import numpy as np
import matplotlib.pyplot as plt
from collections import defaultdict

class VaccinatedEscapeModel:
"""包含疫苗化机制的逃逸模型"""

def __init__(self, n_individuals=1000):
self.n = n_individuals

# 个体特征
self.sensitivity = np.random.normal(0.5, 0.2, n_individuals)
self.sensitivity = np.clip(self.sensitivity, 0.1, 0.9)

# 逃逸策略强度
self.h_V = np.zeros(n_individuals) # 价值内化
self.h_A = np.zeros(n_individuals) # 去中介化
self.h_N = np.zeros(n_individuals) # 情感连接

# 策略"纯度"（未被疫苗化的程度）
self.purity_V = np.ones(n_individuals) # 1.0=纯内化，0.0=被鸡汤化
self.purity_A = np.ones(n_individuals)
self.purity_N = np.ones(n_individuals)

# 个体状态
self.W_E = np.ones(n_individuals) # 价值感
self.true_W_E = np.ones(n_individuals) # 真实价值感（未被疫苗化稀释）
self.pain = np.zeros(n_individuals)
self.escaped = np.zeros(n_individuals, dtype=bool)
self.escape_time = np.zeros(n_individuals) # 逃逸时间

# 系统参数
self.V_C = 1.0 # 表面意识形态（变得"柔软"）
self.V_C_true = 1.0 # 真实意识形态（继续固化）
self.A_C = 1.0 # 吸引效率
self.I_E_system = 1.0

# 疫苗化状态
self.vaccine_level = 0.0 # 系统对逃逸策略的编码程度
self.vaccine_history = []

# 策略有效性追踪
self.strategy_effectiveness = {
'价值内化': [],
'去中介化': [],
'情感连接': []
}

# 阈值
self.pain_threshold = 0.8
self.vaccine_threshold = 0.1 # 逃逸比例多少开始疫苗化

def get_strategy_type(self, i):
"""判断个体的主要逃逸策略"""
if self.h_V[i] > max(self.h_A[i], self.h_N[i]):
return '价值内化'
elif self.h_A[i] > max(self.h_V[i], self.h_N[i]):
return '去中介化'
else:
return '情感连接'

def update_vaccination(self, dt=0.1):
"""系统疫苗化逃逸策略"""
escape_ratio = np.mean(self.escaped)

if escape_ratio > self.vaccine_threshold:
# 疫苗化速度：与逃逸比例和系统效率正相关
vaccine_rate = 0.2 * escape_ratio * (self.A_C - 0.8)
vaccine_rate = max(vaccine_rate, 0)

# 关键机制：疫苗化使系统表面变得"柔软"
# 1. V_C 表面值变得"包容"（实际是伪包容）
self.V_C = min(self.V_C + 0.8 * vaccine_rate * dt, 3.0)

# 2. 但真实意识形态继续固化（这才是系统的本质）
self.V_C_true = min(self.V_C_true + 0.1 * vaccine_rate * dt, 5.0)

# 3. 系统吸引效率反而提高（学会了如何"温柔地"吸引）
self.A_C = min(self.A_C + 0.3 * vaccine_rate * dt, 2.5)

self.vaccine_level += vaccine_rate * dt
self.vaccine_history.append(self.vaccine_level)

# 疫苗化对逃逸者的影响
for i in range(self.n):
if self.escaped[i]:
time_exposed = self.escape_time[i] # 逃逸时间越长，被疫苗化越深

# 不同类型的策略被疫苗化的方式不同
strat_type = self.get_strategy_type(i)

if strat_type == '价值内化':
# 被"鸡汤化"：将深刻的价值内化简化为消费主义口号
# "做自己" -> "买这个产品做更好的自己"
decay = 0.4 * vaccine_rate * (1 + 0.1 * time_exposed)
self.purity_V[i] *= (1 - decay * dt)
self.h_V[i] *= (1 - 0.1 * decay * dt) # 策略强度也衰减

elif strat_type == '去中介化':
# 被"平台化"：去中心化连接被大平台重新中介化
# 独立社群 -> 微信群/小红书话题
decay = 0.3 * vaccine_rate * (1 + 0.05 * time_exposed)
self.purity_A[i] *= (1 - decay * dt)
self.h_A[i] *= (1 - 0.2 * decay * dt)

else: # 情感连接
# 被"搭子经济化"：真诚连接变为功能化社交
# 深度友谊 -> "饭搭子""旅游搭子"
decay = 0.5 * vaccine_rate * (1 + 0.15 * time_exposed)
self.purity_N[i] *= (1 - decay * dt)
self.h_N[i] *= (1 - 0.15 * decay * dt)

else:
self.vaccine_history.append(self.vaccine_level)

def update_individual(self, i, dt=0.1):
"""更新个体（考虑策略纯度）"""

