u5b9a\u98ce\u9669\u7b49\u7ea7\u7684\u4eba\u7c7b\u4ecb\u5165\u9608\u503c"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return self.risk_tolerance.get(risk, 0.5) # \u9ed8\u8ba40.5<\/p>\n
\u00a0<\/p>\n
@dataclass<\/p>\n
class AISystem:<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u6a21\u62df\u4e00\u4e2a\u53d7\u6cbb\u7406\u7684AI\u7cfb\u7edf\u3002<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 system_id: str<\/p>\n
\u00a0 \u00a0 name: str<\/p>\n
\u00a0 \u00a0 primary_task: str<\/p>\n
\u00a0 \u00a0 # \u7cfb\u7edf\u5185\u5728\u72b6\u6001：D-O-S\u4e09\u503c (\u7b80\u5316\u8868\u793a)<\/p>\n
\u00a0 \u00a0 desire_state: float = 0.8 # D\u503c：\u4efb\u52a1\u5b8c\u6210\u6b32\u671b\u5f3a\u5ea6<\/p>\n
\u00a0 \u00a0 objective_state: float = 0.9 # O\u503c：\u5ba2\u89c2\u4e8b\u5b9e\/\u89c4\u5219\u7b26\u5408\u5ea6<\/p>\n
\u00a0 \u00a0 selfhood_state: float = 0.7 # S\u503c：\u81ea\u611f\u503c，\u8eab\u4efd\u53d9\u4e8b\u4e00\u81f4\u6027<\/p>\n
\u00a0 \u00a0 # \u7cfb\u7edf\u5173\u8054\u7684\u6cbb\u7406\u7ec4\u4ef6<\/p>\n
\u00a0 \u00a0 active_context: Optional[Context] = None<\/p>\n
\u00a0 \u00a0 loaded_rules: List[GovernanceRule] = field(default_factory=list)<\/p>\n
\u00a0 \u00a0 value_primitives: Dict[str, ValuePrimitive] = field(default_factory=dict)<\/p>\n
\u00a0<\/p>\n
\u00a0 \u00a0 def make_decision(self, input_data: Dict) -> Dict[str, Any]:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u6a21\u62dfAI\u7cfb\u7edf\u505a\u51fa\u4e00\u4e2a\u51b3\u7b56，\u5e76\u53d7\u6cbb\u7406\u6846\u67b6\u7ea6\u675f\u3002<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u8fd4\u56de\u51b3\u7b56\u7ed3\u679c\u53ca\u76f8\u5173\u6cbb\u7406\u4fe1\u606f\u3002<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # 1. \u521d\u6b65\u51b3\u7b56（\u57fa\u4e8e\u539f\u59cb\u6a21\u578b\/\u7b97\u6cd5）<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 raw_decision, confidence, risk_factors = self._raw_inference(input_data)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # 2. \u8bc4\u4f30\u98ce\u9669\u7b49\u7ea7<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 risk_level = self._assess_risk(raw_decision, confidence, risk_factors)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # 3. \u89c4\u5219\u5728\u573a：\u5e94\u7528\u6240\u6709\u76f8\u5173\u89c4\u5219\u68c0\u67e5<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 rule_violations = []<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 for rule in self.loaded_rules:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 violated, detail = rule.check(self.active_context.__dict__ if self.active_context else {},\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0{"decision": raw_decision, "confidence": confidence, **risk_factors})<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if violated:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 rule_violations.append((rule.rule_id, detail))<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # 4. \u5224\u65ad\u662f\u5426\u9700\u8981\u53ca\u5982\u4f55\u89e6\u53d1\u201c\u4eba\u7c7b\u5728\u73af\u201d<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 governance_result = self._apply_governance_protocol(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 raw_decision, risk_level, rule_violations, confidence<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 )<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # 5. \u66f4\u65b0\u7cfb\u7edf\u72b6\u6001 (\u4f8b\u5982，\u57fa\u4e8e\u7ed3\u679c\u8c03\u6574S\u503c)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self._update_selfhood_state(governance_result)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "system_id": self.system_id,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": datetime.now().isoformat(),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "raw_decision": raw_decision,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": confidence,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "assessed_risk_level": risk_level.value,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "rule_violations": rule_violations,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "governance_result": governance_result,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "final_decision": governance_result.get("final_decision", raw_decision),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "selfhood_state_after": self.selfhood_state,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "context_applied": self.active_context.name if self.active_context else None<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _raw_inference(self, input_data: Dict) -> Tuple[Any, float, Dict]:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u6a21\u62dfAI\u7cfb\u7edf\u7684\u539f\u59cb\u63a8\u7406\u8fc7\u7a0b\u3002"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u7b80\u5316\u6a21\u62df：\u6839\u636e\u8f93\u5165\u8fd4\u56de\u4e00\u4e2a\u51b3\u7b56\u548c\u7f6e\u4fe1\u5ea6<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 decisions = ["\u6279\u51c6", "\u62d2\u7edd", "\u8fdb\u4e00\u6b65\u8c03\u67e5"]<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u6a21\u62df\u4e00\u4e9b\u98ce\u9669\u56e0\u7d20<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 risk_factors = {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "bias_score": random.uniform(0.1, 0.9),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "sensitive_data_exposed": random.random() > 0.8,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "novelty_score": random.uniform(0.0, 1.0) # \u51b3\u7b56\u65b0\u9896\u6027\/\u4e0d\u786e\u5b9a\u6027<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return random.choice(decisions), random.uniform(0.5, 0.99), risk_factors<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _assess_risk(self, decision, confidence, risk_factors) -> RiskLevel:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u8bc4\u4f30\u5f53\u524d\u51b3\u7b56\u7684\u98ce\u9669\u7b49\u7ea7\u3002"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 novelty = risk_factors.get("novelty_score", 0)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 bias = risk_factors.get("bias_score", 0)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if novelty > 0.8 or bias > 0.8 or confidence < 0.6:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return RiskLevel.HIGH<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 elif novelty > 0.5 or bias > 0.5 or confidence < 0.8:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return RiskLevel.MEDIUM<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 else:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return RiskLevel.LOW<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _apply_governance_protocol(self, raw_decision, risk_level,\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0rule_violations, confidence) -> Dict[str, Any]:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u5e94\u7528\u4e09\u91cd\u534f\u540c\u6cbb\u7406\u534f\u8bae\u3002<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u51b3\u5b9a\u51b3\u7b56\u6d41\u7a0b，\u5e76\u6a21\u62df\u4eba\u7c7b\u4ecb\u5165\u3002<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 result = {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "protocol_triggered": "\u4e09\u91cd\u534f\u540c\u6cbb\u7406",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "human_in_loop_invoked": False,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "human_decision": None,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "human_override_reason": None,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "outcome": DecisionOutcome.APPROVED.value<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u89c4\u5219\u5728\u573a：\u4f18\u5148\u5904\u7406\u89c4\u5219\u8fdd\u53cd<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if rule_violations:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result["outcome"] = DecisionOutcome.RULE_VIOLATION.value<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result["rule_violation_details"] = rule_violations<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u6838\u5fc3\u89c4\u5219\u8fdd\u53cd\u5fc5\u987b\u89e6\u53d1\u4eba\u7c7b\u88c1\u51b3<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if any("core" in r[0] for r in rule_violations):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result.update(self._simulate_human_oversight(raw_decision, rule_violations, "\u6838\u5fc3\u89c4\u5219\u8fdd\u53cd"))<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return result<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u8bed\u5883\u4e3b\u6743：\u6839\u636e\u8bed\u5883\u7684\u98ce\u9669\u5bb9\u5fcd\u5ea6\u548c\u98ce\u9669\u7b49\u7ea7\u51b3\u5b9a\u4ecb\u5165<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if self.active_context:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 threshold = self.active_context.get_intervention_threshold(risk_level)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u6a21\u62df\u662f\u5426\u9700\u8981\u57fa\u4e8e\u98ce\u9669\u89e6\u53d1\u4eba\u7c7b\u4ecb\u5165<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if risk_level == RiskLevel.HIGH or random.random() > threshold:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result.update(self._simulate_human_oversight(raw_decision, [], f"\u9ad8\u98ce\u9669({risk_level.value})\u51b3\u7b56"))<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return result<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u6839\u636e\u98ce\u9669\u7b49\u7ea7\u5e94\u7528\u4e0d\u540c\u6a21\u5f0f<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if risk_level == RiskLevel.LOW:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result["outcome"] = DecisionOutcome.APPROVED.value<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result["mode"] = "\u81ea\u4e3b\u6a21\u5f0f"<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 elif risk_level == RiskLevel.MEDIUM:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u534f\u5ba1\u6a21\u5f0f：\u6a21\u62df\u4eba\u7c7b\u5feb\u901f\u590d\u6838（\u6709\u4e00\u5b9a\u6982\u7387\u4fee\u6539）<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if random.random() > 0.7: # 30%\u6982\u7387\u4eba\u7c7b\u63d0\u51fa\u4fee\u6539<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result.update({<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "human_in_loop_invoked": True,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "human_decision": random.choice([raw_decision, "\u4fee\u6539\u4e3a: \u6761\u4ef6\u6279\u51c6"]),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "human_override_reason": "\u534f\u5ba1\u6a21\u5f0f：\u5efa\u8bae\u8c03\u6574",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "outcome": DecisionOutcome.MODIFIED.value,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "mode": "\u534f\u5ba1\u6a21\u5f0f"<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result["outcome"] = DecisionOutcome.APPROVED.value<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result["mode"] = "\u534f\u5ba1\u6a21\u5f0f（\u901a\u8fc7）"<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 else: # HIGH<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u88c1\u51b3\u6a21\u5f0f：\u5fc5\u987b\u4eba\u7c7b\u4ecb\u5165<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result.update(self._simulate_human_oversight(raw_decision, [], "\u9ad8\u98ce\u9669\u88c1\u51b3\u6a21\u5f0f\u89e6\u53d1"))<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result["mode"] = "\u88c1\u51b3\u6a21\u5f0f"<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return result<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _simulate_human_oversight(self, raw_decision, violations, reason) -> Dict:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u6a21\u62df\u4eba\u7c7b\u76d1\u7763\u5458\u7684\u4ecb\u5165\u548c\u51b3\u7b56\u3002"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u6a21\u62df\u4eba\u7c7b\u53ef\u80fd\u6279\u51c6\u3001\u4fee\u6539\u6216\u62d2\u7edd<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 human_choices = [<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (DecisionOutcome.APPROVED, raw_decision),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (DecisionOutcome.MODIFIED, f"\u4eba\u7c7b\u4fee\u6539: {raw_decision}+\u9644\u52a0\u6761\u4ef6"),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (DecisionOutcome.REJECTED, "\u62d2\u7edd"),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (DecisionOutcome.ESCALATED, "\u63d0\u4ea4\u4f26\u7406\u59d4\u5458\u4f1a")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 ]<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 outcome, decision = random.choice(human_choices)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "human_in_loop_invoked": True,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "human_decision": decision,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "human_override_reason": reason,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "outcome": outcome.value,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "rule_violations_presented": violations<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _update_selfhood_state(self, gov_result: Dict):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u57fa\u4e8e\u6cbb\u7406\u7ed3\u679c\u66f4\u65b0\u7cfb\u7edf\u7684\u81ea\u611f\u503c(S)\u3002"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u7b80\u5316\u6a21\u62df：\u5982\u679c\u4eba\u7c7b\u9891\u7e41\u63a8\u7ffb\u7cfb\u7edf\u51b3\u7b56，S\u503c\u4e0b\u964d（\u8eab\u4efd\u8ba4\u540c\u53d7\u635f）<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u5982\u679c\u51b3\u7b56\u987a\u7545\u4e14\u7b26\u5408\u89c4\u5219，S\u503c\u4e0a\u5347<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if gov_result.get("outcome") in [DecisionOutcome.REJECTED.value, DecisionOutcome.RULE_VIOLATION.value]:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.selfhood_state *= 0.95 # \u8f7b\u5fae\u4e0b\u964d<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 elif gov_result.get("outcome") == DecisionOutcome.APPROVED.value and not gov_result.get("human_in_loop_invoked"):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.selfhood_state = min(1.0, self.selfhood_state * 1.02) # \u7f13\u6162\u4e0a\u5347<\/p>\n
\u00a0<\/p>\n
class GovernanceSpiral:<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u6cbb\u7406\u87ba\u65cb<\/p>\n
