When making long-term predictions about everything from economic trends to climate change, forecasters might unknowingly favor earlier outcomes simply because they prefer resolutions that happen sooner rather than later. This tendency, known as time preference bias, could systematically distort forecasts, yet it hasn't been thoroughly studied. One way to address this gap would be to examine whether predictions consistently err toward earlier dates and develop methods to correct this bias if it exists.

The Core Idea

The project would analyze historical forecasts to detect whether predictions with longer time horizons tend to be biased toward earlier dates. For example, if experts predicted a technological breakthrough would occur in 10 years, did it actually take 15? By compiling resolved forecasts across different fields—such as economics, science, and politics—statistical models could isolate time preference from other known biases like overconfidence. If a pattern emerges, the next step would be to create training materials or adjusted forecasting methods that account for this tendency.

Why It Matters

Accurate long-term forecasts are crucial for governments, businesses, and researchers making high-stakes decisions. If time preference skews predictions, correcting it could lead to better planning in areas like:

• Policy decisions based on climate or economic projections
• Corporate investments in emerging technologies
• Scientific research prioritization

Existing forecasting platforms and prediction markets could integrate these findings to improve their accuracy, benefiting both professional forecasters and end-users.

Execution Strategy

A minimal approach could start with publicly available prediction market data, analyzing whether contracts with longer expiration dates show a systematic drift toward early resolutions. If initial results support the hypothesis, the study could expand to include:

1. Academic and corporate forecasts with documented timelines
2. Controlled experiments varying prediction horizons while keeping other factors constant
3. Development of debiasing techniques, such as adjusted training for forecasters

Unlike existing work on forecasting accuracy, which focuses on individual skill or aggregation methods, this would specifically target time-related distortions.