The Nonlinear Library EA - Rethink Priorities' Cross-Cause Cost-Effectiveness Model: Introduction and Overview by Derek Shiller
Nov 3, 2023
23:02
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Rethink Priorities' Cross-Cause Cost-Effectiveness Model: Introduction and Overview, published by Derek Shiller on November 3, 2023 on The Effective Altruism Forum.
This post is a part of Rethink Priorities' Worldview Investigations Team's
CURVE Sequence
: "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
(CCM) is a software tool under development by Rethink Priorities to produce cost-effectiveness evaluations in different cause areas.
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 animal welfare interventions have a much higher expected value than the leading global health and development interventions with a somewhat higher level of uncertainty.
Even with relatively straightforward short-term interventions and research projects, much of the expected value of projects results from the unlikely combination of tail-end parameter values.
We plan to host
an online walkthrough and Q&A of the model
with the Rethink Priorities Worldview Investigations Team on Giving Tuesday, November 28, 2023, at 9 am PT / noon ET / 5 pm BT / 6 pm CET. If you would like to attend this event, please
sign up
here.
Overview
Rethink Priorities' cross-cause cost-effectiveness model (CCM) is a software tool we are developing for evaluating the relative effectiveness of projects across three general domains: global health and development, animal welfare, and the mitigation of existential risks. You can play with our initial version at
ccm.rethinkpriorities.org
and provide us feedback in this post or
via this form
.
The model produces effectiveness estimates, understood in terms of the effect on the sum of welfare across individuals, for interventions and research projects within these domains. Results are generated by computations on the values of user-supplied parameters. Because of the many controversies and uncertainties around these parameters, it follows a Monte Carlo approach to accommodating our uncertainty: users don't supply precise values but instead ...
This post is a part of Rethink Priorities' Worldview Investigations Team's
CURVE Sequence
: "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
(CCM) is a software tool under development by Rethink Priorities to produce cost-effectiveness evaluations in different cause areas.
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 animal welfare interventions have a much higher expected value than the leading global health and development interventions with a somewhat higher level of uncertainty.
Even with relatively straightforward short-term interventions and research projects, much of the expected value of projects results from the unlikely combination of tail-end parameter values.
We plan to host
an online walkthrough and Q&A of the model
with the Rethink Priorities Worldview Investigations Team on Giving Tuesday, November 28, 2023, at 9 am PT / noon ET / 5 pm BT / 6 pm CET. If you would like to attend this event, please
sign up
here.
Overview
Rethink Priorities' cross-cause cost-effectiveness model (CCM) is a software tool we are developing for evaluating the relative effectiveness of projects across three general domains: global health and development, animal welfare, and the mitigation of existential risks. You can play with our initial version at
ccm.rethinkpriorities.org
and provide us feedback in this post or
via this form
.
The model produces effectiveness estimates, understood in terms of the effect on the sum of welfare across individuals, for interventions and research projects within these domains. Results are generated by computations on the values of user-supplied parameters. Because of the many controversies and uncertainties around these parameters, it follows a Monte Carlo approach to accommodating our uncertainty: users don't supply precise values but instead ...