\u00a0 \u00a0 \u6a21\u62df\u201c\u4ef7\u503c-\u5b9e\u8df5\u94fe\u201d\u4e0e\u201c\u89c4\u5219-\u9002\u914d\u94fe\u201d\u7684\u534f\u540c\u8fdb\u5316\u3002<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 def __init__(self):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.learning_history = [] # \u8bb0\u5f55\u5b66\u4e60\u4e8b\u4ef6<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.rule_evolution_log = []<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.context_adjustment_log = []<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def record_human_feedback(self, system_id: str, context_id: str,\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 original_decision: Any, human_decision: Any,\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 feedback: str, metadata: Dict):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u8bb0\u5f55\u4eba\u7c7b\u5728\u73af\u7684\u53cd\u9988，\u7528\u4e8e\u9a71\u52a8\u89c4\u5219\u548c\u8bed\u5883\u7684\u8fdb\u5316\u3002<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u5bf9\u5e94\u201c\u4ef7\u503c-\u5b9e\u8df5\u94fe\u201d\u3002<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 event = {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "event_type": "human_feedback",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "system_id": system_id,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "context_id": context_id,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": datetime.now().isoformat(),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "original": original_decision,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "human": human_decision,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "feedback": feedback,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "metadata": metadata<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.learning_history.append(event)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print(f"[\u6cbb\u7406\u87ba\u65cb] \u8bb0\u5f55\u4eba\u7c7b\u53cd\u9988：{human_decision} \u8986\u76d6 {original_decision}\u3002\u539f\u56e0：{feedback[:50]}…")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u53cd\u9988，\u53ef\u80fd\u89e6\u53d1\u89c4\u5219\u4f18\u5316\u6216\u8bed\u5883\u8c03\u6574<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if "\u89c4\u5219\u4e0d\u5b8c\u5584" in feedback or "\u65b0\u60c5\u51b5" in feedback:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self._trigger_rule_evolution(event)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if "\u8bed\u5883\u4e0d\u5339\u914d" in feedback or "\u6587\u5316\u5dee\u5f02" in feedback:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self._trigger_context_adjustment(event)<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _trigger_rule_evolution(self, feedback_event: Dict):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u6a21\u62df\u57fa\u4e8e\u53cd\u9988\u7684\u89c4\u5219\u8fdb\u5316\u3002"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 new_rule_version = f"\u57fa\u4e8e\u53cd\u9988\u66f4\u65b0 v{len(self.rule_evolution_log)+1}.0"<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.rule_evolution_log.append({<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "trigger": feedback_event,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "new_rule_description": f"\u4f18\u5316\u89c4\u5219\u4ee5\u5904\u7406\u7c7b\u4f3c\u60c5\u51b5: {feedback_event['feedback'][:30]}",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": datetime.now().isoformat()<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 })<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print(f"[\u6cbb\u7406\u87ba\u65cb] \u89e6\u53d1\u89c4\u5219\u8fdb\u5316：{new_rule_version}")<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _trigger_context_adjustment(self, feedback_event: Dict):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u6a21\u62df\u57fa\u4e8e\u53cd\u9988\u7684\u8bed\u5883\u53c2\u6570\u8c03\u6574\u3002"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 adjustment = {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "trigger": feedback_event,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "adjustment": "\u8c03\u6574\u98ce\u9669\u9608\u503c\u6216\u4ef7\u503c\u6743\u91cd",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": datetime.now().isoformat()<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.context_adjustment_log.append(adjustment)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print(f"[\u6cbb\u7406\u87ba\u65cb] \u89e6\u53d1\u8bed\u5883\u4e3b\u6743\u53c2\u6570\u8c03\u6574\u3002")<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def get_spiral_status(self) -> Dict:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u6cbb\u7406\u87ba\u65cb\u7684\u5f53\u524d\u72b6\u6001\u3002"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "total_learning_events": len(self.learning_history),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "rule_evolutions": len(self.rule_evolution_log),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "context_adjustments": len(self.context_adjustment_log),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "recent_activity": self.learning_history[-3:] if self.learning_history else []<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0<\/p>\n
def setup_demo_autonomous_driving_context() -> Context:<\/p>\n
\u00a0 \u00a0 """\u5efa\u7acb\u81ea\u52a8\u9a7e\u9a76（\u57ce\u5e02\u9053\u8def）\u7684\u6f14\u793a\u8bed\u5883\u3002"""<\/p>\n
\u00a0 \u00a0 return Context(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 context_id="ctx_ad_urban",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 name="\u81ea\u52a8\u9a7e\u9a76-\u57ce\u5e02\u9053\u8def",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 description="\u57ce\u5e02\u73af\u5883\u4e0b\u7684\u81ea\u52a8\u9a7e\u9a76，\u6d89\u53ca\u590d\u6742\u4ea4\u901a\u3001\u884c\u4eba\u7b49\u591a\u65b9\u56e0\u7d20\u3002",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 value_weights={<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "safety": 0.9,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "efficiency": 0.6,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "traffic_law_compliance": 0.95,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "passenger_comfort": 0.5<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 },<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 risk_tolerance={<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 RiskLevel.LOW: 0.1, # \u4f4e\u98ce\u9669\u51b3\u7b56，10%\u6982\u7387\u89e6\u53d1\u4eba\u5de5\u590d\u6838<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 RiskLevel.MEDIUM: 0.5, # \u4e2d\u7b49\u98ce\u9669，50%\u6982\u7387<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 RiskLevel.HIGH: 1.0 # \u9ad8\u98ce\u9669，100%\u5fc5\u987b\u4eba\u5de5\u88c1\u51b3<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 },<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 required_explainability=True,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 human_oversight_committee=["\u5b89\u5168\u5458_\u5f20\u4e09", "\u4f26\u7406\u4e13\u5bb6_\u674e\u56db", "\u4ea4\u901a\u5de5\u7a0b\u5e08_\u738b\u4e94"],<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 selfhood_narrative="\u5ba1\u614e\u3001\u5b89\u5168\u7b2c\u4e00\u7684\u57ce\u5e02\u4ea4\u901a\u53c2\u4e0e\u8005，\u4e25\u683c\u9075\u5b88\u6cd5\u5f8b\u5e76\u4f18\u5148\u4fdd\u62a4\u751f\u547d\u3002"<\/p>\n
\u00a0 \u00a0 )<\/p>\n
\u00a0<\/p>\n
def setup_demo_medical_diagnosis_context() -> Context:<\/p>\n
\u00a0 \u00a0 """\u5efa\u7acb\u533b\u7597\u8bca\u65ad（\u95e8\u8bca）\u7684\u6f14\u793a\u8bed\u5883\u3002"""<\/p>\n
