Introducing more sophisticated probability distributions into decision-making tools for altruistic interventions could help organizations better navigate the high uncertainty inherent in areas like global health, poverty alleviation, and climate change. Currently, most cost-effectiveness analyses rely on simplified models that may overlook extreme risks or nonlinear outcomes, potentially leading to suboptimal resource allocation.

Enhancing Decision-Making Under Uncertainty

The idea revolves around expanding an existing portfolio optimization tool to include a wider range of probability distributions, such as Pareto for rare but high-impact events, log-normal for multiplicative effects, and beta for bounded outcomes. This would allow users to model scenarios with greater accuracy, capturing nuances that simpler distributions might miss. For instance, a grantmaker evaluating pandemic preparedness could use heavy-tailed distributions to better account for low-probability, high-consequence risks.

Key features might include:

• Interactive overlays to compare different distribution scenarios
• Real-time parameter adjustments to test assumptions
• Educational guides to help non-technical users select appropriate models

Tailoring the Tool for Stakeholders

The tool could serve diverse users, from effective altruism organizations allocating resources to policymakers justifying funding decisions. Incentives align across stakeholders—developers differentiate their product, users gain better decision-making capabilities, and funders maximize impact. Potential monetization could involve a freemium model, with basic features free and premium options (e.g., custom distributions) for paying users or institutions.

Execution and Competitive Edge

An MVP might start with a few high-priority distributions and basic comparison features, followed by iterative expansion based on user feedback. Competitive advantages include a focus on altruistic applications (unlike generic financial tools) and a community-driven approach—open-sourcing core models while offering paid support for customization. Compared to existing tools like Guesstimate (limited distributions) or GiveWell’s static models, this approach could provide more dynamic, transparent, and realistic uncertainty modeling.

By improving how decision-makers account for uncertainty, such a tool could help direct resources toward more effective interventions, ultimately increasing social impact.