# 1. 计算痛苦（现在考虑真实意识形态）
connection_buffer = self.h_N[i] * self.purity_N[i] * np.sum(
self.h_N * self.purity_N) / max(self.n, 1)

# 痛苦来源：真实意识形态V_C_true，而不是表面V_C
system_pain = max(self.V_C_true - 0.5, 0)
self.pain[i] = system_pain * (1 - self.h_V[i] * self.purity_V[i]) - connection_buffer
self.pain[i] = max(self.pain[i], 0)

# 2. 逃逸决策
if self.pain[i] > self.pain_threshold and not self.escaped[i]:
if self.sensitivity[i] > 0.6:
self.h_V[i] = min(self.h_V[i] + 0.4, 1.0)
self.purity_V[i] = 1.0
elif self.sensitivity[i] > 0.4:
self.h_A[i] = min(self.h_A[i] + 0.3, 1.0)
self.purity_A[i] = 1.0
else:
self.h_N[i] = min(self.h_N[i] + 0.5, 1.0)
self.purity_N[i] = 1.0

self.escaped[i] = True
self.escape_time[i] = 0

if self.escaped[i]:
self.escape_time[i] += dt

# 3. 更新价值感（考虑策略纯度）
if self.escaped[i]:
# 有效策略强度 = 名义强度 × 纯度
effective_h_V = self.h_V[i] * self.purity_V[i]
effective_h_N = self.h_N[i] * self.purity_N[i]

# 内源性价值增长（受纯度影响）
internal_growth = 0.12 * effective_h_V * self.true_W_E[i] * (
1 - self.true_W_E[i]/6.0)

# 外部侵蚀：面对真实意识形态，但被有效策略缓冲
external_erosion = 0.07 * self.V_C_true / (
1 + 2 * effective_h_V) * self.W_E[i]

# 真实价值感（未被疫苗化稀释）
d_true_W = internal_growth - 0.02 * (1 - self.purity_V[i]) * self.true_W_E[i]
self.true_W_E[i] = max(self.true_W_E[i] + d_true_W * dt, 0.1)

# 表面价值感（可能因疫苗化而虚高）
social_recognition = 0.05 * (1 - self.purity_V[i]) * self.V_C # 鸡汤化带来的虚假认可
d_W = internal_growth - external_erosion + social_recognition
self.W_E[i] = max(self.W_E[i] + d_W * dt, 0.1)

# 策略纯度随时间自然衰减（即使没有疫苗化）
decay_natural = 0.01 * dt
self.purity_V[i] *= (1 - decay_natural)
self.purity_A[i] *= (1 - decay_natural * 0.8)
self.purity_N[i] *= (1 - decay_natural * 1.2)

else:
# 未逃逸者：被真实意识形态侵蚀
d_W = -0.1 * self.V_C_true * self.W_E[i]
self.W_E[i] = max(self.W_E[i] + d_W * dt, 0.1)
self.true_W_E[i] = self.W_E[i] # 未逃逸者没有"真实vs表面"之分

def update_system(self, dt=0.1):
"""系统对剩余人群的优化"""
escape_ratio = np.mean(self.escaped)

if escape_ratio > 0:
# 剩余竞争者加大投入
self.I_E_system *= (1 + 0.25 * escape_ratio * dt)

# 系统优化对未疫苗化人群的效率
unvaccinated_ratio = np.mean([p for p in self.purity_V if p > 0.9])
dA = 0.12 * escape_ratio * (1 - unvaccinated_ratio) * (2.2 - self.A_C)
self.A_C = min(self.A_C + dA * dt, 2.2)

# 真实意识形态自然固化
self.V_C_true = min(self.V_C_true * (1 + 0.008 * dt), 6.0)

# 定期增加系统压力
if np.random.random() < 0.02:
self.V_C_true *= 1.1

def calculate_strategy_effectiveness(self):
"""计算各策略当前的平均有效性"""
effects = defaultdict(list)

for i in range(self.n):
if self.escaped[i]:
strat_type = self.get_strategy_type(i)

