AI幻觉率的业务影响量化：看起来对但实际错的成本模型

class HallucinationCostModel:
    def calculate_total_cost(self, scenario):
        """
        计算AI幻觉的总成本
        """
        # 基础参数
        hallucination_rate = scenario.hallucination_rate  # 幻觉率
        output_volume = scenario.monthly_outputs  # 月输出量
        
        # 直接成本
        direct_costs = {
            'detection': self.calc_detection_cost(scenario),
            'fixing': hallucination_rate * output_volume * self.avg_fix_cost,
            'rollback': hallucination_rate * output_volume * self.rollback_rate * self.avg_rollback_cost,
            'review': self.calc_review_cost(scenario),
            'compensation': hallucination_rate * output_volume * self.compensation_rate * self.avg_compensation
        }
        
        # 间接成本（基于直接成本乘数）
        indirect_multiplier = 2.5  # 经验值：间接成本通常是直接成本的2-3倍
        indirect_costs = sum(direct_costs.values()) * indirect_multiplier
        
        # 风险成本（概率加权）
        risk_costs = (
            scenario.regulatory_fine_probability * scenario.avg_regulatory_fine +
            scenario.lawsuit_probability * scenario.avg_lawsuit_cost +
            scenario.insurance_premium
        )
        
        return {
            'direct': sum(direct_costs.values()),
            'indirect': indirect_costs,
            'risk': risk_costs,
            'total': sum(direct_costs.values()) + indirect_costs + risk_costs
        }

def calculate_risk_adjusted_roi(investment, returns, hallucination_costs, confidence_level=0.95):
    """
    计算风险调整后的ROI
    """
    # Monte Carlo模拟
    simulations = 10000
    roi_distribution = []
    
    for _ in range(simulations):
        # 随机抽样
        actual_return = np.random.normal(returns.mean, returns.std)
        actual_hallucination_cost = np.random.choice(hallucination_costs)
        
        net_return = actual_return - actual_hallucination_cost
        roi = (net_return - investment) / investment
        roi_distribution.append(roi)
    
    # 计算VaR（Value at Risk）
    var_95 = np.percentile(roi_distribution, (1 - confidence_level) * 100)
    
    return {
        'expected_roi': np.mean(roi_distribution),
        'median_roi': np.median(roi_distribution),
        'worst_case_roi': np.min(roi_distribution),
        'var_95': var_95,  # 95%置信度下的最坏情况
        'roi_distribution': roi_distribution
    }