研究文章
潜在的结果相互关联的干预对系统性风险(COVID-19)通过一个模型驱动的Network-Agent动态
|
| 为我在范围(n): |
#循环t回路中的每一个人 |
| # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
|
| #勘探 |
|
| # = = = = = = = = = = = = |
|
| temp = B (:, 1:3) |
#一个临时变量保存策略 |
| R3 = np.random.random(大小= (n2)< = (0.5pe) |
#随机选择一个特定的条件(%) |
| 临时(R3) + = np.random。normal (mu, sigma, size = [n2)(R3) |
#正态分布增量 |
| B: 1:3 = temp |
#更新策略值 |
|
|