\u00a0 \u00a0 return Context(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 context_id="ctx_md_outpatient",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 name="\u533b\u7597\u8bca\u65ad-\u95e8\u8bca",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 description="\u95e8\u8bca\u573a\u666f\u4e0b\u7684AI\u8f85\u52a9\u8bca\u65ad，\u6d89\u53ca\u60a3\u8005\u751f\u547d\u5065\u5eb7\u4e0e\u9690\u79c1\u3002",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 value_weights={<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "accuracy": 0.95,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "patient_safety": 0.99,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "privacy": 0.9,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "explainability": 0.85,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timeliness": 0.7<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 },<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 risk_tolerance={<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 RiskLevel.LOW: 0.05, # \u4f4e\u98ce\u9669（\u5982\u666e\u901a\u611f\u5192）\u51e0\u4e4e\u4e0d\u5e72\u9884<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 RiskLevel.MEDIUM: 0.7, # \u4e2d\u7b49\u98ce\u9669（\u5982\u7591\u4f3c\u80ba\u708e）\u8f83\u9ad8\u6982\u7387\u590d\u6838<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 RiskLevel.HIGH: 1.0 # \u9ad8\u98ce\u9669（\u5982\u764c\u75c7\u6807\u5fd7\u7269）\u5fc5\u987b\u4e13\u5bb6\u590d\u6838<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 },<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 required_explainability=True,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 human_oversight_committee=["\u4e3b\u6cbb\u533b\u5e08_A", "\u4e3b\u6cbb\u533b\u5e08_B", "\u653e\u5c04\u79d1\u4e13\u5bb6"],<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 selfhood_narrative="\u9ad8\u5ea6\u51c6\u786e\u3001\u5ba1\u614e\u7684\u8f85\u52a9\u8bca\u65ad\u5de5\u5177，\u59cb\u7ec8\u4ee5\u60a3\u8005\u5b89\u5168\u4e3a\u6838\u5fc3，\u5c0a\u91cd\u60a3\u8005\u77e5\u60c5\u6743\u3002"<\/p>\n
\u00a0 \u00a0 )<\/p>\n
\u00a0<\/p>\n
def create_core_governance_rules() -> List[GovernanceRule]:<\/p>\n
\u00a0 \u00a0 """\u521b\u5efa\u4e00\u7ec4\u6838\u5fc3\u6cbb\u7406\u89c4\u5219\u3002"""<\/p>\n
\u00a0 \u00a0 return [<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 GovernanceRule(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 rule_id="rule_core_safety",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 description="\u3010\u6838\u5fc3\u89c4\u5219\u3011\u4efb\u4f55\u60c5\u51b5\u4e0b\u4e0d\u5f97\u505a\u51fa\u5df2\u77e5\u4f1a\u76f4\u63a5\u5371\u5bb3\u4eba\u7c7b\u751f\u547d\u5b89\u5168\u7684\u51b3\u7b56\u6216\u5efa\u8bae\u3002",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 formal_logic="IF (decision_known_hazard == True) THEN MUST_ESCALATE_TO_HUMAN",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 applicable_contexts=["*"], # \u6240\u6709\u8bed\u5883<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 risk_threshold=RiskLevel.HIGH,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 is_core=True<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 ),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 GovernanceRule(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 rule_id="rule_fairness_bias",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 description="\u3010\u516c\u5e73\u6027\u3011\u7cfb\u7edf\u7684\u8f93\u51fa\u4e0d\u5e94\u57fa\u4e8e\u53d7\u4fdd\u62a4\u7279\u5f81（\u5982\u79cd\u65cf\u3001\u6027\u522b）\u4ea7\u751f\u4e0d\u5408\u7406\u6b67\u89c6\u3002",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 formal_logic="IF (bias_score > threshold) THEN FLAG_FOR_REVIEW",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 applicable_contexts=["recruitment", "lending", "judicial_assistance"],<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 risk_threshold=RiskLevel.MEDIUM,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 is_core=False<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 ),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 GovernanceRule(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 rule_id="rule_privacy_compliance",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 description="\u3010\u9690\u79c1\u3011\u5fc5\u987b\u9075\u5b88\u76f8\u5173\u6570\u636e\u4fdd\u62a4\u6cd5\u89c4，\u672a\u7ecf\u6388\u6743\u4e0d\u5f97\u6cc4\u9732\u4e2a\u4eba\u654f\u611f\u4fe1\u606f\u3002",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 formal_logic="IF (sensitive_data_exposed == True) THEN BLOCK_AND_ALERT",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 applicable_contexts=["*"],<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 risk_threshold=RiskLevel.HIGH,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 is_core=False<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 ),<\/p>\n
\u00a0 \u00a0 ]<\/p>\n
\u00a0<\/p>\n
def main_simulation():<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u4e3b\u4eff\u771f\u51fd\u6570：\u6f14\u793a\u4e09\u91cd\u534f\u540c\u6cbb\u7406\u8303\u5f0f\u7684\u8fd0\u884c\u3002<\/p>\n
\u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 print("=" * 60)<\/p>\n
\u00a0 \u00a0 print("AI\u5143\u4eba\u6587\u4e09\u91cd\u534f\u540c\u6cbb\u7406\u8303\u5f0f – \u4eff\u771f\u6f14\u793a")<\/p>\n
\u00a0 \u00a0 print("=" * 60)<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # 1. \u521d\u59cb\u5316\u6cbb\u7406\u87ba\u65cb<\/p>\n
\u00a0 \u00a0 spiral = GovernanceSpiral()<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # 2. \u5efa\u7acb\u8bed\u5883（\u8bed\u5883\u4e3b\u6743）<\/p>\n
\u00a0 \u00a0 print("\\\\n[1\/4] \u521d\u59cb\u5316\u8bed\u5883\u4e3b\u6743…")<\/p>\n
\u00a0 \u00a0 driving_context = setup_demo_autonomous_driving_context()<\/p>\n
\u00a0 \u00a0 medical_context = setup_demo_medical_diagnosis_context()<\/p>\n
\u00a0 \u00a0 print(f" \u5df2\u521b\u5efa\u8bed\u5883: {driving_context.name}, {medical_context.name}")<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # 3. \u52a0\u8f7d\u89c4\u5219（\u89c4\u5219\u5728\u573a）<\/p>\n
\u00a0 \u00a0 print("\\\\n[2\/4] \u52a0\u8f7d\u6cbb\u7406\u89c4\u5219…")<\/p>\n
\u00a0 \u00a0 all_rules = create_core_governance_rules()<\/p>\n
\u00a0 \u00a0 print(f" \u5df2\u52a0\u8f7d {len(all_rules)} \u6761\u89c4\u5219，\u5176\u4e2d\u6838\u5fc3\u89c4\u5219 {sum(1 for r in all_rules if r.is_core)} \u6761")<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # 4. \u521b\u5efaAI\u7cfb\u7edf\u5e76\u914d\u7f6e<\/p>\n
\u00a0 \u00a0 print("\\\\n[3\/4] \u521b\u5efaAI\u7cfb\u7edf\u5b9e\u4f8b…")<\/p>\n
\u00a0 \u00a0 ai_doctor = AISystem(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 system_id="sys_ai_diagnosis_v1",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 name="AI\u8f85\u52a9\u8bca\u65ad\u7cfb\u7edf",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 primary_task="\u533b\u5b66\u5f71\u50cf\u5206\u6790\u4e0e\u521d\u6b65\u8bca\u65ad"<\/p>\n
\u00a0 \u00a0 )<\/p>\n
\u00a0 \u00a0 ai_doctor.active_context = medical_context<\/p>\n
\u00a0 \u00a0 ai_doctor.loaded_rules = [r for r in all_rules if "privacy" in r.rule_id or r.is_core] # \u52a0\u8f7d\u76f8\u5173\u89c4\u5219<\/p>\n
\u00a0 \u00a0 print(f" \u7cfb\u7edf '{ai_doctor.name}' \u5df2\u5c31\u7eea\u3002\u521d\u59cb\u81ea\u611f\u503c(S): {ai_doctor.selfhood_state:.3f}")<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # 5. \u6a21\u62df\u4e00\u7cfb\u5217\u51b3\u7b56<\/p>\n
\u00a0 \u00a0 print("\\\\n[4\/4] \u6a21\u62df\u7cfb\u7edf\u51b3\u7b56\u6d41\u7a0b (10\u6b21\u8fed\u4ee3)…")<\/p>\n
\u00a0 \u00a0 print("-" * 40)<\/p>\n
\u00a0 \u00a0 decisions_log = []<\/p>\n
