2026美赛数学建模F题思路模型代码 2026 ICM 问题F：Gen-AI 时代职业变革与高等教育适配模型
'classrooms': int(changes['net_change'] / 30),# 每班30人。'faculty': int(changes['net_change'] / 15),# 15:1师生比。'lab_space': changes['net_change'] * 2.5,# 平方米。('data_scientist', 'digital_artist'): 0.4,# 共同
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问题二：
https://blog.csdn.net/m0_52343631/article/details/157545543?spm=1001.2014.3001.5502
https://blog.csdn.net/m0_52343631/article/details/157545543?spm=1001.2014.3001.5502
https://blog.csdn.net/m0_52343631/article/details/157545543?spm=1001.2014.3001.5502https://blog.csdn.net/m0_52343631/article/details/157545543
问题三：
https://blog.csdn.net/m0_52343631/article/details/157545605?spm=1001.2014.3001.5502
https://blog.csdn.net/m0_52343631/article/details/157545605?spm=1001.2014.3001.5502https://blog.csdn.net/m0_52343631/article/details/157545605?spm=1001.2014.3001.5502https://blog.csdn.net/m0_52343631/article/details/157545605
问题1详细分析：Gen-AI时代下教育计划的规模调整策略
一、问题深度解析
1.1 问题的核心内涵
问题1要求我们探讨在Gen-AI对职业产生影响的背景下，高等教育计划应当如何调整招生规模。这个问题本质上是一个复杂系统决策问题，涉及多个层面的考量：
-
市场供需平衡：Gen-AI如何改变劳动力市场的需求结构
-
教育资源配置：有限的教育资源如何在变化的环境中优化分配
-
社会公平考量：规模调整对社会流动性和教育机会的影响
-
技术扩散效应：Gen-AI技术在不同职业领域的渗透速度和深度差异
-
时间滞后效应：教育决策与劳动力市场变化之间的时间差
1.2 分析框架的构建
我们采用五维度分析框架：
规模调整决策 = f(市场需求变化, 技术替代率, 技能转型难度,
教育资源约束, 社会外部性)
每个维度下包含多个具体指标，形成完整的评估体系。
二、数学模型建立
2.1 职业需求预测模型
2.1.1 基础模型：扩展的Cobb-Douglas生产函数
import numpy as np
import pandas as pd
from scipy.optimize import minimize
from scipy.integrate import odeint
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
import warnings
warnings.filterwarnings('ignore')
class LaborDemandModel:
"""
基于扩展生产函数的劳动力需求模型
将Gen-AI作为新的生产要素引入
"""
def __init__(self, alpha=0.3, beta=0.4, gamma=0.3, rho=0.5):
"""
参数初始化
alpha: 劳动力产出弹性
beta: 资本产出弹性
gamma: Gen-AI产出弹性
rho: 生产要素替代弹性参数
"""
self.alpha = alpha
self.beta = beta
self.gamma = gamma
self.rho = rho
self.A = 1.0 # 技术水平
def ces_production(self, L, K, AI, sigma=0.8):
"""
Constant Elasticity of Substitution (CES) 生产函数
考虑劳动力、资本和Gen-AI之间的不完全替代
"""
if self.rho == 0:
# Cobb-Douglas特殊情况
return self.A * (L**self.alpha) * (K**self.beta) * (AI**self.gamma)
else:
# 一般CES形式
r = -self.rho
return self.A * (
self.alpha * L**r +
self.beta * K**r +
self.gamma * AI**r
)**(-1/r)
def optimal_labor_demand(self, w, r, p_ai, Y_target, K_fixed=1.0):
"""
在成本最小化下的最优劳动力需求
w: 工资率
r: 资本成本
p_ai: Gen-AI使用成本
Y_target: 目标产出
K_fixed: 固定资本存量
"""
def cost_function(L_AI):
L, AI = L_AI
# 成本函数
cost = w*L + r*K_fixed + p_ai*AI
# 产出约束
Y = self.ces_production(L, K_fixed, AI)
penalty = 1000 * max(0, Y_target - Y)**2
return cost + penalty
# 初始猜测
x0 = np.array([Y_target/10, Y_target/20])
bounds = [(0, Y_target/2), (0, Y_target/2)]
res = minimize(cost_function, x0, bounds=bounds, method='L-BFGS-B')
if res.success:
optimal_L, optimal_AI = res.x
actual_Y = self.ces_production(optimal_L, K_fixed, optimal_AI)
return {
'optimal_labor': optimal_L,
'optimal_ai': optimal_AI,
'total_cost': res.fun,
'actual_output': actual_Y,
'ai_labor_ratio': optimal_AI / optimal_L if optimal_L > 0 else float('inf')
}
else:
raise ValueError("Optimization failed")
def simulate_demand_trajectory(self, base_demand, ai_cost_decline_rate=0.15,
years=10, wage_growth=0.03):
"""
模拟未来劳动力需求轨迹
"""
results = []
L_current = base_demand
p_ai_current = 1.0 # 标准化AI成本
for year in range(years):
# AI成本逐年下降
p_ai = p_ai_current * (1 - ai_cost_decline_rate)**year
# 工资逐年增长
w = 1.0 * (1 + wage_growth)**year
# 目标产出随经济增长
Y_target = base_demand * (1.02)**year
# 计算最优需求
optimal = self.optimal_labor_demand(w, 0.1, p_ai, Y_target)
results.append({
'year': year + 2026,
'labor_demand': optimal['optimal_labor'],
'ai_intensity': optimal['optimal_ai'] / Y_target,
'ai_labor_ratio': optimal['ai_labor_ratio'],
'wage_rate': w,
'ai_cost': p_ai,
'output_target': Y_target
})
L_current = optimal['optimal_labor']
return pd.DataFrame(results)
2.1.2 行业特定参数校准
class IndustrySpecificCalibration:
"""
针对不同职业类别的参数校准
"""
# 基于世界经济论坛和麦肯锡研究的行业参数
INDUSTRY_PARAMETERS = {
'data_scientist': {
'alpha': 0.7, # 高劳动力产出弹性
'beta': 0.2, # 中等资本弹性
'gamma': 0.5, # 高AI弹性
'substitution_elasticity': 0.9, # 高替代弹性
'ai_adoption_speed': 0.25, # 快速采用
'skill_obsolescence_rate': 0.2 # 技能过时率
},
'electrician': {
'alpha': 0.6, # 中等劳动力弹性
'beta': 0.3, # 设备依赖
'gamma': 0.2, # 低AI弹性
'substitution_elasticity': 0.3, # 低替代弹性
'ai_adoption_speed': 0.1, # 缓慢采用
'skill_obsolescence_rate': 0.08 # 慢技能过时
},
'digital_artist': {
'alpha': 0.8, # 非常高劳动力弹性
'beta': 0.1, # 低资本弹性
'gamma': 0.6, # 高AI弹性
'substitution_elasticity': 0.6, # 中等替代弹性
'ai_adoption_speed': 0.2, # 中等采用速度
'skill_obsolescence_rate': 0.15 # 中等技能过时
}
}
def calibrate_model_for_career(self, career_type, base_data):
"""
为特定职业校准模型参数
"""
params = self.INDUSTRY_PARAMETERS[career_type]
# 创建校准模型
model = LaborDemandModel(
alpha=params['alpha'],
beta=params['beta'],
gamma=params['gamma'],
rho=(1/params['substitution_elasticity']) - 1
)
# 模拟未来需求
simulation = model.simulate_demand_trajectory(
base_demand=base_data['current_demand'],
ai_cost_decline_rate=0.18, # 根据Ray Kurzweil的加速回报定律
years=10,
wage_growth=base_data.get('wage_growth', 0.02)
)
# 计算复合增长率
initial_demand = simulation.iloc[0]['labor_demand']
final_demand = simulation.iloc[-1]['labor_demand']
cagr = (final_demand / initial_demand) ** (1/10) - 1
return {
'model': model,
'simulation': simulation,
'parameters': params,
'cagr': cagr,
'net_change': final_demand - initial_demand,
'percent_change': (final_demand - initial_demand) / initial_demand * 100
}
2.2 教育规模优化模型
2.2.1 多期动态规划模型
class EducationScaleOptimizer:
"""
教育规模优化的动态规划模型
考虑：
1. 劳动力市场的时间滞后
2. 教育资源约束
3. 学生流动的动态性
"""
def __init__(self, horizon=10, discount_factor=0.95):
self.horizon = horizon
self.discount_factor = discount_factor
def state_transition(self, current_state, decision, career_params):
"""
状态转移函数
current_state: (enrollment, labor_demand, skill_gap)
decision: 招生规模变化率 (-1到1)
"""
enrollment, demand, skill_gap = current_state
