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Sensitive assumptions in longtermist modeling
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Owen Murphy
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A 2024 EA Forum post by Owen Murphy synthesizing David Thorstad's critiques of longtermist modeling, useful for understanding epistemic and quantitative challenges in existential risk prioritization arguments.
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82
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eaforum
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Cause prioritizationExistential riskPhilosophyCriticism of longtermism and existential risk studiesLongtermismModelsMoral philosophyS-riskUtilitarianism
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Importance: 52/100blog postanalysis
Summary
This post critically examines key modeling assumptions underlying longtermist cost-benefit analyses of existential risk reduction, drawing on David Thorstad's work. It demonstrates that variables like baseline risk levels, future population estimates, intervention decay rates, and suffering risks can dramatically shift conclusions, while also presenting counterarguments like the 'time of perils hypothesis' and hedonic lotteries.
Key Points
- •Baseline risk is a critical and often overlooked variable: higher overall existential risk reduces the marginal value of any single risk-reduction intervention.
- •Future population estimates and intervention decay rates can dramatically alter expected-value calculations for longtermist interventions.
- •Suffering risks (S-risks) complicate longtermist reasoning by introducing the possibility that a large future population could be net-negative in welfare.
- •The 'time of perils hypothesis' offers a counterargument: if risk is temporarily elevated now, near-term interventions may have outsized long-run impact.
- •Small, plausible changes in modeling assumptions can produce order-of-magnitude differences in estimated intervention value, highlighting deep uncertainty in longtermist prioritization.
Cited by 1 page
| Page | Type | Quality |
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| EA and Longtermist Wins and Losses | -- | 53.0 |
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Sensitive assumptions in longtermist modeling — EA Forum
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by Owen Murphy Sep 18 2024 8 min read 12 82
Cause prioritization Existential risk Philosophy Criticism of longtermism and existential risk studies Longtermism Models Moral philosophy S-risk Utilitarianism Frontpage Sensitive assumptions in longtermist modeling Model assumptions noted by Thorstad 1. Baseline risk (blog post) Takeaways from this: 2. Future Population (blog post) Takeaways from this: Other model assumptions/considerations I think are important 1. Intervention decay Takeaways from this: 2. Suffering risks (S-risks) Takeaways from this: Counter-arguments for the value of existential risk reduction 1. The ‘time of perils hypothesis’ versus ‘baseline risk’ and ‘intervention decay’ Takeaways from this: 2. Hedonic lotteries Takeaways from this: Final thoughts 12 comments This is a linkpost for https://ohmurphy.substack.com/p/sensitive-assumptions-in-longtermist {Epistemic Status: Repeating critiques from David Thorstad’s excellent papers ( link , link ) and blog , with some additions of my own. The list is not intended to be representative and/or comprehensive for either critiques or rebuttals. Unattributed graphs are my own and more likely to contain errors.}
I am someone generally sympathetic to philosophical longtermism and total utilitarianism, but like many effective altruists , I have often been skeptical about the relative value of actual longtermism-inspired interventions. Unfortunately, though, for a long time I was unable to express any specific, legible critiques of longtermism other than a semi- incredulous stare . Luckily, this condition has changed in the last several months since I started reading David Thorstad’s excellent blog (and papers) critiquing longtermism. [1] His points cover a wide range of issues, but in this post, I would like to focus on a couple of crucial and plausibly incorrect modeling assumptions Thorstad notes in analyses of existential risk reduction, explain a few more critiques of my own, and cover some relevant counterarguments.
Model assumptions noted by Thorstad
1. Baseline risk ( blog post )
When estimating the value of reducing existential risk, one essential – but non-obvious – component is the ‘baseline risk’, i.e., the total existential risk, including risks from sources not being intervened on. [2]
To understand this, let’s start with an equation for the expected life-years E[L] in the future, parameterized by a period existential risk (r), and fill it with respectable values: [3]
Now, to understand the importance of baseline risk, let’s start by examining an estimated E[L] under different levels of risk (without considering interventions):
Here we can observe that the expected life-years in the future drops off
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