# 有效性 = 价值感提升 × 策略纯度
if strat_type == '价值内化':
effectiveness = (self.true_W_E[i] - 1.0) * self.purity_V[i]
effects['价值内化'].append(effectiveness)
elif strat_type == '去中介化':
effectiveness = (self.true_W_E[i] - 1.0) * self.purity_A[i]
effects['去中介化'].append(effectiveness)
else:
effectiveness = (self.true_W_E[i] - 1.0) * self.purity_N[i]
effects['情感连接'].append(effectiveness)

# 记录
for strat in ['价值内化', '去中介化', '情感连接']:
if effects[strat]:
self.strategy_effectiveness[strat].append(np.mean(effects[strat]))
else:
self.strategy_effectiveness[strat].append(0.0)

def run(self, T=200):
"""运行模拟"""
metrics = {
'escape_ratio': [],
'avg_true_pain': [],
'avg_surface_pain': [],
'vaccine_level': [],
'V_C': [],
'V_C_true': [],
'A_C': [],
'avg_true_W_E_escaped': [],
'avg_surface_W_E_escaped': [],
'avg_W_E_trapped': [],
'avg_purity': []
}

for t in range(T):
# 系统疫苗化（先于个体更新）
self.update_vaccination()

# 更新所有个体
for i in range(self.n):
self.update_individual(i)

# 更新系统
self.update_system()

# 计算策略有效性
self.calculate_strategy_effectiveness()

# 记录指标
metrics['escape_ratio'].append(np.mean(self.escaped))
metrics['avg_true_pain'].append(np.mean(self.pain))

# 计算表面痛苦（基于V_C而不是V_C_true）
surface_pain = np.mean([
max(self.V_C - 0.5, 0) * (1 - self.h_V[i] * self.purity_V[i]) -
self.h_N[i] * self.purity_N[i] * 0.1
for i in range(self.n)
])
metrics['avg_surface_pain'].append(max(surface_pain, 0))

metrics['vaccine_level'].append(self.vaccine_level)
metrics['V_C'].append(self.V_C)
metrics['V_C_true'].append(self.V_C_true)
metrics['A_C'].append(self.A_C)

# 逃逸者价值感（真实vs表面）
escaped_idx = np.where(self.escaped)[0]
if len(escaped_idx) > 0:
metrics['avg_true_W_E_escaped'].append(np.mean(self.true_W_E[escaped_idx]))
metrics['avg_surface_W_E_escaped'].append(np.mean(self.W_E[escaped_idx]))
else:
metrics['avg_true_W_E_escaped'].append(1.0)
metrics['avg_surface_W_E_escaped'].append(1.0)

# 被困者价值感
trapped_idx = np.where(~self.escaped)[0]
if len(trapped_idx) > 0:
metrics['avg_W_E_trapped'].append(np.mean(self.W_E[trapped_idx]))
else:
metrics['avg_W_E_trapped'].append(0.0)

# 平均策略纯度
if len(escaped_idx) > 0:
avg_purity = np.mean([
(self.purity_V[i] + self.purity_A[i] + self.purity_N[i]) / 3
for i in escaped_idx
])
metrics['avg_purity'].append(avg_purity)
else:
metrics['avg_purity'].append(1.0)

return metrics

# 运行模拟
print("="*70)
print("疫苗化机制模拟：系统如何吸收、编码并反向利用逃逸策略")
print("="*70)

model = VaccinatedEscapeModel(n_individuals=800)
metrics = https://t.co/7KP55kFHh3(T=180)

# 可视化结果
fig, axes = plt.subplots(3, 3, figsize=(16, 14))

# 图1: 逃逸比例与疫苗化水平
ax1 = axes[0, 0]
ax1.plot(metrics['escape_ratio'], 'b-', label='逃逸比例', linewidth=2)
ax1.plot(metrics['vaccine_level'], 'r--', label='疫苗化水平', linewidth=2)
ax1.axhline(y=0.1, color='gray', linestyle=':', alpha=0.5, label='疫苗化阈值')
ax1.set_xlabel('时间')
ax1.set_ylabel('比例/水平')
ax1.set_title('逃逸比例 vs 疫苗化水平')
ax1.legend()
ax1.grid(True, alpha=0.3)

# 图2: 真实vs表面意识形态
ax2 = axes[0, 1]
ax2.plot(metrics['V_C'], 'orange', label='V_C (表面: "包容""柔软")', linewidth=2, alpha=0.8)
ax2.plot(metrics['V_C_true'], 'darkred', label='V_C_true (真实: 继续固化)', linewidth=2)
ax2.set_xlabel('时间')
ax2.set_ylabel('意识形态强度')
ax2.set_title('意识形态分裂: 表面柔软 vs 真实固化')
ax2.legend()
ax2.grid(True, alpha=0.3)