# 1. 招生规模更新（有最大变化率限制）
max_change_rate = 0.15 # 每年最多变化15%
actual_decision = max(-max_change_rate, min(max_change_rate, decision))
new_enrollment = enrollment * (1 + actual_decision)
# 2. 毕业生进入劳动力市场（4年延迟）
graduates_entering = enrollment * 0.25
# 3. 劳动力需求预测更新
demand_growth = career_params.get('demand_growth', 0.02)
new_demand = demand * (1 + demand_growth)
# 4. 技能缺口计算
ai_penetration = career_params.get('ai_penetration', 0.3)
required_ai_skills = new_demand * ai_penetration
# 计算供给的AI技能（取决于课程改革效果）
ai_training_efficiency = career_params.get('training_efficiency', 0.7)
ai_skills_supplied = graduates_entering * ai_training_efficiency
new_skill_gap = max(0, required_ai_skills - ai_skills_supplied)
return np.array([new_enrollment, new_demand, new_skill_gap])
def reward_function(self, state, decision, next_state, career_type):
"""
奖励函数：综合考虑多个目标
"""
enrollment, demand, skill_gap = state
next_enrollment, next_demand, next_skill_gap = next_state
rewards = {}
# 1. 就业匹配奖励（负向技能缺口）
employment_match = -next_skill_gap * 10
# 2. 规模稳定性奖励（避免剧烈波动）
enrollment_change = abs(decision)
stability_reward = -enrollment_change * 5
# 3. 成本效率奖励
cost_per_student = self.calculate_cost_per_student(career_type)
efficiency_reward = -next_enrollment * cost_per_student / 1000
# 4. 社会价值奖励（基于职业类型）
social_value_weights = {
'stem': 1.5, # STEM有更高的社会外部性
'trade': 1.2, # 技工对社会运行重要
'arts': 1.0 # 艺术的文化价值
}
social_reward = next_enrollment * social_value_weights.get(career_type, 1.0)
# 总奖励（加权和）
total_reward = (
0.4 * employment_match +
0.2 * stability_reward +
0.2 * efficiency_reward +
0.2 * social_reward
)
rewards['total'] = total_reward
rewards['components'] = {
'employment_match': employment_match,
'stability': stability_reward,
'efficiency': efficiency_reward,
'social_value': social_reward
}
return rewards
def calculate_cost_per_student(self, career_type):
"""计算生均成本"""
costs = {
'stem': 25000, # STEM教育设备成本高
'trade': 18000, # 技工教育实训成本
'arts': 22000 # 艺术教育材料成本
}
return costs.get(career_type, 20000)
def dynamic_programming_optimization(self, initial_state, career_params, career_type):
"""
动态规划求解最优招生策略
"""
# 离散化决策空间
decision_options = np.linspace(-0.15, 0.15, 31) # -15%到+15%，步长1%
# 初始化值函数
V = np.zeros((self.horizon + 1, 3)) # 简化的状态表示
policy = np.zeros((self.horizon + 1, 3))
# 向后递归
for t in range(self.horizon - 1, -1, -1):
for s_idx, state in enumerate([initial_state]): # 简化状态空间
best_value = -float('inf')
best_decision = 0
for decision in decision_options:
# 状态转移
next_state = self.state_transition(state, decision, career_params)
# 立即奖励
rewards = self.reward_function(state, decision, next_state, career_type)
# 未来价值（简化处理）
future_value = 0
if t < self.horizon - 1:
# 在简化版本中，我们近似处理
future_value = V[t + 1, 0] * self.discount_factor
total_value = rewards['total'] + future_value
if total_value > best_value:
best_value = total_value
best_decision = decision
V[t, s_idx] = best_value
policy[t, s_idx] = best_decision
# 生成最优路径
optimal_path = []
current_state = initial_state
for t in range(self.horizon):
decision = policy[t, 0]
next_state = self.state_transition(current_state, decision, career_params)
optimal_path.append({
'year': 2026 + t,
'decision': decision,
'enrollment': current_state[0],
'demand': current_state[1],
'skill_gap': current_state[2],
'new_enrollment': next_state[0]
})
current_state = next_state
return pd.DataFrame(optimal_path), policy
2.2.2 考虑资源约束的线性规划模型
from scipy.optimize import linprog
class ResourceConstrainedOptimizer:
"""
考虑多重资源约束的线性规划模型
"""
def __init__(self, n_programs=3, n_years=5):
self.n_programs = n_programs
self.n_years = n_years
def build_lp_problem(self, demand_forecast, resource_constraints, transition_costs):
"""
构建线性规划问题
目标：最大化社会总价值
约束：资源限制、最小规模、最大变化率等
"""
# 决策变量：每个项目每年的招生规模变化
n_vars = self.n_programs * self.n_years
# 1. 目标函数系数：社会价值权重
social_values = np.array([1.5, 1.2, 1.0])
# 扩展到多年
c = np.zeros(n_vars)
for year in range(self.n_years):
for prog in range(self.n_programs):
idx = year * self.n_programs + prog
# 目标：最大化社会价值（所以取负）
c[idx] = -social_values[prog] * (1 - 0.05 * year) # 时间折扣
# 2. 约束条件
A_eq = [] # 等式约束
b_eq = []
A_ub = [] # 不等式约束
b_ub = []
# 约束1：总资源限制（每年）
for year in range(self.n_years):
row = np.zeros(n_vars)
for prog in range(self.n_programs):
idx = year * self.n_programs + prog
row[idx] = resource_constraints['cost_per_student'][prog]
A_ub.append(row)
b_ub.append(resource_constraints['total_budget'][year])
# 约束2：最大变化率约束
for year in range(1, self.n_years):
for prog in range(self.n_programs):
row = np.zeros(n_vars)
idx_current = year * self.n_programs + prog
idx_previous = (year-1) * self.n_programs + prog
# 增长约束：x_current <= 1.15 * x_previous
row[idx_current] = 1
row[idx_previous] = -1.15
A_ub.append(row)
b_ub.append(0)
# 缩减约束：x_current >= 0.85 * x_previous
row2 = np.zeros(n_vars)
row2[idx_current] = -1
row2[idx_previous] = 0.85
A_ub.append(row2)
b_ub.append(0)
# 约束3：满足最低社会需求
for year in range(self.n_years):
for prog in range(self.n_programs):
row = np.zeros(n_vars)
idx = year * self.n_programs + prog
row[idx] = -1 # -x <= -min_demand
A_ub.append(row)
b_ub.append(-demand_forecast[prog][year] * 0.8) # 至少满足80%需求
# 约束4：总学生数限制（学校容量）
for year in range(self.n_years):
row = np.zeros(n_vars)
for prog in range(self.n_programs):
idx = year * self.n_programs + prog
row[idx] = 1
A_ub.append(row)
b_ub.append(resource_constraints['max_capacity'][year])
# 边界条件
bounds = [(0, None) for _ in range(n_vars)] # 招生数非负
# 求解线性规划
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq=b_eq,
bounds=bounds, method='highs')
if res.success:
return self.interpret_solution(res.x, demand_forecast)
else:
raise ValueError("Linear programming failed to find solution")
def interpret_solution(self, solution, demand_forecast):
"""
解释线性规划结果
"""
enrollment_plan = solution.reshape((self.n_years, self.n_programs))
results = []
for prog in range(self.n_programs):
program_results = {
'program': ['STEM', 'Trade', 'Arts'][prog],
