Cooperation Among Agents With Diverse Moral Goals

Cooperation Among Agents With Diverse Moral Goals

Summary: The idea addresses the challenge of enabling cooperation among agents with diverse moral goals without direct communication or mutual simulation. It proposes an evidence-based framework where agents use correlated actions to balance individual objectives via a shared utility function, scaling better than negotiation-based solutions in large or infinitely complex systems. This leverages insights from decision theory, offering practical benefits for altruistic resource allocation and AI coordination dilemmas.

The idea explores how agents with different moral goals could cooperate without direct communication or the need to simulate each other's decisions. Traditional models often rely on these mechanisms, which become impractical in large or infinite systems. This gap is particularly relevant for communities like Effective Altruism, where efficiently allocating resources across diverse priorities is crucial.

How Evidential Cooperation Could Work

One way to enable cooperation among such agents is through a framework where their actions provide evidence of each other's behavior. Instead of negotiating or simulating one another, agents could maximize a shared utility function that balances their individual moral goals. This approach builds on updateless decision theories, where agents act as if their choices are correlated with those of similar agents. For example, an agent focused on animal welfare might allocate resources to regions where their efforts have the highest impact, while another agent prioritizing global health does the same in different contexts—both benefiting from comparative advantages without direct coordination.

Potential Applications and Stakeholders

This framework could be valuable for:

  • Researchers in moral philosophy and decision theory, offering a new way to model cooperation in complex systems.
  • The Effective Altruism community, helping refine resource allocation across competing priorities like poverty reduction and existential risk mitigation.
  • AI alignment researchers, providing insights into how AI systems with differing objectives might cooperate implicitly.

Stakeholders like academic researchers might be motivated by theoretical breakthroughs, while practical adopters could use the framework to improve real-world prioritization.

Execution and Comparisons

A starting point could involve formalizing the theoretical framework and testing it through simulations or thought experiments. Existing work on acausal trade and updateless decision theories provides a foundation, but this idea extends those concepts by focusing on diverse moral goals and evidence-based cooperation. Unlike traditional bargaining models, this approach doesn’t require explicit negotiation, making it scalable to large or infinite scenarios.

While the idea is theoretical, initial steps might include publishing papers or collaborating with organizations to explore applications. The emphasis would be on clarity—translating abstract concepts into actionable insights for non-experts.

Source of Idea:
This idea was taken from https://impartial-priorities.org/self-study-directions-2020.html and further developed using an algorithm.
Skills Needed to Execute This Idea:
Decision TheoryMoral PhilosophyAlgorithm DesignGame TheoryMathematical ModelingAI AlignmentResource AllocationSimulation DevelopmentAcademic ResearchCollaborative Systems
Categories:Moral PhilosophyDecision TheoryEffective AltruismAI AlignmentResource AllocationCooperation Models

Hours To Execute (basic)

750 hours to execute minimal version ()

Hours to Execute (full)

1000 hours to execute full idea ()

Estd No of Collaborators

1-10 Collaborators ()

Financial Potential

$0–1M Potential ()

Impact Breadth

Affects 1K-100K people ()

Impact Depth

Significant Impact ()

Impact Positivity

Probably Helpful ()

Impact Duration

Impacts Lasts 3-10 Years ()

Uniqueness

Highly Unique ()

Implementability

()

Plausibility

Logically Sound ()

Replicability

Moderately Difficult to Replicate ()

Market Timing

Good Timing ()

Project Type

Research

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