# 图3: 吸引效率提升
ax3 = axes[0, 2]
ax3.plot(metrics['A_C'], 'purple', linewidth=2)
ax3.set_xlabel('时间')
ax3.set_ylabel('A_C')
ax3.set_title('系统吸引效率 (因疫苗化而提升)')
ax3.grid(True, alpha=0.3)

# 图4: 真实痛苦vs表面痛苦
ax4 = axes[1, 0]
ax4.plot(metrics['avg_true_pain'], 'red', label='真实痛苦 (基于V_C_true)', linewidth=2)
ax4.plot(metrics['avg_surface_pain'], 'pink', label='表面痛苦 (基于V_C)', linewidth=2, alpha=0.7)
ax4.axhline(y=model.pain_threshold, color='black', linestyle='--', alpha=0.5, label='痛苦阈值')
ax4.set_xlabel('时间')
ax4.set_ylabel('痛苦水平')
ax4.set_title('痛苦感知分裂: 疫苗化制造假象')
ax4.legend()
ax4.grid(True, alpha=0.3)

# 图5: 价值感对比（关键图）
ax5 = axes[1, 1]
ax5.plot(metrics['avg_true_W_E_escaped'], 'darkgreen',
label='逃逸者真实价值感', linewidth=2.5)
ax5.plot(metrics['avg_surface_W_E_escaped'], 'lightgreen',
label='逃逸者表面价值感', linewidth=2, alpha=0.7)
ax5.plot(metrics['avg_W_E_trapped'], 'red',
label='被困者价值感', linewidth=2)
ax5.set_xlabel('时间')
ax5.set_ylabel('价值感')
ax5.set_title('价值感分裂: 真实vs表面 (疫苗化效果)')
ax5.legend()
ax5.grid(True, alpha=0.3)

# 图6: 策略纯度衰减
ax6 = axes[1, 2]
ax6.plot(metrics['avg_purity'], 'brown', linewidth=2)
ax6.set_xlabel('时间')
ax6.set_ylabel('平均纯度')
ax6.set_title('逃逸策略纯度衰减 (被疫苗化稀释)')
ax6.grid(True, alpha=0.3)

# 图7: 各策略有效性衰减
ax7 = axes[2, 0]
for strat, effectiveness in model.strategy_effectiveness.items():
ax7.plot(effectiveness, label=strat, linewidth=2)
ax7.set_xlabel('时间')
ax7.set_ylabel('策略有效性')
ax7.set_title('逃逸策略有效性随时间衰减 (疫苗化作用)')
ax7.legend()
ax7.grid(True, alpha=0.3)

# 图8: 最终时刻的策略纯度分布
escaped_idx = np.where(model.escaped)[0]
if len(escaped_idx) > 0:
purities_V = [model.purity_V[i] for i in escaped_idx]
purities_A = [model.purity_A[i] for i in escaped_idx]
purities_N = [model.purity_N[i] for i in escaped_idx]

ax8 = axes[2, 1]
bins = np.linspace(0, 1, 11)
ax8.hist(purities_V, bins=bins, alpha=0.5, label='价值内化纯度', color='blue')
ax8.hist(purities_A, bins=bins, alpha=0.5, label='去中介化纯度', color='green')
ax8.hist(purities_N, bins=bins, alpha=0.5, label='情感连接纯度', color='red')
ax8.set_xlabel('策略纯度')
ax8.set_ylabel('人数')
ax8.set_title(f'最终时刻策略纯度分布 (逃逸者数: {len(escaped_idx)})')
ax8.legend()
else:
axes[2, 1].text(0.5, 0.5, '无逃逸者', ha='center', va='center')
axes[2, 1].set_title('策略纯度分布')

# 图9: 系统指标总结
ax9 = axes[2, 2]
summary_text = f"""
最终逃逸比例: {metrics['escape_ratio'][-1]:.2%}
疫苗化水平: {metrics['vaccine_level'][-1]:.3f}
真实意识形态 V_C_true: {metrics['V_C_true'][-1]:.3f}
表面意识形态 V_C: {metrics['V_C'][-1]:.3f}
吸引效率 A_C: {metrics['A_C'][-1]:.3f}

逃逸者真实价值感: {metrics['avg_true_W_E_escaped'][-1]:.3f}
逃逸者表面价值感: {metrics['avg_surface_W_E_escaped'][-1]:.3f}
被困者价值感: {metrics['avg_W_E_trapped'][-1]:.3f}