'enrollment_plan': enrollment_plan[:, prog],
'demand_forecast': demand_forecast[prog],
'supply_gap': enrollment_plan[:, prog] - demand_forecast[prog],
'utilization_rate': enrollment_plan[:, prog] / demand_forecast[prog] * 100
}
results.append(program_results)
# 计算总体指标
total_enrollment = enrollment_plan.sum(axis=1)
total_demand = np.array(demand_forecast).sum(axis=0)
summary = {
'total_enrollment': total_enrollment,
'total_demand': total_demand,
'overall_gap': total_enrollment - total_demand,
'overall_utilization': total_enrollment / total_demand * 100,
'yearly_resource_allocation': enrollment_plan
}
return results, summary
2.3 敏感性分析与情景模拟
class SensitivityAnalysis:
"""
对关键参数进行敏感性分析
"""
def __init__(self, base_scenario):
self.base = base_scenario
def analyze_parameter_sensitivity(self, parameters, variations=[-0.3, -0.15, 0, 0.15, 0.3]):
"""
分析单个参数的敏感性
"""
results = {}
for param_name, param_value in parameters.items():
scenario_results = []
for var in variations:
# 创建修改后的参数
modified_params = self.base['career_params'].copy()
modified_params[param_name] = param_value * (1 + var)
# 重新运行优化
optimizer = EducationScaleOptimizer()
optimal_path, _ = optimizer.dynamic_programming_optimization(
self.base['initial_state'],
modified_params,
self.base['career_type']
)
# 提取关键指标
final_enrollment = optimal_path.iloc[-1]['new_enrollment']
final_skill_gap = optimal_path.iloc[-1]['skill_gap']
avg_decision = optimal_path['decision'].mean()
scenario_results.append({
'parameter_variation': var,
'final_enrollment': final_enrollment,
'enrollment_change_pct': (final_enrollment - self.base['final_enrollment']) / self.base['final_enrollment'] * 100,
'final_skill_gap': final_skill_gap,
'skill_gap_change_pct': (final_skill_gap - self.base['final_skill_gap']) / max(self.base['final_skill_gap'], 1) * 100,
'avg_decision': avg_decision
})
results[param_name] = pd.DataFrame(scenario_results)
return results
def monte_carlo_simulation(self, n_simulations=1000):
"""
蒙特卡洛模拟：考虑多个参数同时变化
"""
simulations = []
# 定义参数分布
param_distributions = {
'ai_adoption_speed': lambda: np.random.beta(5, 3) * 0.4, # 偏右分布
'demand_growth': lambda: np.random.normal(0.02, 0.005),
'training_efficiency': lambda: np.random.beta(8, 3) * 0.8 + 0.2, # 0.2-1.0
'wage_growth': lambda: np.random.normal(0.03, 0.01),
'ai_cost_decline': lambda: np.random.beta(3, 2) * 0.3 # 0-0.3
}
for sim in range(n_simulations):
# 生成随机参数
random_params = {}
for param_name, dist_func in param_distributions.items():
random_params[param_name] = dist_func()
# 运行模拟
model = LaborDemandModel()
simulation = model.simulate_demand_trajectory(
base_demand=self.base['base_demand'],
ai_cost_decline_rate=random_params['ai_cost_decline'],
wage_growth=random_params['wage_growth']
)
# 计算关键结果
final_demand = simulation.iloc[-1]['labor_demand']
demand_change = (final_demand - self.base['base_demand']) / self.base['base_demand']
# 基于需求变化决定招生策略
if demand_change > 0.2:
enrollment_decision = '大幅扩大'
enrollment_change = 0.15
elif demand_change > 0.05:
enrollment_decision = '适度扩大'
enrollment_change = 0.05
elif demand_change < -0.2:
enrollment_decision = '大幅缩减'
enrollment_change = -0.15
elif demand_change < -0.05:
enrollment_decision = '适度缩减'
enrollment_change = -0.05
else:
enrollment_decision = '保持稳定'
enrollment_change = 0
simulations.append({
'simulation_id': sim,
'final_demand': final_demand,
'demand_change_pct': demand_change * 100,
'enrollment_decision': enrollment_decision,
'enrollment_change': enrollment_change,
**random_params
})
return pd.DataFrame(simulations)
三、详细分析过程
3.1 数据收集与处理
class DataProcessor:
"""
数据收集、清洗和处理模块
"""
def __init__(self):
self.data_sources = {
'employment': 'BLS_Occupational_Outlook_Handbook',
'wages': 'BLS_OES_Data',
'education': 'NCES_IPEDS',
'ai_impact': 'McKinsey_AI_Impact_Report',
'skill_demand': 'WEF_Future_of_Jobs'
}
def load_simulated_data(self):
"""
加载模拟数据（实际应用中替换为真实数据）
"""
# 职业基础数据
careers_data = {
'data_scientist': {
'current_demand': 100000,
'current_supply': 85000,
'wage_growth': 0.045,
'ai_susceptibility': 0.6,
'ai_augmentation': 0.8,
'education_required': 'master',
'training_years': 6
},
'electrician': {
'current_demand': 80000,
'current_supply': 75000,
'wage_growth': 0.035,
'ai_susceptibility': 0.3,
'ai_augmentation': 0.5,
'education_required': 'apprenticeship',
'training_years': 4
},
'digital_artist': {
'current_demand': 50000,
'current_supply': 45000,
'wage_growth': 0.04,
'ai_susceptibility': 0.7,
'ai_augmentation': 0.9,
'education_required': 'bachelor',
'training_years': 4
}
}
# AI影响参数（基于行业研究）
ai_impact_params = {
'data_scientist': {
'task_automation_potential': 0.4,
'new_task_creation': 0.6,
'productivity_gain': 1.8,
'skill_shift_required': 0.7
},
'electrician': {
'task_automation_potential': 0.2,
'new_task_creation': 0.3,
'productivity_gain': 1.3,
'skill_shift_required': 0.4
},
'digital_artist': {
'task_automation_potential': 0.5,
'new_task_creation': 0.4,
'productivity_gain': 2.0,
'skill_shift_required': 0.8
}
}
# 教育机构数据
institutions_data = {
'MIT_Data_Science': {
'current_enrollment': 500,
'capacity': 800,
'graduation_rate': 0.85,
'employment_rate': 0.92,
'cost_per_student': 45000,
'faculty_student_ratio': 1:10
},
'Lincoln_Electrician': {
'current_enrollment': 300,
'capacity': 400,
'graduation_rate': 0.88,
'employment_rate': 0.94,
'cost_per_student': 22000,
'faculty_student_ratio': 1:12
},
'RISD_Digital_Arts': {
'current_enrollment': 200,
'capacity': 300,
'graduation_rate': 0.82,
'employment_rate': 0.87,
'cost_per_student': 38000,
'faculty_student_ratio': 1:8
}
}
return {
'careers': careers_data,
'ai_impact': ai_impact_params,
'institutions': institutions_data
}
def calculate_net_impact(self, career_data, ai_impact_data):
"""
计算Gen-AI的净影响
净影响 = 创造效应 - 替代效应 + 增强效应
"""
results = {}
for career_name, career_info in career_data.items():
ai_info = ai_impact_data[career_name]
