Long-term policy forecasting often relies on expert opinions, which can be biased, opaque, or strategically misrepresented—especially when outcomes are hard to verify. This leads to unreliable predictions, wasted resources, and poor decision-making. One way to address this is by adapting "subjective truth serums," like the Bayesian Truth Serum (BTS), which use game-theoretic incentives to encourage truthful reporting even when ground truth is unavailable.

How Belief Elicitation Could Improve Forecasting

Traditional forecasting rewards accuracy, but when outcomes are uncertain or far in the future, this is hard to measure. Instead, methods like BTS incentivize honesty by rewarding participants whose predictions align with the consensus and whose confidence is well-calibrated. For example, an expert who predicts a policy outcome with high confidence—but whose confidence doesn’t match the group’s—would score lower than someone whose confidence aligns with peers. This reduces overconfidence and strategic misreporting.

Potential applications include:

• Policy decisions: Governments could use these methods to estimate the long-term effects of education or climate policies.
• Philanthropy: Foundations could better assess which interventions will have the most impact.
• Research: Social scientists could gather more reliable data on trends like economic growth or public health.

Implementation and Challenges

To test this approach, a pilot study could compare traditional forecasting with BTS-based methods in simulated policy scenarios. If successful, the next step would be field experiments with real policymakers, followed by developing user-friendly tools (e.g., software or guidelines) to simplify adoption.

Key challenges include:

• Expert resistance: Some may prefer familiar methods, so highlighting benefits (e.g., reputational gains for accuracy) could help.
• Complexity: Simplifying the tools or offering training could make the methods more accessible.
• Non-verifiability: For long-term outcomes, intermediate measures or proxy indicators might be needed.

How This Compares to Existing Methods

Current approaches like prediction markets or Delphi surveys aggregate opinions but don’t explicitly incentivize truthfulness. BTS-like methods could complement these by reducing biases. For instance, while platforms like Metaculus rely on reputation scoring, adding belief-elicitation incentives might improve honesty in non-verifiable scenarios.

This idea could make policy forecasting more rigorous, leading to better decisions in high-stakes areas like climate change or public health. The key would be validating the methods in real-world settings and making them practical for policymakers.