Executive summary: The report argues that individuals and communities aiming to maximize expected altruistic impact should often allocate significantly more effort to exploration (searching for new opportunities) and prioritization (comparing known opportunities) than direct work, especially under assumptions of heavy-tailed impact distributions.
Key points:
Exploration should often dominate effort allocation: In many scenarios, spending over half of one’s resources on exploring new projects is more beneficial than focusing on known ones (Claim 3, confidence: 45%/20%).
Prioritization is crucial, especially with uncorrelated options: When projects are uncorrelated, evaluating each project can be as valuable as directly working on the best one, justifying equal effort allocation across evaluation and execution (Claim 6a).
Even with correlated projects, prioritization remains important: Although its relative importance decreases, prioritization still merits significantly more effort than direct work under plausible assumptions (Claim 6b).
Impact distributions are likely heavy-tailed: Theories of change (TOC) impact across projects likely follow heavy-tailed distributions, such as Pareto, making exploration and prioritization especially valuable for identifying high-impact opportunities (Claims 2 & 4).
Real-world applicability depends on several contextual factors: These include difficulty of reducing uncertainty, relative scalability of E&P, and fixed budgets; exploration and prioritization are most valuable in cause prioritization, within-cause prioritization, and identifying promising individuals (Claims 7 & 8).
Caveats and limitations: The model ignores negative impacts, externalities, diminishing returns, and non-TOC impacts; it also assumes idealized conditions and treats exploration, prioritization, and direct work as cleanly separable, which is often unrealistic.
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Executive summary: The report argues that individuals and communities aiming to maximize expected altruistic impact should often allocate significantly more effort to exploration (searching for new opportunities) and prioritization (comparing known opportunities) than direct work, especially under assumptions of heavy-tailed impact distributions.
Key points:
Exploration should often dominate effort allocation: In many scenarios, spending over half of one’s resources on exploring new projects is more beneficial than focusing on known ones (Claim 3, confidence: 45%/20%).
Prioritization is crucial, especially with uncorrelated options: When projects are uncorrelated, evaluating each project can be as valuable as directly working on the best one, justifying equal effort allocation across evaluation and execution (Claim 6a).
Even with correlated projects, prioritization remains important: Although its relative importance decreases, prioritization still merits significantly more effort than direct work under plausible assumptions (Claim 6b).
Impact distributions are likely heavy-tailed: Theories of change (TOC) impact across projects likely follow heavy-tailed distributions, such as Pareto, making exploration and prioritization especially valuable for identifying high-impact opportunities (Claims 2 & 4).
Real-world applicability depends on several contextual factors: These include difficulty of reducing uncertainty, relative scalability of E&P, and fixed budgets; exploration and prioritization are most valuable in cause prioritization, within-cause prioritization, and identifying promising individuals (Claims 7 & 8).
Caveats and limitations: The model ignores negative impacts, externalities, diminishing returns, and non-TOC impacts; it also assumes idealized conditions and treats exploration, prioritization, and direct work as cleanly separable, which is often unrealistic.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.