Rethink Priorities’ Cross-Cause Cost-Effectiveness Model: Introduction and Overview

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2023·EA Forum·forum.effectivealtruism.org/posts/pniDWyjc9vY5sjGre/rethi...

Authors

Derek Shiller·Bernardo Baron·Chase Carter·Agustín Covarrubias 🔸·Marcus_A_Davis·MichaelDickens·Laura Duffy·Peter Wildeford

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3/5

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Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.

Rating inherited from publication venue: EA Forum

This Rethink Priorities post is relevant to AI safety prioritization insofar as it attempts to compare existential risk interventions (including AI safety work) against other cause areas, providing a methodology for understanding the relative value of AI safety funding and efforts.

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Importance: 62/100analysis

Summary

Rethink Priorities introduces a cross-cause cost-effectiveness model designed to compare interventions across different cause areas (animal welfare, global health, existential risk, etc.) on a common scale. The model attempts to quantify and compare the expected value of diverse charitable interventions despite deep uncertainty, enabling more principled allocation of resources across cause areas. It represents a significant methodological contribution to effective altruism priority-setting research.

Key Points

•Develops a unified framework for comparing cost-effectiveness across fundamentally different cause areas including AI safety, animal welfare, and global health
•Attempts to place existential risk reduction and near-term welfare interventions on a comparable scale using welfare-years and probability weighting
•Acknowledges deep moral and empirical uncertainty and builds in explicit parameters for users to adjust based on their values
•Provides a tool for philanthropists and EA organizations to make more informed cross-cause funding decisions
•Represents one of the most ambitious attempts to formalize cross-cause prioritization in the EA research ecosystem

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# Rethink Priorities’ Cross-Cause Cost-Effectiveness Model: Introduction and Overview
By Derek Shiller, Bernardo Baron, Chase Carter, Agustín Covarrubias 🔸, Marcus_A_Davis, MichaelDickens, Laura Duffy, Peter Wildeford
Published: 2023-11-03
This post is a part of Rethink Priorities’ Worldview Investigations Team’s [CURVE Sequence](https://forum.effectivealtruism.org/s/WdL3LE5LHvTwWmyqj): “Causes and Uncertainty: Rethinking Value in Expectation.” The aim of this sequence is twofold: first, to consider alternatives to expected value maximization for cause prioritization; second, to evaluate the claim that a commitment to expected value maximization robustly supports the conclusion that we ought to prioritize existential risk mitigation over all else. This post presents a software tool we're developing to better understand risk and effectiveness.

**Executive Summary**
=====================

The [cross-cause cost-effectiveness model](https://ccm.rethinkpriorities.org) (CCM) is a software tool under development   by Rethink Priorities to produce cost-effectiveness evaluations in different cause areas.

**Video introduction:** [**the CCM in the context of the curve sequence**](https://www.loom.com/share/d577afaae88648cc811fc5ff275505ed)**,** [**an overview of CCM functionality **](https://www.loom.com/share/0a8cbaa3acc1458586bf1b35e36fc2bf?sid=b08ec1de-8d2c-402d-9f38-192e072d87be)

[**Code Repository**](https://github.com/rethinkpriorities/cross-cause-cost-effectiveness-model-public)

*   The CCM enables evaluations of interventions in global health and development, animal welfare, and existential risk mitigation.
*   The CCM also includes functionality for evaluating research projects aimed at improving existing interventions or discovering more effective alternatives.

The CCM follows a Monte Carlo approach to assessing probabilities.

*   The CCM accepts user-supplied distributions as parameter values.
*   Our primary goal with the CCM is to clarify how parameter choices translate into uncertainty about possible results.

The limitations of the CCM make it an inadequate tool for definitive comparisons.

*   The model is optimized for certain easily quantifiable effective projects and cannot assess many relevant causes.
*   Probability distributions are a questionable way of representing deep uncertainty.
*   The model may not adequately handle possible interdependence between parameters.

Building and using the CCM has confirmed some of our expectations. It has also surprised us in other ways.

*   Given parameter choices that are plausible to us, existential risk mitigation projects dominate others in expected value in the long term, but the results are too high variance to approximate through Monte Carlo simulations without drawing billions of samples.
*   The expected value of existential risk mitigation in the long run is mostly determined by the tail-end possible values for a handful of deeply uncertain parameters.
*   The most promising anima

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