Higher-order forecasting could be a useful concept for prediction markets and forecasting systems more broadly.

The core idea is straightforward:
Nth-order forecasts are forecasts about (N-1)th order forecasts.

Examples

Here are some examples:

0-Order Forecasting (i.e., the ground truth)

• Biden won the 2020 U.S. presidential election
• The US GDP in 2023 was $27 trillion

1st-Order Forecasting (i.e., regular forecasting)

• What is the chance that Trump will win the 2024 U.S. presidential election?
• What will be the GDP of the US in 2024?

2nd-Order Forecasting

• How much will the forecasts for US GDP in 2024 and 2025 be correlated over the next year?
• How many forecasts will the question "What will be the GDP of the US in 2024?" receive in total?
• If the question “What is the chance that a Republican will win the 2028 Presidential Election?” was posted [...]

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Outline:

(00:28) Examples

(02:32) Benefits

(04:46) Challenges

(05:29) Conclusion

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First published:

Source:

Narrated by TYPE III AUDIO.