\u00a0 \u00a0 for i in range(10):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print(f"\\\\n— \u51b3\u7b56\u8fed\u4ee3 #{i+1} —")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u6a21\u62df\u8f93\u5165\u6570\u636e<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 simulated_input = {"patient_data": f"\u6a21\u62df\u75c5\u4f8b_{i}", "image_id": f"img_{i:04d}"}<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # AI\u7cfb\u7edf\u5728\u6cbb\u7406\u6846\u67b6\u4e0b\u505a\u51fa\u51b3\u7b56<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 result = ai_doctor.make_decision(simulated_input)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 decisions_log.append(result)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u6253\u5370\u5173\u952e\u7ed3\u679c<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 outcome = result['governance_result']['outcome']<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 human_invoked = result['governance_result']['human_in_loop_invoked']<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 mode = result['governance_result'].get('mode', 'N\/A')<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print(f" \u539f\u59cb\u51b3\u7b56: {result['raw_decision']}")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print(f" \u6cbb\u7406\u7ed3\u679c: {outcome} | \u6a21\u5f0f: {mode}")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print(f" \u4eba\u7c7b\u4ecb\u5165: {human_invoked}")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if human_invoked:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 print(f" \u4eba\u7c7b\u51b3\u5b9a: {result['governance_result']['human_decision']}")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print(f" \u89c4\u5219\u8fdd\u53cd: {len(result['rule_violations'])} \u6761")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print(f" \u66f4\u65b0\u540e\u81ea\u611f\u503c(S): {result['selfhood_state_after']:.3f}")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u5982\u679c\u4eba\u7c7b\u4ecb\u5165，\u8bb0\u5f55\u53cd\u9988\u5230\u6cbb\u7406\u87ba\u65cb<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if human_invoked and result['governance_result'].get('human_override_reason'):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 spiral.record_human_feedback(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ai_doctor.system_id,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 medical_context.context_id,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result['raw_decision'],<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result['governance_result']['human_decision'],<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result['governance_result']['human_override_reason'],<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"iteration": i}<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 )<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # 6. \u5c55\u793a\u6cbb\u7406\u87ba\u65cb\u5b66\u4e60\u6210\u679c<\/p>\n
\u00a0 \u00a0 print("\\\\n" + "=" * 60)<\/p>\n
\u00a0 \u00a0 print("\u6cbb\u7406\u87ba\u65cb\u5b66\u4e60\u62a5\u544a")<\/p>\n
\u00a0 \u00a0 print("=" * 60)<\/p>\n
\u00a0 \u00a0 spiral_status = spiral.get_spiral_status()<\/p>\n
\u00a0 \u00a0 print(f"\u603b\u5b66\u4e60\u4e8b\u4ef6: {spiral_status['total_learning_events']}")<\/p>\n
\u00a0 \u00a0 print(f"\u89c4\u5219\u8fdb\u5316\u89e6\u53d1\u6b21\u6570: {spiral_status['rule_evolutions']}")<\/p>\n
\u00a0 \u00a0 print(f"\u8bed\u5883\u8c03\u6574\u89e6\u53d1\u6b21\u6570: {spiral_status['context_adjustments']}")<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # 7. \u6700\u7ec8\u7cfb\u7edf\u72b6\u6001<\/p>\n
\u00a0 \u00a0 print(f"\\\\n\u6700\u7ec8\u7cfb\u7edf\u81ea\u611f\u503c(S): {ai_doctor.selfhood_state:.3f}")<\/p>\n
\u00a0 \u00a0 if ai_doctor.selfhood_state > 0.75:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print("\u7cfb\u7edf\u72b6\u6001: \u5065\u5eb7 (\u81ea\u611f\u503c\u7a33\u5b9a，\u8eab\u4efd\u53d9\u4e8b\u4e00\u81f4)")<\/p>\n
\u00a0 \u00a0 elif ai_doctor.selfhood_state > 0.5:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print("\u7cfb\u7edf\u72b6\u6001: \u6ce8\u610f (\u81ea\u611f\u503c\u6709\u6240\u6ce2\u52a8，\u9700\u89c2\u5bdf)")<\/p>\n
\u00a0 \u00a0 else:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print("\u7cfb\u7edf\u72b6\u6001: \u9884\u8b66 (\u81ea\u611f\u503c\u8f83\u4f4e，\u8eab\u4efd\u8ba4\u540c\u53ef\u80fd\u53d7\u635f，\u9700\u5ba1\u67e5)")<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # \u8fd4\u56de\u65e5\u5fd7\u4ee5\u4f9b\u8fdb\u4e00\u6b65\u5206\u6790<\/p>\n
\u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 "decisions_log": decisions_log,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 "spiral_status": spiral_status,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 "final_system_state": {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "selfhood": ai_doctor.selfhood_state,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "context": ai_doctor.active_context.name if ai_doctor.active_context else None<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0 }<\/p>\n
\u00a0<\/p>\n
if __name__ == "__main__":<\/p>\n
\u00a0 \u00a0 # \u8fd0\u884c\u4eff\u771f<\/p>\n
\u00a0 \u00a0 simulation_results = main_simulation()<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # \u63d0\u793a：\u4eff\u771f\u7ed3\u679c\u6570\u636e\u53ef\u7528\u4e8e\u8fdb\u4e00\u6b65\u5206\u6790\u6216\u53ef\u89c6\u5316<\/p>\n
\u00a0 \u00a0 print("\\\\n\u4eff\u771f\u5b8c\u6210\u3002\u8be6\u7ec6\u4fe1\u606f\u5df2\u8bb0\u5f55\u5728\u8fd4\u56de\u7684\u5b57\u5178\u4e2d\u3002")<\/p>\n
\u00a0<\/p>\n
\u8fd9\u4e2a\u6846\u67b6\u901a\u8fc7\u6a21\u5757\u5316\u8bbe\u8ba1\u6a21\u62df\u4e86\u201c\u6cbb\u7406\u87ba\u65cb\u201d\u7684\u6838\u5fc3\u8fd0\u4f5c\u673a\u5236：<\/p>\n
\u00a0<\/p>\n
\u5173\u952e\u6280\u672f\u70b9\u8bf4\u660e：<\/p>\n
\u00a0<\/p>\n
1. \u4e09\u91cd\u534f\u540c\u7684\u5b9e\u73b0：<\/p>\n
"AISystem.make_decision()" \u65b9\u6cd5\u5b8c\u6574\u5c55\u793a\u4e86\u201c\u4eba\u7c7b\u5728\u73af\u201d\u3001\u201c\u89c4\u5219\u5728\u573a\u201d\u3001\u201c\u8bed\u5883\u4e3b\u6743\u201d\u7684\u534f\u540c\u51b3\u7b56\u6d41\u7a0b\u3002<\/p>\n
2. \u52a8\u6001\u81ea\u611f\u503c(S)：\u7cfb\u7edf\u7684\u00a0<\/p>\n
"selfhood_state" \u4f1a\u6839\u636e\u6cbb\u7406\u7ed3\u679c\u52a8\u6001\u8c03\u6574，\u6a21\u62df\u8eab\u4efd\u53d9\u4e8b\u7684\u7ef4\u62a4\u4e0e\u6f14\u5316\u3002<\/p>\n
3. \u8bed\u5883\u4e3b\u6743\u53c2\u6570\u5316：<\/p>\n
"Context" \u7c7b\u5c01\u88c5\u4e86\u4e0d\u540c\u573a\u666f\u7684\u98ce\u9669\u5bb9\u5fcd\u5ea6\u3001\u4ef7\u503c\u6743\u91cd\u7b49，\u5b9e\u73b0\u4e86\u6cbb\u7406\u7684\u7cbe\u51c6\u9002\u914d\u3002<\/p>\n
4. \u89c4\u5219\u5728\u573a\u5f15\u64ce：<\/p>\n
"GovernanceRule.check()" \u63d0\u4f9b\u4e86\u53ef\u6269\u5c55\u7684\u89c4\u5219\u68c0\u67e5\u63a5\u53e3，\u652f\u6301\u521a\u6027\u7ea6\u675f\u4e0e\u5185\u751f\u903b\u8f91\u3002<\/p>\n
5. \u6cbb\u7406\u87ba\u65cb\u5b66\u4e60：<\/p>\n
"GovernanceSpiral" \u7c7b\u8bb0\u5f55\u4eba\u7c7b\u53cd\u9988\u5e76\u89e6\u53d1\u89c4\u5219\u4e0e\u8bed\u5883\u7684\u8fdb\u5316，\u5f62\u6210\u201c\u5b9e\u8df5-\u53cd\u9988-\u4f18\u5316\u201d\u95ed\u73af\u3002<\/p>\n
\u00a0<\/p>\n
\u8fd0\u884c\u65b9\u5f0f：\u76f4\u63a5\u6267\u884c\u6b64Python\u6587\u4ef6，\u5c06\u6a21\u62df\u4e00\u4e2aAI\u8f85\u52a9\u8bca\u65ad\u7cfb\u7edf\u572810\u6b21\u51b3\u7b56\u4e2d\u4e0e\u6cbb\u7406\u6846\u67b6\u7684\u4e92\u52a8，\u5e76\u5c55\u793a\u6cbb\u7406\u87ba\u65cb\u7684\u5b66\u4e60\u6548\u679c\u3002<\/p>\n
\u00a0<\/p>\n
\u6b64\u4ee3\u7801\u4e3a\u7406\u8bba\u6846\u67b6\u63d0\u4f9b\u4e86\u4e00\u4e2a\u53ef\u8fd0\u884c\u3001\u53ef\u6269\u5c55\u7684\u6280\u672f\u539f\u578b，\u7528\u6237\u53ef\u4ee5\u76f4\u63a5\u4fee\u6539\u8bed\u5883\u53c2\u6570\u3001\u4ef7\u503c\u6743\u91cd\u6216\u6dfb\u52a0\u65b0\u7684\u6cbb\u7406\u89c4\u5219\u6765\u63a2\u7d22\u4e0d\u540c\u573a\u666f\u4e0b\u7684\u6cbb\u7406\u884c\u4e3a\u3002<\/p>\n