# 替代效应：可能被自动化的工作比例
substitution_effect = career_info['ai_susceptibility'] * ai_info['task_automation_potential']
# 创造效应：新技术创造的新工作
creation_effect = ai_info['new_task_creation']
# 增强效应：生产力提升带来的相对需求变化
# 生产力提升可能导致总需求减少，但也可能创造新需求
productivity_effect = (ai_info['productivity_gain'] - 1) * 0.3 # 简化计算
# 净就业影响
net_employment_impact = creation_effect - substitution_effect + productivity_effect
# 技能转型需求
skill_shift_needed = ai_info['skill_shift_required']
results[career_name] = {
'substitution_effect': substitution_effect,
'creation_effect': creation_effect,
'productivity_effect': productivity_effect,
'net_employment_impact': net_employment_impact,
'skill_shift_needed': skill_shift_needed,
'overall_impact_category': self.categorize_impact(net_employment_impact)
}
return results
def categorize_impact(self, net_impact):
"""根据净影响大小分类"""
if net_impact > 0.2:
return "Strong Growth (扩张 >20%)"
elif net_impact > 0.05:
return "Moderate Growth (适度扩张 5-20%)"
elif net_impact > -0.05:
return "Stable (基本稳定 -5%到5%)"
elif net_impact > -0.2:
return "Moderate Decline (适度缩减 -20%到-5%)"
else:
return "Strong Decline (大幅缩减 < -20%)"
3.2 规模调整决策算法
class ScaleAdjustmentDecider:
"""
综合决策算法：确定最优的规模调整策略
"""
def __init__(self, decision_criteria):
self.criteria = decision_criteria
def make_decision(self, analysis_results, institution_data, career_type):
"""
基于多重标准做出决策
"""
decisions = {}
# 标准1：市场需求变化
demand_change = analysis_results['demand_forecast']['percent_change'] / 100
# 标准2：技能缺口大小
skill_gap_ratio = analysis_results['skill_analysis']['gap_ratio']
# 标准3：资源约束
capacity_utilization = institution_data['current_enrollment'] / institution_data['capacity']
# 标准4：社会外部性
social_externality = self.criteria['social_externality_weights'][career_type]
# 标准5：战略重要性
strategic_importance = self.criteria['strategic_importance'][career_type]
# 综合评分
scores = {}
# 市场需求评分（越高越好）
scores['demand'] = self.score_demand_change(demand_change)
# 技能匹配评分（缺口越小越好）
scores['skill_match'] = 10 - skill_gap_ratio * 10
# 资源效率评分（利用率适中最好）
scores['resource_efficiency'] = self.score_utilization(capacity_utilization)
# 社会价值评分
scores['social_value'] = social_externality * 10
# 战略价值评分
scores['strategic'] = strategic_importance * 10
# 加权总分
weights = self.criteria['decision_weights']
total_score = sum(scores[cat] * weights[cat] for cat in weights)
# 决策阈值
if total_score >= 8:
decision = {
'action': 'Significant Expansion',
'enrollment_change': 0.15,
'reasoning': 'High demand, good skill match, and strategic importance',
'priority': 'High',
'timeline': 'Immediate (next academic year)'
}
elif total_score >= 6:
decision = {
'action': 'Moderate Expansion',
'enrollment_change': 0.05,
'reasoning': 'Positive outlook with manageable challenges',
'priority': 'Medium',
'timeline': 'Phase over 2-3 years'
}
elif total_score >= 4:
decision = {
'action': 'Maintain Current Scale',
'enrollment_change': 0.0,
'reasoning': 'Balanced factors, wait and see approach',
'priority': 'Low',
'timeline': 'Monitor and reassess annually'
}
elif total_score >= 2:
decision = {
'action': 'Moderate Reduction',
'enrollment_change': -0.05,
'reasoning': 'Declining demand or significant skill mismatch',
'priority': 'Medium',
'timeline': 'Gradual reduction over 3-4 years'
}
else:
decision = {
'action': 'Significant Reduction or Program Restructuring',
'enrollment_change': -0.15,
'reasoning': 'Severe challenges requiring fundamental changes',
'priority': 'High',
'timeline': 'Immediate action with transition plan'
}
decisions['scores'] = scores
decisions['total_score'] = total_score
decisions['decision'] = decision
decisions['detailed_analysis'] = analysis_results
return decisions
def score_demand_change(self, change):
"""需求变化评分"""
if change > 0.2:
return 10
elif change > 0.1:
return 8
elif change > 0:
return 6
elif change > -0.1:
return 4
else:
return 2
def score_utilization(self, utilization):
"""利用率评分（黄金比例：80-90%）"""
if 0.8 <= utilization <= 0.9:
return 10
elif 0.7 <= utilization < 0.8 or 0.9 < utilization <= 0.95:
return 8
elif 0.6 <= utilization < 0.7 or 0.95 < utilization <= 1.0:
return 6
else:
return 4
def generate_transition_plan(self, decision, current_state, target_state):
"""
生成转型计划
"""
plan = {
'phases': [],
'resource_reallocation': {},
'student_transition': {},
'faculty_development': {}
}
if decision['action'] in ['Significant Expansion', 'Moderate Expansion']:
# 扩张计划
plan['phases'].append({
'phase': 1,
'duration': '1 year',
'actions': [
'Increase marketing and recruitment efforts',
'Hire additional adjunct faculty',
'Expand online course offerings',
'Form industry partnerships for internships'
]
})
plan['resource_reallocation'] = {
'budget_increase': decision['enrollment_change'] * current_state['current_enrollment'] * current_state['cost_per_student'] * 1.2,
'faculty_needed': int(decision['enrollment_change'] * current_state['current_enrollment'] / 15),
'facility_expansion': 'Additional lab space or rental facilities'
}
elif decision['action'] in ['Moderate Reduction', 'Significant Reduction or Program Restructuring']:
# 缩减或重组计划
plan['phases'].append({
'phase': 1,
'duration': '1-2 years',
'actions': [
'Freeze new admissions gradually',
'Develop transition programs for current students',
'Retrain faculty for emerging fields',
'Redeploy resources to high-demand programs'
]
})
plan['student_transition'] = {
'alternative_programs': self.suggest_alternative_programs(current_state['career_type']),
'credits_transfer': 'Establish articulation agreements',
'career_services': 'Enhanced counseling and job placement'
}
plan['faculty_development'] = {
'retraining_programs': 'AI integration, new teaching methodologies',
'research_redirection': 'Focus on interdisciplinary projects',
'early_retirement_options': 'Voluntary separation packages'
}
return plan
def suggest_alternative_programs(self, career_type):
"""建议替代性专业"""
alternatives = {
'data_scientist': [
'AI Ethics and Governance',
'Quantum Computing',
'Bioinformatics',
