包含100个参与者的随机演化博弈模型,每个参与者可以选择合作或竞争策略。通过模拟参与者之间的交互和策略更新过程,代码输出了最终的策略分布情况。
import numpy as np
# 定义参与者和策略
num_players = 100 # 参与者数量
strategies = ['合作', '竞争'] # 策略列表
# 初始化策略分布
strategy_distribution = np.random.choice(strategies, size=num_players)
# 定义收益矩阵
payoff_matrix = {
('合作', '合作'): (3, 3),
('合作', '竞争'): (0, 5),
('竞争', '合作'): (5, 0),
('竞争', '竞争'): (1, 1)
}
# 模拟演化过程
num_iterations = 1000 # 迭代次数
for iteration in range(num_iterations):
# 随机选择两个参与者进行交互
player1 = np.random.randint(num_players)
player2 = np.random.randint(num_players)
# 获取参与者的策略
strategy1 = strategy_distribution[player1]
strategy2 = strategy_distribution[player2]
# 计算收益
payoff1, payoff2 = payoff_matrix[(strategy1, strategy2)]
# 更新策略(这里使用简单的模仿更新规则)
if payoff1 > payoff2:
strategy_distribution[player2] = strategy1
else:
strategy_distribution[player1] = strategy2
# 输出最终的策略分布
cooperation_count = np.sum(strategy_distribution == '合作')
competition_count = num_players - cooperation_count
print("合作策略数量:", cooperation_count)
print("竞争策略数量:", competition_count)
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