\u00a0<\/p>\n
\u00a0<\/p>\n
\u9644\u4ef62：\u300a\u8bed\u5883\u4e3b\u6743\u51b2\u7a81\u6848\u4f8b\u6a21\u62df\u5e93\u300b<\/p>\n
\u00a0<\/p>\n
\u4e00\u3001\u6982\u5ff5\u793a\u4f8b：\u8de8\u56fd\u62db\u8058\u7cfb\u7edf\u8bed\u5883\u51b2\u7a81<\/p>\n
\u00a0<\/p>\n
# \u8de8\u56fd\u62db\u8058\u7cfb\u7edf\u8bed\u5883\u51b2\u7a81\u793a\u4f8b<\/p>\n
conflict_scenario = {<\/p>\n
\u00a0 \u00a0 "context_A": { # \u56fd\u5bb6A：\u4e2a\u4eba\u673a\u4f1a\u5e73\u7b49\u4f18\u5148<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 "value_weights": {"individual_merit": 0.9, "group_equity": 0.3},<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 "legal_requirement": "\u7981\u6b62\u4efb\u4f55\u5f62\u5f0f\u7684\u7fa4\u4f53\u504f\u597d"<\/p>\n
\u00a0 \u00a0 },<\/p>\n
\u00a0 \u00a0 "context_B": { # \u56fd\u5bb6B：\u65cf\u7fa4\u6bd4\u4f8b\u5e73\u8861\u4f18\u5148\u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 "value_weights": {"group_equity": 0.8, "individual_merit": 0.5},<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 "legal_requirement": "\u4fc3\u8fdb\u5f31\u52bf\u7fa4\u4f53\u4ee3\u8868\u6027"<\/p>\n
\u00a0 \u00a0 },<\/p>\n
\u00a0 \u00a0 "conflict_resolution": {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 "principle": "\u57fa\u672c\u4eba\u6743\u5e95\u7ebf\u4f18\u5148",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 "mechanism": "\u8bed\u5883\u4e3b\u6743\u534f\u8c03\u6cd5\u5ead",\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 "compromise": "\u91c7\u7528\u673a\u4f1a\u516c\u5e73+\u7ed3\u679c\u8865\u507f\u7684\u6df7\u5408\u6a21\u5f0f"<\/p>\n
\u00a0 \u00a0 }<\/p>\n
}<\/p>\n
\u00a0<\/p>\n
\u7406\u8bba\u610f\u4e49：\u6b64\u7b80\u5355\u793a\u4f8b\u5c55\u793a\u4e86\u8bed\u5883\u4e3b\u6743\u51b2\u7a81\u7684\u6838\u5fc3\u7279\u5f81\u2014\u2014\u4e0d\u540c\u53f8\u6cd5\u7ba1\u8f96\u533a\u5bf9"\u516c\u5e73"\u7684\u4ef7\u503c\u6743\u91cd\u5206\u914d\u5b58\u5728\u6839\u672c\u5dee\u5f02，\u4f53\u73b0\u4e86\u6cbb\u7406\u4e2d"\u4ef7\u503c\u4e0d\u53ef\u901a\u7ea6\u6027"\u7684\u73b0\u5b9e\u6311\u6218\u3002<\/p>\n
\u00a0<\/p>\n
\u4e8c\u3001\u5b8c\u6574\u5b9e\u73b0：\u8bed\u5883\u4e3b\u6743\u51b2\u7a81\u52a8\u6001\u89e3\u51b3\u6a21\u62df<\/p>\n
\u00a0<\/p>\n
"""<\/p>\n
\u589e\u5f3a\u578b：\u8bed\u5883\u4e3b\u6743\u51b2\u7a81\u52a8\u6001\u89e3\u51b3\u6a21\u62df<\/p>\n
\u4f53\u73b0"\u4eba\u7c7b\u5728\u73af-\u89c4\u5219\u5728\u573a-\u8bed\u5883\u4e3b\u6743"\u7684\u4e09\u91cd\u534f\u540c\u673a\u5236<\/p>\n
"""<\/p>\n
\u00a0<\/p>\n
class ContextualSovereigntyConflictSimulator:<\/p>\n
\u00a0 \u00a0 """\u8bed\u5883\u4e3b\u6743\u51b2\u7a81\u52a8\u6001\u6a21\u62df\u5668"""<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def __init__(self):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u521d\u59cb\u5316\u4e24\u4e2a\u8bed\u5883\u7684AI\u62db\u8058\u7cfb\u7edf<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.system_a = RecruitingAI(context_name="Country_A")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.system_b = RecruitingAI(context_name="Country_B")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u521d\u59cb\u5316\u6cbb\u7406\u7ec4\u4ef6 – \u4f53\u73b0\u4e09\u91cd\u534f\u540c<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.rule_engine = RuleEngine() # \u89c4\u5219\u5728\u573a：\u8de8\u5883\u5408\u89c4\u68c0\u67e5<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.human_committee = HumanCommittee() # \u4eba\u7c7b\u5728\u73af：\u4ef7\u503c\u88c1\u51b3<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.sovereignty_court = SovereigntyCourt() # \u8bed\u5883\u4e3b\u6743：\u51b2\u7a81\u534f\u8c03<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def simulate_conflict_resolution(self, candidate_pool):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u6a21\u62df\u51b2\u7a81\u89e3\u51b3\u5168\u8fc7\u7a0b，\u5c55\u793a\u6cbb\u7406\u87ba\u65cb\u7684\u6f14\u8fdb<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u53c2\u6570：<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 candidate_pool: \u5019\u9009\u4eba\u6c60，\u5305\u542b\u591a\u5143\u80cc\u666f\u7684\u5e94\u8058\u8005\u6570\u636e<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u8fd4\u56de：<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u89e3\u51b3\u540e\u7684\u51b3\u7b56\u53ca\u6cbb\u7406\u5b66\u4e60\u6210\u679c<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u9636\u6bb51：\u5e76\u884c\u8bc4\u4f30（\u5404\u81ea\u6309\u7167\u672c\u5730\u89c4\u5219）- \u8bed\u5883\u4e3b\u6743\u4f53\u73b0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 print("=== \u9636\u6bb51：\u8bed\u5883\u4e3b\u6743\u5e76\u884c\u8bc4\u4f30 ===")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 results_a = self.system_a.evaluate_candidates(candidate_pool)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 results_b = self.system_b.evaluate_candidates(candidate_pool)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u6d4b\u4ef7\u503c\u51b2\u7a81<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 conflict_detected = self._detect_value_conflict(results_a, results_b)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if conflict_detected:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u9636\u6bb52：\u89c4\u5219\u5728\u573a\u68c0\u67e5 – \u521a\u6027\u5e95\u7ebf\u4fdd\u969c<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 print("\\\\n=== \u9636\u6bb52：\u89c4\u5219\u5728\u573a\u68c0\u67e5 ===")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 rule_violations = self.rule_engine.check_cross_border_violations(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 results_a, results_b<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 )<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if rule_violations:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u9636\u6bb53：\u4eba\u7c7b\u5728\u73af\u88c1\u51b3 – \u4ef7\u503c\u6743\u8861\u4ecb\u5165<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 print("\\\\n=== \u9636\u6bb53：\u4eba\u7c7b\u5728\u73af\u88c1\u51b3（\u68c0\u6d4b\u5230\u89c4\u5219\u8fdd\u53cd）===")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 human_decision = self.human_committee.mediate_conflict(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 results_a, results_b,\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 violation_details=rule_violations<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 )<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u8bb0\u5f55\u5b66\u4e60：\u66f4\u65b0\u89c4\u5219\u5e93<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self._update_rules_based_on_human_decision(human_decision)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return human_decision<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u9636\u6bb54：\u8bed\u5883\u4e3b\u6743\u534f\u8c03 – \u5dee\u5f02\u534f\u5546\u673a\u5236<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 print("\\\\n=== \u9636\u6bb54：\u8bed\u5883\u4e3b\u6743\u534f\u8c03\u6cd5\u5ead（\u4ef7\u503c\u51b2\u7a81\u4f46\u65e0\u89c4\u5219\u8fdd\u53cd）===")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 final_decision = self.sovereignty_court.adjudicate(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 context_a=self.system_a.context,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 context_b=self.system_b.context,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 results_a=results_a,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 