(test) [cagan_adaptive] DRAFT report from tool-onboarding (action-translation)

[mmcky]

cagan_adaptive.md

Property	Value
Source	lecture-python-intro
Target	lecture-intro.zh-cn
Source Date	2024-07-19
Target Date	2025-03-12
Direction	← Target newer
Status	⚠️ Review
Recommendation	MANUAL REVIEW (complex changes detected)

---

Summary

8 aligned, 12 issues in 4 sections

Section	Type	Status	Issue	Links
§1	Section	✅	-	-
§2	Section	✅	-	-
§3	Section	✅	-	-
§4	Title	⚠️ DIFFERS	Differs	View / Action
§5	Title	⚠️ DIFFERS	Differs	View / Action
§5	Content	⚠️ DIFFERS	Differs	View / Action
§5	Code B1	⚠️ DIFFERS	Function names	View / Action
§5	Code B2	⚠️ DIFFERS	Code differs	View / Action
§5	Code B3	⚠️ DIFFERS	Function names	View / Action
§6	Code B4	⚠️ DIFFERS	Code differs	View / Action
§7	Content	⚠️ DIFFERS	Differs	View / Action
§7	Code T7	🔵 INSERTED	Extra	View / Action
§7	Code B5	⚠️ DIFFERS	Code differs	View / Action
§7	Code T8	🔵 INSERTED	Extra	View / Action
§7	Code B6	⚠️ DIFFERS	Code differs	View / Action
§8	Section	✅	-	-
§9	Section	✅	-	-
§10	Section	✅	-	-
§11	Section	✅	-	-
§12	Section	✅	-	-

Code Action

Source	Target	Status	Recommendation	Details
1	1	⚠️ DIFFERS	BACKPORT	View
2	2	⚠️ DIFFERS	BACKPORT	View
3	3	⚠️ DIFFERS	BACKPORT	View
4	4	⚠️ DIFFERS	BACKPORT	View
-	7	🔵 INSERTED	BACKPORT	View
5	5	⚠️ DIFFERS	BACKPORT	View
-	8	🔵 INSERTED	BACKPORT	View
6	6	⚠️ DIFFERS	BACKPORT	View

Recommendation: BACKPORT

Target code appears improved. Review for backport to source.

Action:

• SYNC code from source
• BACKPORT improvements to source (unanimous)
• ACCEPT current target code
• MANUAL review needed

---

Section Review

📅 Target is newer (2025-03-12 vs 2024-07-19) - translation may contain improvements

§4 Harvesting insights from our matrix formulation

§4 Title: Harvesting insights from our matrix formulation → 求解模型

Status: ⚠️ DIFFERS | Issue: Differs

Notes:

• Issue: "Harvesting insights from our matrix formulation" is metaphorical and exploratory, while "求解模型" (Solving the model) is more direct and procedural, losing the nuance of extracting understanding from the mathematical structure
• Fix: Consider "从矩阵表达式中获得洞见" or "矩阵公式推导及求解" to better capture the original emphasis on gaining insights through the formulation process

Action:

• SYNC (recommended)
• BACKPORT
• ACCEPT
• MANUAL

§5 Forecast errors and model computation

§5 Title: Forecast errors and model computation → 预期与实际通货膨胀的差异

Status: ⚠️ DIFFERS | Issue: Differs

Notes:

• Issue: "Forecast errors and model computation" encompasses both the conceptual issue of forecast errors and computational aspects, while "预期与实际通货膨胀的差异" (Differences between expected and actual inflation) focuses only on one aspect
• Fix: Use "预测误差与模型计算" to maintain both concepts

Action:

• SYNC
• BACKPORT
• ACCEPT
• MANUAL (recommended)

§5 Content: Forecast errors and model computation → 预期与实际通货膨胀的差异

Status: ⚠️ DIFFERS | Issue: Differs

Notes:

• Issue: Target adds extensive explanatory paragraphs (explaining adaptive expectations characteristics, contrasting with rational expectations) that are not present in the concise source text
• Fix: Remove the added interpretive paragraphs to match the source's brevity and direct transition to code

Action:

• SYNC
• BACKPORT
• ACCEPT
• MANUAL (recommended)

Code Block 1 - ⚠️ DIFFERS | Function names differ

Source:

Cagan_Adaptive = namedtuple("Cagan_Adaptive", 
                        ["α", "m0", "Eπ0", "T", "λ"])

def create_cagan_adaptive_model(α = 5, m0 = 1, Eπ0 = 0.5, T=80, λ = 0.9):
    return Cagan_Adaptive(α, m0, Eπ0, T, λ)

md = create_cagan_adaptive_model()