策略平均纯度: {metrics['avg_purity'][-1]:.3f}
真实痛苦水平: {metrics['avg_true_pain'][-1]:.3f}
表面痛苦水平: {metrics['avg_surface_pain'][-1]:.3f}
"""
ax9.text(0.05, 0.95, summary_text, fontsize=10, verticalalignment='top',
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))
ax9.axis('off')
ax9.set_title('系统最终状态总结')

plt.suptitle('疫苗化机制: 系统如何温柔地消解逃逸策略', fontsize=16, y=1.02)
plt.tight_layout()
https://t.co/tIyPsTBPeQ()

# 关键分析
print("\n" + "="*70)
print("疫苗化机制的关键发现")
print("="*70)

# 计算疫苗化前后对比
early_phase = 30
late_phase = -30

escape_early = np.mean(metrics['escape_ratio'][:early_phase])
escape_late = np.mean(metrics['escape_ratio'][late_phase:])

true_pain_early = np.mean(metrics['avg_true_pain'][:early_phase])
true_pain_late = np.mean(metrics['avg_true_pain'][late_phase:])

surface_pain_early = np.mean(metrics['avg_surface_pain'][:early_phase])
surface_pain_late = np.mean(metrics['avg_surface_pain'][late_phase:])

true_W_E_early = np.mean(metrics['avg_true_W_E_escaped'][:early_phase])
true_W_E_late = np.mean(metrics['avg_true_W_E_escaped'][late_phase:])

surface_W_E_early = np.mean(metrics['avg_surface_W_E_escaped'][:early_phase])
surface_W_E_late = np.mean(metrics['avg_surface_W_E_escaped'][late_phase:])

print(f"1. 疫苗化前后逃逸比例变化: {escape_early:.1%} → {escape_late:.1%}")
print(f" 增长幅度: {(escape_late/escape_early-1):+.1%}")

print(f"\n2. 痛苦感知分裂:")
print(f" 真实痛苦: {true_pain_early:.3f} → {true_pain_late:.3f} (变化: {true_pain_late-true_pain_early:+.3f})")
print(f" 表面痛苦: {surface_pain_early:.3f} → {surface_pain_late:.3f} (变化: {surface_pain_late-surface_pain_early:+.3f})")
print(f" 痛苦分裂度: {abs(true_pain_late - surface_pain_late):.3f}")

print(f"\n3. 价值感分裂（逃逸者）:")
print(f" 真实价值感: {true_W_E_early:.3f} → {true_W_E_late:.3f} (变化: {true_W_E_late-true_W_E_early:+.3f})")
print(f" 表面价值感: {surface_W_E_early:.3f} → {surface_W_E_late:.3f} (变化: {surface_W_E_late-surface_W_E_early:+.3f})")
print(f" 价值分裂度: {abs(true_W_E_late - surface_W_E_late):.3f}")

print(f"\n4. 意识形态分裂:")
V_C_diff = metrics['V_C'][-1] - metrics['V_C_true'][-1]
print(f" 表面V_C ({metrics['V_C'][-1]:.3f}) - 真实V_C_true ({metrics['V_C_true'][-1]:.3f}) = {V_C_diff:.3f}")
print(f" 分裂方向: {'表面更"柔软"' if V_C_diff > 0 else '真实更固化'}")

# 计算策略有效性衰减
print(f"\n5. 策略有效性衰减:")
for strat in model.strategy_effectiveness:
eff = model.strategy_effectiveness[strat]
if len(eff) > 20:
decay = (eff[-1] - eff[20]) / max(eff[20], 0.001)
print(f" {strat}: {eff[20]:.3f} → {eff[-1]:.3f} (衰减: {decay:.1%})")

print(f"\n6. 系统效率提升:")
A_C_growth = (metrics['A_C'][-1] - metrics['A_C'][0]) / metrics['A_C'][0]
print(f" A_C 增长: {metrics['A_C'][0]:.3f} → {metrics['A_C'][-1]:.3f} (+{A_C_growth:.1%})")
print(f" 疫苗化贡献: 估计{min(A_C_growth*0.6, 1.0):.1%} 来自对逃逸策略的吸收")

print("\n" + "="*70) December 12, 2025

インディヴィのバイカラーカーディガン、めっちゃ綺麗なのが出てるよ！金ボタンが上品で俺も欲しくなっちゃった😅

【極美品】インディヴィ（INDIVI ）バイカラー クルーネックカーディガン M
https://t.co/VcyGUAwF3q December 12, 2025