Philanthropy often struggles with allocating limited resources among causes like animal welfare, global health, and existential risks. Current methods, which tend to be top-down or siloed, may miss opportunities for collaboration or fair trade-offs between different priorities. This inefficiency can lead to wasted efforts or even conflicting interventions, reducing overall impact.

How Negotiation Between Causes Could Work

One approach to solving this problem could involve creating a structured way for different philanthropic priorities to negotiate resource sharing. For instance:

• Define key worldviews (e.g., animal welfare, global health) and their current funding allocations.
• Simulate negotiations where these groups trade resources based on mutual benefit—like two causes cooperating to reduce climate change impacts that harm both.
• Analyze the outcomes to identify stable funding distributions or unexpected synergies.

The idea is to model how these groups would interact if given the chance to negotiate, rather than assuming they must compete. Organizations could use this to refine grantmaking, advocates could leverage trade opportunities, and donors could make better-informed contributions.

Testing and Implementing the Concept

To explore this idea, one might start with:

1. Research: Map out major cause areas and their conflicts or overlaps through expert input.
2. Modeling: Build simple simulations to test how bargaining affects outcomes (e.g., "What happens if 10% of a budget shifts from one cause to another?").
3. Pilot Negotiations: Facilitate small-scale discussions between real organizations to see if the theory holds in practice.

Potential hurdles include ideological resistance or difficulty comparing impact across causes, but existing metrics (like adjusted quality-of-life measures) could help bridge gaps.

How This Fits with Existing Efforts

Unlike static grantmaking or voting-based systems, this approach focuses on active negotiation between causes. For example:

• It could complement participatory grantmaking by adding a strategic layer to community decisions.
• It diverges from prediction markets, which forecast outcomes but don’t foster collaboration.

By treating funding as a dynamic system, rather than a fixed pie, this model might uncover more efficient—and even cooperative—ways to allocate resources.