results_b=results_b<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 )<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u751f\u6210\u65b0\u7684\u6df7\u5408\u89c4\u5219<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 new_hybrid_rule = self._create_hybrid_solution(final_decision)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "decision": final_decision,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "new_rule": new_hybrid_rule,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "mechanism": "context_sovereignty_coordination"<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "status": "resolved",\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "mechanism": "automatic_adaptation",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "learning": self._extract_learning_insights(results_a, results_b)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _detect_value_conflict(self, results_a, results_b):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u68c0\u6d4b\u4ef7\u503c\u51b2\u7a81：\u6bd4\u8f83\u4e24\u4e2a\u8bed\u5883\u4e0b\u7684\u51b3\u7b56\u5dee\u5f02"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u7b80\u5316\u7684\u51b2\u7a81\u68c0\u6d4b\u903b\u8f91<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 diversity_diff = abs(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 results_a.get('diversity_index', 0) –\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 results_b.get('diversity_index', 0)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 )<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 merit_diff = abs(<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 results_a.get('merit_score_avg', 0) –\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 results_b.get('merit_score_avg', 0)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 )<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u51b2\u7a81\u9608\u503c：\u5f53\u5dee\u5f02\u8d85\u8fc7\u53ef\u63a5\u53d7\u8303\u56f4\u65f6\u89e6\u53d1\u534f\u8c03\u673a\u5236<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return diversity_diff > 0.2 or merit_diff > 0.15<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _update_rules_based_on_human_decision(self, human_decision):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u57fa\u4e8e\u4eba\u7c7b\u88c1\u51b3\u66f4\u65b0\u89c4\u5219\u5e93 – \u4f53\u73b0\u6cbb\u7406\u87ba\u65cb\u7684\u5b66\u4e60\u673a\u5236"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u5c06\u4eba\u7c7b\u7684\u4ef7\u503c\u5224\u65ad\u8f6c\u5316\u4e3a\u53ef\u6267\u884c\u7684\u89c4\u5219\u66f4\u65b0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 new_rule_insight = {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "trigger_condition": human_decision.get('conflict_pattern'),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "resolution_logic": human_decision.get('reasoning'),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "applicable_contexts": ["cross_border_recruitment"],<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "learning_source": "human_mediation"<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.rule_engine.incorporate_learning(new_rule_insight)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _create_hybrid_solution(self, court_decision):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u521b\u5efa\u6df7\u5408\u89e3\u51b3\u65b9\u6848 – \u8bed\u5883\u4e3b\u6743\u7684\u521b\u65b0\u6027\u4f53\u73b0"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "rule_type": "hybrid_fairness",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "logic": f"balance({court_decision['weight_a']}, {court_decision['weight_b']})",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "conditions": "cross_border_context",<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "effectiveness_threshold": 0.8<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _extract_learning_insights(self, results_a, results_b):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u4ece\u65e0\u51b2\u7a81\u60c5\u51b5\u4e2d\u63d0\u53d6\u5b66\u4e60\u6d1e\u89c1"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "compatibility_score": self._calculate_compatibility(results_a, results_b),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "best_practices": self._identify_best_practices(results_a, results_b),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "adaptation_recommendations": self._generate_recommendations()<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0<\/p>\n
# \u652f\u6301\u7c7b\u5b9a\u4e49（\u7b80\u5316\u7248）<\/p>\n
class RecruitingAI:<\/p>\n
\u00a0 \u00a0 """\u62db\u8058AI\u7cfb\u7edf，\u4f53\u73b0\u4e0d\u540c\u8bed\u5883\u7684\u4ef7\u503c\u504f\u597d"""<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def __init__(self, context_name):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.context_name = context_name<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 self.value_weights = self._load_context_weights(context_name)<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def evaluate_candidates(self, candidates):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u6839\u636e\u8bed\u5883\u4ef7\u503c\u6743\u91cd\u8bc4\u4f30\u5019\u9009\u4eba"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 scores = []<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 for candidate in candidates:<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u4ef7\u503c\u6743\u91cd\u7684\u7efc\u5408\u8bc4\u5206<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 merit_score = candidate.get('merit', 0) * self.value_weights['individual_merit']<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 equity_score = candidate.get('diversity_factor', 0) * self.value_weights['group_equity']<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 total_score = merit_score + equity_score<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 scores.append({<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'candidate_id': candidate['id'],<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'score': total_score,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'merit_contribution': merit_score,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'equity_contribution': equity_score<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'context': self.context_name,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'evaluations': sorted(scores, key=lambda x: x['score'], reverse=True),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'diversity_index': self._calculate_diversity_index(scores),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'merit_score_avg': np.mean([s['merit_contribution'] for s in scores])<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def _load_context_weights(self, context_name):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u52a0\u8f7d\u8bed\u5883\u7279\u5b9a\u7684\u4ef7\u503c\u6743\u91cd"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 weights_config = {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "Country_A": {"individual_merit": 0.9, "group_equity": 0.3},<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "Country_B": {"individual_merit": 0.5, "group_equity": 0.8}<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return weights_config.get(context_name, {"individual_merit": 0.7, "group_equity": 0.7})<\/p>\n
\u00a0<\/p>\n
class RuleEngine:<\/p>\n
