Effective altruism (EA) organizations often struggle with making high-impact decisions due to limited access to reliable forecasts. While some volunteer-run forecasting platforms exist, they tend to be fragmented and lack the professional rigor needed for EA's high-stakes decisions about global health, existential risks, and long-term future shaping. A dedicated forecasting organization could address this gap by providing centralized, professional predictions tailored specifically to EA needs.

How This Could Work

One approach would be to establish an organization that employs full-time professional forecasters to generate and maintain predictions on EA-relevant questions. Beyond just making forecasts, it could also host and improve existing forecasting platforms, creating a feedback loop where better platforms lead to better data, which in turn improves forecast quality. The organization could start small with just a few forecasters focusing on the most critical EA questions, then expand based on demand.

Potential beneficiaries include:

• EA organizations needing data for grantmaking decisions
• Individual altruists making career or donation choices
• Researchers studying long-term trends
• Policy makers addressing global challenges

Advantages Over Existing Solutions

Unlike general-purpose forecasting platforms like Good Judgment Project or Metaculus, this would focus specifically on EA questions with professional oversight. Compared to government-oriented forecasting efforts like CSET's Foretell, it would prioritize EA's unique concerns. The specialization could lead to deeper expertise in areas like existential risk assessment and global development metrics.

Key differentiators might include:

1. Curated question selection based on EA priorities
2. Professional forecasters instead of volunteer crowds
3. Integration with EA decision-making processes

Potential Implementation Path

An initial phase could involve recruiting a small team of proven forecasters to work on high-priority questions while building relationships with EA organizations. Funding might come from EA-aligned donors who recognize the value of improved forecasting. As the organization grows, it could develop specialized teams for different cause areas and potentially offer training services to help EA professionals improve their own forecasting skills.

The concept would need to validate assumptions about demand from EA organizations and the superior performance of professional forecasters. However, the potential to significantly improve resource allocation decisions across the EA ecosystem makes this an intriguing possibility.