Openai/695c3b15-4a8c-8011-b0a8-d4b81e8374b4

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gpt-5-2

User: I'm looking at a sigmoid curve projected onto data at this link https://www.reddit.com/r/energy/s/ev2VrUlGgM .

I'm looking at a sigmoid curve projected onto data at this link https://www.reddit.com/r/energy/s/ev2VrUlGgM .

When the more recent data shows itself to be shifted to the left or right of the curve as in this image, is the more accurate productive value in weighing this data heavier?

Assistant: Short answer: no, not automatically.

Short answer: no, not automatically. Whether you should weight the more recent data more heavily depends on why it has shifted relative to the sigmoid—not simply that it has.

Let’s unpack this carefully.

1. What a sigmoid fit is assuming

A sigmoid (logistic) curve usually encodes three strong assumptions:

1. There is an upper limit (carrying capacity, saturation level).
2. Growth dynamics are stable over time (same drivers, same constraints).
3. Deviations from the curve are mostly noise, not regime changes.

When you project a sigmoid onto energy or production data, you’re implicitly saying:

“This system behaves like a constrained growth process with predictable saturation.”

2. What a left/right shift actually means

When recent data lies to the left or right of the curve, it usually implies one of four things:

A. Timing error (harmless)

• Adoption or production is happening earlier or later than expected
• The shape of the curve is still correct
• Example: EV adoption accelerating due to policy incentives

➡ In this case, re-fitting the curve is better than re-weighting points.

B. Structural acceleration (important)

• The underlying growth rate has changed
• Often due to: - Cost collapses - Policy shifts - Technology substitution
• The sigmoid’s midpoint or steepness is wrong

➡ Here, recent data is more informative, but not because it’s “more accurate”—because the model is now mis-specified.

C. Temporary shock (misleading)

• Supply constraints
• Subsidy spikes
• Wars, pandemics, one-off demand surges

➡ Weighting recent data more heavily overstates long-term change.

D. Saturation ceiling is wrong (critical)

• The assumed maximum is too low or too high
• This causes systematic deviation near the “flattening” region

➡ Weighting data won’t fix this; the entire curve form must change.

3. Why “weight recent data more” is often the wrong instinct

Weighting recent data more heavily assumes:

“The system is the same, but older data is less relevant.”

But in energy systems, the opposite is often true:

• Early data tells you about physical and economic constraints
• Later data reflects policy and market distortions

If the deviation is systematic, re-weighting is treating a model error as a data error.

4. What to do instead (best practice)

Step 1: Diagnose the deviation

Ask:

• Is the residual increasing monotonically?
• Does the deviation align with known events (policy, tech cost curves)?
• Does the slope mismatch or just the timing?

Step 2: Choose the fix

| Problem | Correct response | | ------------------ | ------------------------------ | | Timing shift | Re-estimate midpoint | | Growth rate change | Re-estimate slope | | Ceiling wrong | Change carrying capacity | | Regime change | New model or piecewise sigmoid |

Step 3: Use weighting only if

• You believe older data is drawn from a different distribution
• And you can justify why (not just “it’s old”)

5. A useful rule of thumb

If recent data violates your model’s assumptions, don’t weight it—change the model.

Weighting should reduce noise, not conceal structural breaks.

If you want, you can:

• Describe what the data represents (oil, renewables, EVs, etc.)
• Or summarize the deviation pattern (early acceleration vs late slowdown)

…and I can tell you whether a sigmoid is even the right tool here.