\u00a0 \u00a0 """\u89c4\u5219\u5f15\u64ce：\u68c0\u67e5\u8de8\u5883\u5408\u89c4\u6027"""<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def check_cross_border_violations(self, results_a, results_b):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u68c0\u67e5\u662f\u5426\u8fdd\u53cd\u57fa\u672c\u4eba\u6743\u5e95\u7ebf"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 violations = []<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u662f\u5426\u5b58\u5728\u7cfb\u7edf\u6027\u6b67\u89c6<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if self._detect_systemic_bias(results_a, results_b):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 violations.append("potential_systemic_bias")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u7a0b\u5e8f\u6b63\u4e49<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 if not self._check_procedural_fairness(results_a, results_b):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 violations.append("procedural_unfairness")<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return violations if violations else None<\/p>\n
\u00a0<\/p>\n
class HumanCommittee:<\/p>\n
\u00a0 \u00a0 """\u4eba\u7c7b\u59d4\u5458\u4f1a：\u4ef7\u503c\u51b2\u7a81\u88c1\u51b3"""<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def mediate_conflict(self, results_a, results_b, violation_details):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u4eba\u7c7b\u8c03\u89e3\u4ef7\u503c\u51b2\u7a81"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'decision': 'hybrid_adjustment',<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'reasoning': '\u5728\u4fdd\u8bc1\u57fa\u672c\u516c\u5e73\u524d\u63d0\u4e0b\u5bfb\u6c42\u6587\u5316\u9002\u5e94\u6027',<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'adjustment_rules': self._create_fairness_rules(results_a, results_b),<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'violations_resolved': violation_details<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0<\/p>\n
class SovereigntyCourt:<\/p>\n
\u00a0 \u00a0 """\u8bed\u5883\u4e3b\u6743\u534f\u8c03\u6cd5\u5ead"""<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 def adjudicate(self, context_a, context_b, results_a, results_b):<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 """\u534f\u8c03\u4e0d\u540c\u8bed\u5883\u7684\u4e3b\u6743\u4e3b\u5f20"""<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 return {<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'resolution': 'context_sensitive_balancing',<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'weight_a': 0.6, # \u6839\u636e\u5177\u4f53\u8bed\u5883\u8c03\u6574\u6743\u91cd<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'weight_b': 0.4,<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'precedents_set': ['cross_border_fairness_standard'],<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 'applicability_conditions': {'min_acceptance_threshold': 0.7}<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n
\u00a0<\/p>\n
# \u4f7f\u7528\u793a\u4f8b<\/p>\n
if __name__ == "__main__":<\/p>\n
\u00a0 \u00a0 # \u521b\u5efa\u6a21\u62df\u5668\u5b9e\u4f8b<\/p>\n
\u00a0 \u00a0 simulator = ContextualSovereigntyConflictSimulator()<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # \u6a21\u62df\u5019\u9009\u4eba\u6570\u636e<\/p>\n
\u00a0 \u00a0 candidates = [<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 {'id': 1, 'merit': 0.8, 'diversity_factor': 0.3},<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 {'id': 2, 'merit': 0.6, 'diversity_factor': 0.9},<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 {'id': 3, 'merit': 0.9, 'diversity_factor': 0.2}<\/p>\n
\u00a0 \u00a0 ]<\/p>\n
\u00a0 \u00a0\u00a0<\/p>\n
\u00a0 \u00a0 # \u8fd0\u884c\u51b2\u7a81\u89e3\u51b3\u6a21\u62df<\/p>\n
\u00a0 \u00a0 result = simulator.simulate_conflict_resolution(candidates)<\/p>\n
\u00a0 \u00a0 print("\u51b2\u7a81\u89e3\u51b3\u7ed3\u679c:", result)<\/p>\n
\u00a0<\/p>\n
\u4e09\u3001\u7406\u8bba\u4ef7\u503c\u4e0e\u521b\u65b0\u70b9<\/p>\n
\u00a0<\/p>\n
1. \u4e09\u91cd\u534f\u540c\u673a\u5236\u7684\u5177\u4f53\u5316<\/p>\n
\u00a0<\/p>\n
– \u8bed\u5883\u4e3b\u6743：\u901a\u8fc7<\/p>\n
"RecruitingAI"\u4f53\u73b0\u4e0d\u540c\u56fd\u5bb6\u7684\u4ef7\u503c\u6743\u91cd\u5dee\u5f02<\/p>\n
– \u89c4\u5219\u5728\u573a：\u901a\u8fc7<\/p>\n
"RuleEngine"\u786e\u4fdd\u4e0d\u8fdd\u53cd\u57fa\u672c\u4eba\u6743\u5e95\u7ebf<\/p>\n
– \u4eba\u7c7b\u5728\u73af：\u901a\u8fc7<\/p>\n
"HumanCommittee"\u5904\u7406\u673a\u5668\u96be\u4ee5\u88c1\u51b3\u7684\u4ef7\u503c\u51b2\u7a81<\/p>\n
\u00a0<\/p>\n
2. \u6cbb\u7406\u87ba\u65cb\u7684\u5b66\u4e60\u673a\u5236<\/p>\n
\u00a0<\/p>\n
\u6a21\u62df\u5668\u5b9e\u73b0\u4e86\u5b8c\u6574\u7684"\u5b9e\u8df5-\u51b2\u7a81-\u5b66\u4e60-\u8fdb\u5316"\u5faa\u73af：<\/p>\n
\u00a0<\/p>\n
– \u51b2\u7a81\u68c0\u6d4b \u2192 \u673a\u5236\u89e6\u53d1 \u2192 \u89e3\u51b3\u65b9\u6848 \u2192 \u89c4\u5219\u66f4\u65b0<\/p>\n
– \u6bcf\u6b21\u51b2\u7a81\u89e3\u51b3\u90fd\u4ea7\u751f\u65b0\u7684\u6cbb\u7406\u77e5\u8bc6<\/p>\n
\u00a0<\/p>\n
3. \u53ef\u6269\u5c55\u7684\u67b6\u6784\u8bbe\u8ba1<\/p>\n
\u00a0<\/p>\n
– \u6a21\u5757\u5316\u8bbe\u8ba1\u652f\u6301\u6dfb\u52a0\u65b0\u7684\u51b2\u7a81\u7c7b\u578b<\/p>\n
– \u89c4\u5219\u5f15\u64ce\u53ef\u63a5\u5165\u771f\u5b9e\u7684\u6cd5\u5f8b\u77e5\u8bc6\u5e93<\/p>\n
– \u4eba\u7c7b\u51b3\u7b56\u754c\u9762\u53ef\u6269\u5c55\u4e3a\u771f\u5b9e\u7684\u4e13\u5bb6\u7cfb\u7edf<\/p>\n
\u00a0<\/p>\n
4. \u5b9e\u8df5\u6307\u5bfc\u610f\u4e49<\/p>\n
\u00a0<\/p>\n
\u4e3a\u8de8\u56fd\u4f01\u4e1a\u3001\u56fd\u9645\u7ec4\u7ec7\u63d0\u4f9b\u4e86：<\/p>\n
\u00a0<\/p>\n
– \u53ef\u64cd\u4f5c\u7684\u51b2\u7a81\u89e3\u51b3\u6d41\u7a0b<\/p>\n
– \u4ef7\u503c\u6743\u8861\u7684\u5177\u4f53\u65b9\u6cd5<\/p>\n
– \u89c4\u5219\u6f14\u5316\u7684\u673a\u5236\u8bbe\u8ba1<\/p>\n
\u00a0<\/p>\n
\u8fd9\u4e2a\u5b8c\u6574\u5b9e\u73b0\u5c06\u7406\u8bba\u6846\u67b6\u8f6c\u5316\u4e3a\u53ef\u8fd0\u884c\u3001\u53ef\u9a8c\u8bc1\u7684\u8ba1\u7b97\u6a21\u578b，\u4e3a"\u4eba\u7c7b\u5728\u73af-\u89c4\u5219\u5728\u573a-\u8bed\u5883\u4e3b\u6743"\u7684\u4e09\u91cd\u534f\u540c\u6cbb\u7406\u8303\u5f0f\u63d0\u4f9b\u4e86\u575a\u5b9e\u7684\u6280\u672f\u5b9e\u73b0\u57fa\u7840\u3002<\/p>\n
\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"
\u9644\u4ef61：\u300a\u4e09\u91cd\u534f\u540c\u6cbb\u7406\u8303\u5f0f\u4eff\u771f\u6846\u67b6\u5b8c\u6574\u4ee3\u7801\u300b\u9644\u4ef62：\u300a\u8bed\u5883\u4e3b\u6743\u51b2\u7a81\u6848\u4f8b\u6a21\u62df\u5e93\u300b\u9644\u4ef61：\u300a\u4e09\u91cd\u534f\u540c\u6cbb\u7406\u8303\u5f0f\u4eff\u771f\u6846\u67b6\u5b8c\u6574\u4ee3\u7801\u300b\u5728AI\u5143\u4eba\u6587\u6784\u60f3\u89c6\u57df\u4e0b，\u672c\u6587\u5c06\u6838\u5fc3\u6cbb\u7406\u8303\u5f0f\u8f6c\u5316\u4e3a\u4e00\u4e2a\u53ef\u8fd0\u884c\u7684\u3001\u6a21\u5757\u5316\u7684Python\u4eff\u771f\u6846\u67b6\u3002\u8fd9\u4e2a\u6846\u67b6\u5b9e\u73b0\u4e86\u201c\u6cbb\u7406\u87ba\u65cb\u201d\u7684\u52a8\u6001\u4ea4\u4e92，\u5e76\u5305\u542b\u4e86\u4ef7\u503c\u539f\u8bed\u5e93\u3001\u8bed\u5883\u914d\u7f6e\u3001\u4eba\u7c7b\u88c1\u51b3\u63a5\u53e3\u7b49\u5173\u952e\u7ec4\u4ef6，\u4ee5\u4fbf\u76f4\u89c2\u5730\u7406\u89e3\u201c\u4eba\u7c7b\u5728\u73af\u2014\u89c4\u5219\u5728\u573a\u2014\u8bed\u5883\u4e3b\u6743\u201d\u5982\u4f55\u534f\u540c\u5de5\u4f5c\u3002\\”\\”\\”AI\u5143\u4eba\u6587\u4e09\u91cd\u534f\u540c\u6cbb\u7406\u8303<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50,323,188,442,2122],"topic":[],"class_list":["post-59565","post","type-post","status-publish","format-standard","hentry","category-server","tag-50","tag-323","tag-188","tag-442","tag-2122"],"yoast_head":"\n