Target:

Cagan_Adaptive = namedtuple("Cagan_Adaptive", 
                        ["α", "m0", "Eπ0", "T", "λ"])

def create_cagan_model(α, m0, Eπ0, T, λ):
    return Cagan_Adaptive(α, m0, Eπ0, T, λ)

Code Block 2 - ⚠️ DIFFERS | Code logic differs

Source:

def solve_cagan_adaptive(model, μ_seq):
    " Solve the Cagan model in finite time. "
    α, m0, Eπ0, T, λ = model
    
    A = np.eye(T+2, T+2) - λ*np.eye(T+2, T+2, k=-1)
    B = np.eye(T+2, T+1, k=-1)
    C = -α*np.eye(T+1, T+2) + α*np.eye(T+1, T+2, k=1)
    Eπ0_seq = np.append(Eπ0, np.zeros(T+1))

    # Eπ_seq is of length T+2
    Eπ_seq = np.linalg.solve(A - (1-λ)*B @ C, (1-λ) * B @ μ_seq + Eπ0_seq)

    # π_seq is of length T+1
    π_seq = μ_seq + C @ Eπ_seq

    D = np.eye(T+1, T+1) - np.eye(T+1, T+1, k=-1) # D is the coefficient matrix in Equation (14.8)
    m0_seq = np.append(m0, np.zeros(T))

    # m_seq is of length T+2
    m_seq = np.linalg.solve(D, μ_seq + m0_seq)
    m_seq = np.append(m0, m_seq)

    # p_seq is of length T+2
    p_seq = m_seq + α * Eπ_seq

    return π_seq, Eπ_seq, m_seq, p_seq

Target:

# 参数
T = 80
T1 = 60
α = 5
λ = 0.9
m0 = 1

μ0 = 0.5
μ_star = 0

md = create_cagan_model(α=α, m0=m0, Eπ0=μ0, T=T, λ=λ)

Code Block 3 - ⚠️ DIFFERS | Function names differ

Source:

def solve_and_plot(model, μ_seq):
    
    π_seq, Eπ_seq, m_seq, p_seq = solve_cagan_adaptive(model, μ_seq)
    
    T_seq = range(model.T+2)
    
    fig, ax = plt.subplots(5, 1, figsize=[5, 12], dpi=200)
    ax[0].plot(T_seq[:-1], μ_seq)
    ax[1].plot(T_seq[:-1], π_seq, label=r'$\pi_t$')
    ax[1].plot(T_seq, Eπ_seq, label=r'$\pi^{*}_{t}$')
    ax[2].plot(T_seq, m_seq - p_seq)
    ax[3].plot(T_seq, m_seq)
    ax[4].plot(T_seq, p_seq)
    
    y_labs = [r'$\mu$', r'$\pi$', r'$m - p$', r'$m$', r'$p$']
    subplot_title = [r'Money supply growth', r'Inflation', r'Real balances', r'Money supply', r'Price level']

    for i in range(5):
        ax[i].set_xlabel(r'$t$')
        ax[i].set_ylabel(y_labs[i])
        ax[i].set_title(subplot_title[i])

    ax[1].legend()
    plt.tight_layout()
    plt.show()
    
    return π_seq, Eπ_seq, m_seq, p_seq

Target:

def solve(model, μ_seq):
    "在求解有限视界的凯根模型"
    
    model_params = model.α, model.m0, model.Eπ0, model.T, model.λ
    α, m0, Eπ0, T, λ = model_params
    
    A = np.eye(T+2, T+2) - λ*np.eye(T+2, T+2, k=-1)
    B = np.eye(T+2, T+1, k=-1)
    C = -α*np.eye(T+1, T+2) + α*np.eye(T+1, T+2, k=1)
    Eπ0_seq = np.append(Eπ0, np.zeros(T+1))

    # Eπ_seq 的长度为 T+2
    Eπ_seq = np.linalg.inv(A - (1-λ)*B @ C) @ ((1-λ) * B @ μ_seq + Eπ0_seq)

    # π_seq 的长度为 T+1
    π_seq = μ_seq + C @ Eπ_seq

    D = np.eye(T+1, T+1) - np.eye(T+1, T+1, k=-1)
    m0_seq = np.append(m0, np.zeros(T))

    # m_seq 的长度为 T+2
    m_seq = np.linalg.inv(D) @ (μ_seq + m0_seq)
    m_seq = np.append(m0, m_seq)

    # p_seq 的长度为 T+2
    p_seq = m_seq + α * Eπ_seq

    return π_seq, Eπ_seq, m_seq, p_seq

Code Block 4 - ⚠️ DIFFERS | Code logic differs

Source:

print(np.abs((md.λ - md.α*(1-md.λ))/(1 - md.α*(1-md.λ))))