'Cybersecurity Analytics'
],
'electrician': [
'Renewable Energy Technician',
'Smart Grid Technology',
'Robotics Maintenance',
'Energy Efficiency Consulting'
],
'digital_artist': [
'Immersive Media Design',
'Creative Technology',
'Digital Heritage Preservation',
'Interactive Narrative Design'
]
}
return alternatives.get(career_type, [])
四、实例应用：三个职业的具体分析
4.1 数据科学家（STEM职业）
def analyze_data_scientist_case():
"""
数据科学家职业的详细分析
"""
# 1. 基础数据
data_scientist = {
'current_demand': 100000,
'current_supply': 85000,
'skill_gap': 15000,
'avg_salary': 120000,
'growth_rate_historical': 0.15,
'ai_readiness': 0.8
}
# 2. AI影响分析
ai_impact = {
'automation_potential': {
'data_processing': 0.7,
'basic_analysis': 0.6,
'model_training': 0.4,
'business_insights': 0.3,
'strategy_development': 0.1
},
'augmentation_potential': {
'complex_problem_solving': 1.5,
'hypothesis_generation': 1.8,
'multimodal_analysis': 2.0,
'real_time_decision': 1.7
},
'new_roles_created': [
'AI Ethics Officer',
'Prompt Engineering Specialist',
'Human-AI Collaboration Manager',
'Generative AI Strategist'
]
}
# 3. 需求预测
print("=== Data Scientist Career Analysis ===")
# 使用CES模型预测
model = LaborDemandModel(alpha=0.7, beta=0.2, gamma=0.5, rho=0.5)
simulation = model.simulate_demand_trajectory(
base_demand=100000,
ai_cost_decline_rate=0.2,
years=10,
wage_growth=0.04
)
print(f"Initial demand: {simulation.iloc[0]['labor_demand']:,.0f}")
print(f"Final demand (5 years): {simulation.iloc[4]['labor_demand']:,.0f}")
print(f"Final demand (10 years): {simulation.iloc[-1]['labor_demand']:,.0f}")
print(f"AI-Labor ratio change: {simulation.iloc[0]['ai_labor_ratio']:.2f} → {simulation.iloc[-1]['ai_labor_ratio']:.2f}")
# 4. 教育需求分析
# MIT数据科学硕士项目
mit_program = {
'current_enrollment': 500,
'graduation_rate': 0.85,
'employment_rate': 0.92,
'time_to_degree': 2,
'capacity': 800
}
# 计算所需规模变化
demand_growth = (simulation.iloc[-1]['labor_demand'] - simulation.iloc[0]['labor_demand']) / simulation.iloc[0]['labor_demand']
# 考虑替代性供给来源
alternative_sources = {
'other_programs': 0.3,
'career_changers': 0.2,
'international_students': 0.25
}
# MIT需要满足的需求比例
mit_market_share = 0.15
required_graduates = simulation.iloc[-1]['labor_demand'] * mit_market_share
current_graduates = mit_program['current_enrollment'] * mit_program['graduation_rate']
enrollment_change_needed = (required_graduates - current_graduates) / current_graduates
print(f"\nMIT Program Analysis:")
print(f"Required graduates: {required_graduates:.0f}")
print(f"Current graduates: {current_graduates:.0f}")
print(f"Enrollment change needed: {enrollment_change_needed:.1%}")
# 5. 决策建议
if enrollment_change_needed > 0.2:
recommendation = "Significant expansion needed (>20%)"
actions = [
"Increase enrollment capacity to 650-700 students",
"Develop online/hybrid options",
"Establish industry partnerships for funding",
"Hire additional faculty with AI expertise"
]
elif enrollment_change_needed > 0.05:
recommendation = "Moderate expansion needed (5-20%)"
actions = [
"Gradual increase to 550-600 students",
"Enhance existing programs with AI focus",
"Create specialized tracks in Gen-AI applications"
]
else:
recommendation = "Maintain or slight adjustment"
actions = [
"Focus on quality over quantity",
"Update curriculum for AI integration",
"Strengthen alumni network for placement"
]
print(f"\nRecommendation: {recommendation}")
print("Recommended actions:")
for i, action in enumerate(actions, 1):
print(f" {i}. {action}")
return {
'simulation': simulation,
'demand_growth': demand_growth,
'enrollment_change_needed': enrollment_change_needed,
'recommendation': recommendation,
'actions': actions
}
4.2 高级电工（技工职业）
def analyze_electrician_case():
"""
高级电工职业的详细分析
"""
# 1. 基础数据
electrician_data = {
'current_demand': 80000,
'current_supply': 75000,
'skill_gap': 5000,
'avg_salary': 65000,
'growth_rate_historical': 0.04,
'ai_readiness': 0.3
}
# 2. AI影响分析
ai_impact_electrician = {
'automation_potential': {
'routine_inspections': 0.4,
'diagnostic_testing': 0.5,
'scheduling': 0.7,
'inventory_management': 0.8,
'physical_installation': 0.1
},
'augmentation_potential': {
'complex_troubleshooting': 1.3,
'smart_system_design': 1.6,
'energy_efficiency_audit': 1.4,
'safety_compliance': 1.2
},
'new_skills_required': [
'IoT Device Configuration',
'Smart Grid Maintenance',
'AR-Assisted Repair',
'Predictive Maintenance Analytics'
]
}
# 3. 需求预测
print("\n=== Electrician Career Analysis ===")
model = LaborDemandModel(alpha=0.6, beta=0.3, gamma=0.2, rho=0.8)
simulation = model.simulate_demand_trajectory(
base_demand=80000,
ai_cost_decline_rate=0.1,
years=10,
wage_growth=0.035
)
print(f"Initial demand: {simulation.iloc[0]['labor_demand']:,.0f}")
print(f"Final demand (10 years): {simulation.iloc[-1]['labor_demand']:,.0f}")
# 考虑绿色转型影响
green_transition_boost = 1.15 # 可再生能源转型带来15%额外需求
adjusted_demand = simulation.iloc[-1]['labor_demand'] * green_transition_boost
print(f"Adjusted for green transition: {adjusted_demand:,.0f}")
# 4. 教育需求分析 - 林肯电气学校
lincoln_program = {
'current_enrollment': 300,
'graduation_rate': 0.88,
'employment_rate': 0.94,
'program_duration': 2, # 年
'capacity': 400
}
# 学校市场份额估计
school_market_share = 0.02 # 培养2%的电工
required_graduates = adjusted_demand * school_market_share
current_graduates = lincoln_program['current_enrollment'] * lincoln_program['graduation_rate']
enrollment_change_needed = (required_graduates - current_graduates) / current_graduates
print(f"\nLincoln Electric Program Analysis:")
print(f"Required graduates: {required_graduates:.0f}")
print(f"Current graduates: {current_graduates:.0f}")
print(f"Enrollment change needed: {enrollment_change_needed:.1%}")
print(f"Current capacity utilization: {lincoln_program['current_enrollment']/lincoln_program['capacity']:.1%}")
# 5. 决策建议
capacity_utilization = lincoln_program['current_enrollment'] / lincoln_program['capacity']
if enrollment_change_needed > 0.1 and capacity_utilization < 0.9:
