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🌐 [translation-sync] [uncertainty_traps.md] Update np.random → Generator API #214

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🌐 [translation-sync] [uncertainty_traps.md] Update np.random → Generator API #214

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7608ea4
Update translation: lectures/uncertainty_traps.md
mmcky Jul 31, 2026
e70059b
Update translation: .translate/state/uncertainty_traps.md.yml
mmcky Jul 31, 2026
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10 changes: 5 additions & 5 deletions .translate/state/uncertainty_traps.md.yml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
source-sha: a1cd90c5ef6d6f05277318f47fe21eda60fa5e6b
synced-at: "2025-06-17"
model: unknown
mode: RESYNC
source-sha: b90d96d6aa7cda663577180e8e366aee5bb04a7e
synced-at: "2026-07-31"
model: claude-sonnet-5
mode: UPDATE
section-count: 5
tool-version: 0.20.0
tool-version: 0.24.0
22 changes: 11 additions & 11 deletions lectures/uncertainty_traps.md
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Original file line number Diff line number Diff line change
Expand Up @@ -291,15 +291,15 @@ class UncertaintyTrapEcon:
"""
self.θ = self.ρ * self.θ + self.σ_θ * w

def gen_aggregates(self):
def gen_aggregates(self, rng):
"""
基于当前信念(μ, γ)生成总量。这是一个
依赖于F抽样的模拟步骤。
"""
F_vals = self.σ_F * np.random.randn(self.num_firms)
F_vals = self.σ_F * rng.standard_normal(self.num_firms)
M = np.sum(self.ψ(F_vals) > 0) # 计算活跃企业数量
if M > 0:
x_vals = self.θ + self.σ_x * np.random.randn(M)
x_vals = self.θ + self.σ_x * rng.standard_normal(M)
X = x_vals.mean()
else:
X = 0
Expand Down Expand Up @@ -404,7 +404,7 @@ $$

这里的 $M$ 是活跃企业的数量。下图在45度图上绘制了不同 $M$ 值下 $\gamma_{t+1}$ 对 $\gamma_t$ 的关系

```{code-cell} ipython3
```{code-cell} python3
econ = UncertaintyTrapEcon()
ρ, σ_θ, γ_x = econ.ρ, econ.σ_θ, econ.γ_x # 简化名称
γ = np.linspace(1e-10, 3, 200) # γ 网格
Expand All @@ -426,7 +426,7 @@ plt.show()

接下来让我们生成信念和总量的时间序列数据——即活跃企业数量和平均产出

```{code-cell} ipython3
```{code-cell} python3
sim_length=2000

μ_vec = np.empty(sim_length)
Expand All @@ -439,10 +439,11 @@ M_vec = np.empty(sim_length)
γ_vec[0] = econ.γ
θ_vec[0] = 0

w_shocks = np.random.randn(sim_length)
rng = np.random.default_rng()
w_shocks = rng.standard_normal(sim_length)

for t in range(sim_length-1):
X, M = econ.gen_aggregates()
X, M = econ.gen_aggregates(rng)
X_vec[t] = X
M_vec[t] = M

Expand All @@ -454,14 +455,14 @@ for t in range(sim_length-1):
θ_vec[t+1] = econ.θ

# 记录总量的最终值
X, M = econ.gen_aggregates()
X, M = econ.gen_aggregates(rng)
X_vec[-1] = X
M_vec[-1] = M
```

首先，让我们看看在这些模拟中 $\mu$ 是如何跟踪 $\theta$ 的

```{code-cell} ipython3
```{code-cell} python3
fig, ax = plt.subplots(figsize=(9, 6))
ax.plot(range(sim_length), θ_vec, alpha=0.6, lw=2, label=r"$\theta$")
ax.plot(range(sim_length), μ_vec, alpha=0.6, lw=2, label=r"$\mu$")
Expand All @@ -472,7 +473,7 @@ plt.show()

现在让我们把所有内容一起绘制出来

```{code-cell} ipython3
```{code-cell} python3
fig, axes = plt.subplots(4, 1, figsize=(12, 20))
# 添加一些间距
fig.subplots_adjust(hspace=0.3)
Expand Down Expand Up @@ -504,4 +505,3 @@ plt.show()

```{solution-end}
```

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