Target:

def solve_and_plot(model, μ_seq):
    
    π_seq, Eπ_seq, m_seq, p_seq = solve(model, μ_seq)
    
    T_seq = range(model.T+2)
    
    fig, ax = plt.subplots(5, 1, figsize=[5, 12], dpi=200)
    ax[0].plot(T_seq[:-1], μ_seq)
    ax[1].plot(T_seq[:-1], π_seq, label=r'$\pi_t$')
    ax[1].plot(T_seq, Eπ_seq, label=r'$\pi^{*}_{t}$')
    ax[2].plot(T_seq, m_seq - p_seq)
    ax[3].plot(T_seq, m_seq)
    ax[4].plot(T_seq, p_seq)
    
    y_labs = [r'$\mu$', r'$\pi$', r'$m - p$', r'$m$', r'$p$']

    for i in range(5):
        ax[i].set_xlabel(r'$t$')
        ax[i].set_ylabel(y_labs[i])

    ax[1].legend()
    plt.tight_layout()
    plt.show()
    
    return π_seq, Eπ_seq, m_seq, p_seq

§7 (Code block 2 - model creation)

§7 Content: (Code block 2 - model creation) → (Code block 2 - model creation with parameters)

Status: ⚠️ DIFFERS | Issue: Differs

Notes:

• Issue: Target version inserts additional explanatory code block defining parameters (T, T1, α, λ, m0, μ0, μ_star) and instantiating model with md = create_cagan_model(...) before the solve functions, while source shows model creation without parameter definitions at this point
• Fix: Remove the inserted parameter definition block to align with source structure where parameters appear later in the experiments section

Action:

• SYNC
• BACKPORT
• ACCEPT
• MANUAL (recommended)

Code Block T7 - 🔵 INSERTED | Extra block in target

Target:

μ_seq_1 = np.append(μ0*np.ones(T1), μ_star*np.ones(T+1-T1))

# 求解并绘图
π_seq_1, Eπ_seq_1, m_seq_1, p_seq_1 = solve_and_plot(md, μ_seq_1)

This block exists only in target - not in source.

Code Block 5 - ⚠️ DIFFERS | Code logic differs

Source:

# Parameters for the experiment 1
T1 = 60
μ0 = 0.5
μ_star = 0

μ_seq_1 = np.append(μ0*np.ones(T1), μ_star*np.ones(md.T+1-T1))

# solve and plot
π_seq_1, Eπ_seq_1, m_seq_1, p_seq_1 = solve_and_plot(md, μ_seq_1)

Target:

print(np.abs((λ - α*(1-λ))/(1 - α*(1-λ))))

Code Block T8 - 🔵 INSERTED | Extra block in target

Target:

# 参数
ϕ = 0.9
μ_seq_2 = np.array([ϕ**t * μ0 + (1-ϕ**t)*μ_star for t in range(T)])
μ_seq_2 = np.append(μ_seq_2, μ_star)


# 求解并绘图
π_seq_2, Eπ_seq_2, m_seq_2, p_seq_2 = solve_and_plot(md, μ_seq_2)

This block exists only in target - not in source.

Code Block 6 - ⚠️ DIFFERS | Code logic differs

Source:

# parameters
ϕ = 0.9
μ_seq_2 = np.array([ϕ**t * μ0 + (1-ϕ**t)*μ_star for t in range(md.T)])
μ_seq_2 = np.append(μ_seq_2, μ_star)


# solve and plot
π_seq_2, Eπ_seq_2, m_seq_2, p_seq_2 = solve_and_plot(md, μ_seq_2)

Target:

print(λ - α*(1-λ))

[mmcky]

@HumphreyYang here is a new sample report that focuses on cagan_adaptive as a test case. It is providing analysis comparing the ENGLISH document with the one in lecture-intro.zh-cn looking at STRUCTURE and CODE.

I have ben working on an onboarding tool that can help initially align the two SOURCE and TARGET repos before enabling actions-translation to keep them in sync. I was ambitious in trying to figure out the mix of SYNC from SOURCE, and possibly BACKPORT to SOURCE. This is an interesting case, as it has identified some things we should BACKPORT (which would be via a SUGGEST mechanism back to this repo).

If you have time, I'd appreciate a review and let me know if you agree with recommendations and or any comments.