recommendation = "Moderate expansion to meet growing demand"
actions = [
"Increase enrollment to 340-360 students",
"Add evening/weekend programs",
"Develop renewable energy specialization",
"Partner with utilities for apprenticeship programs"
]
elif capacity_utilization > 0.9:
recommendation = "At capacity - consider facility expansion or partnerships"
actions = [
"Explore satellite campus options",
"Develop hybrid learning models",
"Form consortium with other trade schools",
"Prioritize quality and completion rates"
]
else:
recommendation = "Maintain current scale with curriculum updates"
actions = [
"Integrate smart grid technology training",
"Add AI-assisted diagnostic modules",
"Strengthen safety and compliance training",
"Focus on energy efficiency specialization"
]
print(f"\nRecommendation: {recommendation}")
print("Recommended actions:")
for i, action in enumerate(actions, 1):
print(f" {i}. {action}")
return {
'simulation': simulation,
'adjusted_demand': adjusted_demand,
'enrollment_change_needed': enrollment_change_needed,
'capacity_utilization': capacity_utilization,
'recommendation': recommendation,
'actions': actions
}
4.3 数字艺术家（艺术职业）
def analyze_digital_artist_case():
"""
数字艺术家职业的详细分析
"""
# 1. 基础数据
artist_data = {
'current_demand': 50000,
'current_supply': 45000,
'skill_gap': 5000,
'avg_income': 55000, # 收入变化大
'growth_rate_historical': 0.08,
'ai_readiness': 0.6
}
# 2. AI影响分析 - 特别复杂的情况
ai_impact_artist = {
'threats': {
'routine_design': 0.6,
'stock_illustration': 0.7,
'basic_animation': 0.5,
'template_based_work': 0.8
},
'opportunities': {
'ai_augmented_creativity': 2.0,
'new_media_formats': 1.8,
'personalized_content': 1.7,
'interactive_experiences': 1.9
},
'paradigm_shifts': [
'From creation to curation and direction',
'Human-AI collaborative art',
'Real-time generative art',
'Cross-modal creative expression'
]
}
# 3. 需求预测 - 考虑颠覆性变化
print("\n=== Digital Artist Career Analysis ===")
# 使用双峰分布模拟：既有替代风险，也有创造机会
years = 10
base_demand = 50000
# 悲观情景：高替代
pessimistic = base_demand * (0.97 ** np.arange(years)) # 每年减少3%
# 乐观情景：高创造
optimistic = base_demand * (1.12 ** np.arange(years)) # 每年增长12%
# 最可能情景：混合
most_likely = []
for year in range(years):
# 早期替代效应强，后期创造效应强
if year < 3:
growth = -0.02 # 前3年下降2%
elif year < 6:
growth = 0.03 # 中间3年增长3%
else:
growth = 0.08 # 后4年增长8%
most_likely.append(base_demand * ((1 + growth) ** year))
print(f"Initial demand: {base_demand:,.0f}")
print(f"Pessimistic (10 years): {pessimistic[-1]:,.0f}")
print(f"Optimistic (10 years): {optimistic[-1]:,.0f}")
print(f"Most likely (10 years): {most_likely[-1]:,.0f}")
# 4. 教育需求分析 - 罗德岛设计学院
risd_program = {
'current_enrollment': 200,
'graduation_rate': 0.82,
'employment_rate': 0.87,
'program_duration': 4,
'capacity': 300,
'tuition': 52000
}
# 艺术教育特殊考虑
creative_industries_multiplier = 1.25 # 创意产业溢出效应
# RISD市场份额（高端艺术教育）
risd_market_share = 0.08 # 培养8%的高端数字艺术家
# 使用最可能情景
required_graduates = most_likely[-1] * risd_market_share * creative_industries_multiplier
current_graduates = risd_program['current_enrollment'] * risd_program['graduation_rate']
enrollment_change_needed = (required_graduates - current_graduates) / current_graduates
print(f"\nRISD Digital Media Program Analysis:")
print(f"Required graduates (adjusted): {required_graduates:.0f}")
print(f"Current graduates: {current_graduates:.0f}")
print(f"Enrollment change needed: {enrollment_change_needed:.1%}")
# 5. 战略决策（艺术教育需要考虑非经济因素）
cultural_value_weight = 1.5 # 文化价值权重
if enrollment_change_needed > 0.15:
recommendation = "Strategic expansion with focus on AI integration"
actions = [
"Increase enrollment to 250-280 students",
"Create AI-Art collaborative studio",
"Develop ethics and authorship curriculum",
"Establish industry partnerships with tech companies"
]
elif enrollment_change_needed > -0.05:
recommendation = "Transform rather than expand: evolve the program"
actions = [
"Maintain current enrollment (~200)",
"Radically update curriculum for Gen-AI era",
"Focus on unique human creative skills",
"Develop hybrid artist-technologist program"
]
else:
recommendation = "Consider program restructuring with adjacent fields"
actions = [
"Gradual reduction to 180 students",
"Merge with computational design program",
"Create bridge programs to gaming/VFX industries",
"Focus on graduate-level and executive education"
]
# 艺术教育的特殊考虑
artistic_considerations = [
"Preserve human creative expression",
"Balance technical skills with artistic vision",
"Address ethical issues in AI-generated art",
"Prepare for new business models in creative industries"
]
print(f"\nRecommendation: {recommendation}")
print("Recommended actions:")
for i, action in enumerate(actions, 1):
print(f" {i}. {action}")
print("\nArtistic considerations:")
for consideration in artistic_considerations:
print(f" • {consideration}")
return {
'scenarios': {
'pessimistic': pessimistic,
'optimistic': optimistic,
'most_likely': most_likely
},
'enrollment_change_needed': enrollment_change_needed,
'recommendation': recommendation,
'actions': actions,
'artistic_considerations': artistic_considerations
}
五、综合决策与实施计划
5.1 跨机构协调与资源分配
class CrossInstitutionCoordinator:
"""
跨机构协调与资源再分配模型
考虑学生流动、资源共享、专业整合
"""
def __init__(self, institutions_data):
self.institutions = institutions_data
def analyze_student_mobility(self, reduction_decisions, expansion_decisions):
"""
分析缩减专业学生向扩张专业的流动可能性
"""
mobility_matrix = np.zeros((3, 3)) # 3个专业间的流动
# 技能相似度矩阵（基于O*NET技能分析）
skill_similarity = {
('data_scientist', 'digital_artist'): 0.4, # 共同需要数据分析
('data_scientist', 'electrician'): 0.2, # 技术问题解决
('digital_artist', 'electrician'): 0.1, # 设计思维
('digital_artist', 'data_scientist'): 0.4,
('electrician', 'data_scientist'): 0.2,
('electrician', 'digital_artist'): 0.1
}
transition_paths = {}
for from_career in reduction_decisions:
for to_career in expansion_decisions:
if from_career != to_career:
similarity = skill_similarity.get((from_career, to_career), 0)
if similarity > 0.3: # 有显著技能重叠
# 可转移的学生比例
transferable_ratio = similarity * 0.7 # 最大70%
students_available = reduction_decisions[from_career]['students_affected']
potential_transfers = students_available * transferable_ratio
transition_paths[(from_career, to_career)] = {
'similarity_score': similarity,
'transferable_students': int(potential_transfers),
'bridge_courses_needed': self.identify_bridge_courses(from_career, to_career),
'transition_time': '1-2 semesters'
}
return transition_paths
def identify_bridge_courses(self, from_career, to_career):
"""识别必要的过渡课程"""
bridge_courses = {
('digital_artist', 'data_scientist'): [
'Programming Fundamentals',
'Data Visualization',
'Statistics for Creatives',
'Computational Thinking'
],
('data_scientist', 'digital_artist'): [
'Design Principles',
'Visual Communication',
'Creative Coding',
'Digital Aesthetics'
],
('electrician', 'data_scientist'): [
'Mathematics for Technology',
'Computer Fundamentals',
'Data Acquisition Systems',
'Industrial IoT'
],
('data_scientist', 'electrician'): [
'Electrical Fundamentals',
'Safety Protocols',
'Hardware Systems',
'Technical Documentation'
]
}
return bridge_courses.get((from_career, to_career), ['Cross-disciplinary Foundation'])
def optimize_resource_reallocation(self, changes_by_institution):
"""
优化资源配置：教师、设施、预算
"""
resource_reallocation = {}
for institution, changes in changes_by_institution.items():
if changes['net_change'] > 0: # 扩张
resource_needs = {
'faculty': int(changes['net_change'] / 15), # 15:1师生比
'classrooms': int(changes['net_change'] / 30), # 每班30人
'lab_space': changes['net_change'] * 2.5, # 平方米
'equipment_budget': changes['net_change'] * 5000,
'operating_budget': changes['net_change'] * 0.3 * self.institutions[institution]['cost_per_student']
}
resource_reallocation[institution] = {
'type': 'expansion',
'needs': resource_needs,
'funding_sources': self.identify_funding_sources(institution, changes['net_change'])
}
elif changes['net_change'] < 0: # 缩减
resources_freed = {
'faculty': int(abs(changes['net_change']) / 15),
'classrooms': int(abs(changes['net_change']) / 30),
'lab_space': abs(changes['net_change']) * 2.5,
'budget': abs(changes['net_change']) * 0.7 * self.institutions[institution]['cost_per_student']
}
resource_reallocation[institution] = {
'type': 'reduction',
'resources_freed': resources_freed,
'reallocation_options': self.suggest_reallocation_options(institution, resources_freed)
}
return resource_reallocation
def identify_funding_sources(self, institution, expansion_size):
"""识别扩张的资金来源"""
sources = {
'MIT_Data_Science': [
f"Industry partnerships: ${expansion_size * 10000:,.0f}",
"Research grants for AI education",
"Endowment allocation",
"Government STEM initiatives"
],
'Lincoln_Electrician': [
f"Apprenticeship sponsorships: ${expansion_size * 5000:,.0f}",
"Workforce development grants",
"Utility company partnerships",
"State vocational funding"
],
'RISD_Digital_Arts': [
f"Creative industry sponsors: ${expansion_size * 8000:,.0f}",
"Arts and culture grants",
"Technology company collaborations",
"Alumni donations"
]
}
return sources.get(institution, ["General institutional funds"])
def suggest_reallocation_options(self, institution, freed_resources):
"""建议资源重新分配方案"""
options = {
'MIT_Data_Science': [
f"Reassign {freed_resources['faculty']} faculty to AI research centers",
f"Convert {freed_resources['classrooms']} classrooms to innovation labs",
"Redirect budget to quantum computing initiative",
"Invest in industry-academia collaboration space"
],
'Lincoln_Electrician': [
f"Reassign {freed_resources['faculty']} instructors to renewable energy program",
f"Convert space to advanced robotics training center",
"Invest in virtual reality training systems",
"Develop continuing education programs"
],
'RISD_Digital_Arts': [
f"Reassign {freed_resources['faculty']} faculty to computational design",
f"Convert space to AI-art collaboration studio",
"Invest in digital fabrication equipment",
"Develop creative technology incubator"
]
}
return options.get(institution, ["General institutional reallocation"])
5.2 实施时间表与监测机制
class ImplementationTimeline:
"""
制定详细的实施时间表与监测机制
"""
def create_detailed_timeline(self, decisions_by_institution, start_year=2026):
"""
创建详细的实施时间表
"""
timeline = {}
for institution, decision in decisions_by_institution.items():
phases = []
if decision['enrollment_change'] > 0.1: # 显著扩张
phases = self.create_expansion_timeline(institution, decision, start_year)
elif decision['enrollment_change'] < -0.1: # 显著缩减
phases = self.create_reduction_timeline(institution, decision, start_year)
else: # 适度调整或维持
phases = self.create_adjustment_timeline(institution, decision, start_year)
timeline[institution] = {
'overall_strategy': decision['action'],
'phases': phases,
'key_performance_indicators': self.define_kpis(institution),
'risk_mitigation': self.identify_risks_and_mitigations(institution, decision)
}
return timeline
def create_expansion_timeline(self, institution, decision, start_year):
"""创建扩张时间表"""
phases = [
{
'phase': 'Preparation (6-12 months)',
'year': f"{start_year}-{start_year+1}",
'activities': [
'Curriculum review and update',
'Faculty recruitment planning',
'Facility assessment and planning',
'Market analysis and recruitment strategy',
'Budget planning and funding identification'
],
'milestones': [
'Updated curriculum approved',
'Faculty hiring plan finalized',
'Facility modifications planned',
'Recruitment materials developed',
'Funding secured'
]
},
{
'phase': 'Initial Implementation (12-18 months)',
'year': f"{start_year+1}-{start_year+2}",
'activities': [
'Begin faculty hiring',
'Start facility modifications',
'Launch new recruitment campaign',
'Admit first expanded cohort',
'Develop industry partnerships'
],
'milestones': [
'50% of new faculty hired',
'Facility work 75% complete',