[mmcky]

@HumphreyYang please feel free to be critical. I half wonder if this is overly ambitious and we should build an onboarding tool that assumes the SOURCE REPO is the source of truth, and then write some fuzzy rules around not replacing localisation that may be applied in the target repo.

[HumphreyYang]

Many thanks @mmcky. I will study this carefully after my presentation tomorrow in Sydney!

Thanks for all these nice reports!

[HumphreyYang]

Many thanks @mmcky! I can see how this tool will be super useful once we finetune it!

I will start with some general observations before going into individual items!

• I did not tick all the boxes because I’m not sure how SYNC (recommended) differs from BACKPORT and ACCEPT as actions.

• Overall the agent could not match code cells accurately. I think this is because it tries to number the code cells and match them one-to-one.

  • This would break if we add or delete a code cell in the target.
  • Instead of using an issue, would it be helpful to use a draft PR that tries to push the Chinese version to the English version, so we can ask the agent to look at the diffs where code changes are shown more clearly (similar to the change report we had in the test repo)?

Issue: "Harvesting insights from our matrix formulation" is metaphorical and exploratory, while "求解模型" (Solving the model) is more direct and procedural, losing the nuance of extracting understanding from the mathematical structure

For this one, I think the suggestion to update the Chinese version sounds good: "矩阵公式推导及求解".

Issue: "Forecast errors and model computation" encompasses both the conceptual issue of forecast errors and computational aspects, while "预期与实际通货膨胀的差异" (Differences between expected and actual inflation) focuses only on one aspect

The suggested fix sounds good to me.

Issue: Target adds extensive explanatory paragraphs (explaining adaptive expectations characteristics, contrasting with rational expectations) that are not present in the concise source text

Personally, I think the Chinese version with slightly more explanation and being more explicit about objects in the equation will help readers.

I also think that’s why the Xiamen review team added those paragraphs.

Code Block 1 - ⚠️ DIFFERS | Function names differ

This code change is correctly identified.

Code Block 2 - ⚠️ DIFFERS | Code logic differs

I think it is not matching code blocks correctly here. The correct corresponding code block in the target should be:

def solve(model, μ_seq):
    "在求解有限视界的凯根模型"
    
    model_params = model.α, model.m0, model.Eπ0, model.T, model.λ
    α, m0, Eπ0, T, λ = model_params
    
    A = np.eye(T+2, T+2) - λ*np.eye(T+2, T+2, k=-1)
    B = np.eye(T+2, T+1, k=-1)
    C = -α*np.eye(T+1, T+2) + α*np.eye(T+1, T+2, k=1)
    Eπ0_seq = np.append(Eπ0, np.zeros(T+1))

    # Eπ_seq 的长度为 T+2
    Eπ_seq = np.linalg.inv(A - (1-λ)*B @ C) @ ((1-λ) * B @ μ_seq + Eπ0_seq)

    # π_seq 的长度为 T+1
    π_seq = μ_seq + C @ Eπ_seq

    D = np.eye(T+1, T+1) - np.eye(T+1, T+1, k=-1)
    m0_seq = np.append(m0, np.zeros(T))

    # m_seq 的长度为 T+2
    m_seq = np.linalg.inv(D) @ (μ_seq + m0_seq)
    m_seq = np.append(m0, m_seq)

    # p_seq 的长度为 T+2
    p_seq = m_seq + α * Eπ_seq

    return π_seq, Eπ_seq, m_seq, p_seq

So the code logic did not change—the function name changed.

Code Block 3 - ⚠️ DIFFERS | Function names differ

solve_and_plot is present in the Chinese target, and the function name / logic did not change, so this is not correctly identified.

Code Block 4 - ⚠️ DIFFERS | Code logic differs

This is also not correctly matched to the corresponding code in the Chinese target.

§7 Content: (Code block 2 - model creation) → (Code block 2 - model creation with parameters)

This correctly identified an important change in the code logic—here I think the English source version is cleaner.

Code Block T7 - 🔵 INSERTED | Extra block in target

This is not correct; the original code also has the same line:

μ_seq_1 = np.append(μ0*np.ones(T1), μ_star*np.ones(md.T+1-T1))

The difference is that the Chinese target uses:

μ_seq_1 = np.append(μ0*np.ones(T1), μ_star*np.ones(T+1-T1))

That is:

md.T (English) $\to$ $T$ (Chinese)

Code Block 5 - ⚠️ DIFFERS | Code logic differs

Code Block T8 - 🔵 INSERTED | Extra block in target

Code Block 6 - ⚠️ DIFFERS | Code logic differs

The three suggestions above have the same issue: they fail to match the code blocks between the target and the source.