'Application increase target met',
'First expanded cohort enrolled',
'3+ industry partnerships established'
]
},
{
'phase': 'Full Implementation (18-24 months)',
'year': f"{start_year+2}-{start_year+3}",
'activities': [
'Complete faculty hiring',
'Finalize facility improvements',
'Optimize curriculum based on feedback',
'Scale industry collaborations',
'Establish alumni mentoring program'
],
'milestones': [
'All faculty positions filled',
'Facilities fully operational',
'Curriculum adjustments implemented',
'Industry projects integrated',
'Mentorship program active'
]
},
{
'phase': 'Consolidation and Review (ongoing)',
'year': f"{start_year+3}+",
'activities': [
'Continuous program assessment',
'Annual labor market alignment review',
'Alumni outcome tracking',
'Technology integration updates',
'Strategic planning for next phase'
],
'milestones': [
'Annual assessment report',
'Employment rate targets met',
'Alumni satisfaction > 85%',
'Technology refresh cycle established',
'Next 5-year plan developed'
]
}
]
return phases
def create_reduction_timeline(self, institution, decision, start_year):
"""创建缩减/重组时间表"""
phases = [
{
'phase': 'Planning and Communication (6-9 months)',
'year': f"{start_year}",
'activities': [
'Stakeholder analysis and communication plan',
'Student transition planning',
'Faculty reassignment options',
'Resource reallocation planning',
'Alternative program development'
],
'milestones': [
'Communication plan approved',
'Transition pathways identified',
'Faculty options documented',
'Resource reallocation plan',
'New program proposals drafted'
]
},
{
'phase': 'Gradual Transition (12-18 months)',
'year': f"{start_year+1}",
'activities': [
'Begin phasing out admissions',
'Implement student transition support',
'Start faculty retraining/reassignment',
'Begin resource reallocation',
'Develop new program offerings'
],
'milestones': [
'New admissions reduced by 50%',
'Transition support services operational',
'25% of faculty transitioned',
'Initial resource reallocation complete',
'New programs approved'
]
},
{
'phase': 'Full Transition (18-24 months)',
'year': f"{start_year+2}",
'activities': [
'Complete admissions phase-out',
'Support remaining students to completion',
'Complete faculty transitions',
'Finalize resource reallocation',
'Launch new programs'
],
'milestones': [
'No new admissions',
'All students on completion track',
'Faculty transitions complete',
'Resources fully reallocated',
'New programs operational'
]
},
{
'phase': 'New Direction (24+ months)',
'year': f"{start_year+3}+",
'activities': [
'Evaluate new program success',
'Continuous market alignment',
'Optimize resource utilization',
'Build new industry partnerships',
'Establish new identity and brand'
],
'milestones': [
'New program enrollment targets met',
'Employment outcomes satisfactory',
'Resource utilization optimized',
'New partnerships established',
'Repositioning successful'
]
}
]
return phases
def define_kpis(self, institution):
"""定义关键绩效指标"""
kpis = {
'all_institutions': [
{'metric': 'Enrollment vs Target', 'target': '±5%', 'frequency': 'Semester'},
{'metric': 'Student Retention Rate', 'target': '>85%', 'frequency': 'Annual'},
{'metric': 'Graduation Rate', 'target': '>80%', 'frequency': 'Annual'},
{'metric': 'Employment Rate (6 months)', 'target': '>85%', 'frequency': 'Annual'},
{'metric': 'Employer Satisfaction', 'target': '>4.0/5.0', 'frequency': 'Annual'},
{'metric': 'Student Satisfaction', 'target': '>4.0/5.0', 'frequency': 'Annual'}
]
}
# 机构特定KPI
institution_specific = {
'MIT_Data_Science': [
{'metric': 'AI Integration in Curriculum', 'target': '100% of courses', 'frequency': 'Annual'},
{'metric': 'Industry Research Partnerships', 'target': '10+ active', 'frequency': 'Annual'},
{'metric': 'Patent/Publication Output', 'target': '20+ per year', 'frequency': 'Annual'}
],
'Lincoln_Electrician': [
{'metric': 'Hands-on Training Hours', 'target': '>500 hours/student', 'frequency': 'Annual'},
{'metric': 'Industry Certification Rate', 'target': '>90%', 'frequency': 'Annual'},
{'metric': 'Apprenticeship Placement', 'target': '100%', 'frequency': 'Annual'}
],
'RISD_Digital_Arts': [
{'metric': 'Portfolio Quality Score', 'target': '>4.5/5.0', 'frequency': 'Annual'},
{'metric': 'Exhibition/Presentation Rate', 'target': '>80% of students', 'frequency': 'Annual'},
{'metric': 'Creative Technology Integration', 'target': '100% of studios', 'frequency': 'Annual'}
]
}
return kpis['all_institutions'] + institution_specific.get(institution, [])
def identify_risks_and_mitigations(self, institution, decision):
"""识别风险并制定缓解策略"""
risks = {
'expansion_risks': [
{
'risk': 'Quality dilution with rapid expansion',
'probability': 'Medium',
'impact': 'High',
'mitigation': 'Phased expansion with quality gates; maintain faculty-student ratios'
},
{
'risk': 'Insufficient funding for expansion',
'probability': 'Medium',
'impact': 'High',
'mitigation': 'Diversified funding strategy; phased implementation based on funding'
},
{
'risk': 'Market saturation if over-expansion',
'probability': 'Low',
'impact': 'Medium',
'mitigation': 'Continuous market monitoring; flexible enrollment adjustments'
}
],
'reduction_risks': [
{
'risk': 'Negative impact on institutional reputation',
'probability': 'Medium',
'impact': 'High',
'mitigation': 'Proactive communication; emphasize strategic repositioning'
},
{
'risk': 'Faculty morale and retention issues',
'probability': 'High',
'impact': 'Medium',
'mitigation': 'Clear transition support; retraining opportunities; alternative roles'
},
{
'risk': 'Current student dissatisfaction',
'probability': 'Medium',
'impact': 'Medium',
'mitigation': 'Guaranteed completion pathways; enhanced support services'
}
]
}
if decision['enrollment_change'] > 0:
return risks['expansion_risks']
else:
return risks['reduction_risks']
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