How moral weights and soil life change cause prioritisation

By Vasco Grilo🔸 @ 2026-10-13T14:55 (+11)

This is a linkpost to https://claude.ai/artifact/9ksZNZqbRfYcdG8JbMxCBg

This is a linkpost for How moral weights and soil life change cause prioritisation, an artifact I built with Claude Opus 5.5. The summary is below.

Twelve interventions, all but two of them donations at the current margin, two models of moral weight, and every exponent from 0 to 2. Model 1 counts animals with neurons, and assumes moral weight is proportional to the number of neurons to the power n. Model 2 adds trees, and soil bacteria and archaea, and assumes moral weight is proportional to basal metabolic rate (BMR) to the power m. Neuron counts, metabolic rates, pain intensities and the welfare of soil organisms in each land class are all uncertain inputs.

Low exponents

Effects on soil organisms dominate, and the interventions that change farmland the most per dollar lead. Under model 1, those that decrease farmland lead, with donations to the Good Food Institute (GFI) first, since soil animals, mostly nematodes, are assumed to fare worse on farmland. Under model 2, those that add farmland lead, with Giving What We Can (GWWC) ahead of corporate cage-free campaigns, since farmland holds fewer soil bacteria and archaea, whose lives are assumed negative in expectation. These leads come from guessed mean welfare levels much smaller than their spreads, and each leader is among the worst options under the other model. So they show which questions research should answer more than which of the interventions analysed to fund.

Intermediate exponents

The Shrimp Welfare Project's (SWP's) Humane Slaughter Initiative (HSI), which electrically stuns shrimps, narrowly leads in narrow bands of these exponents. Its beneficiaries have small weights but may be spared intense suffering at slaughter. Its position turns on whether stunning reduces suffering and on the intensity of excruciating pain.

High exponents

Effects on the target beneficiaries, the people or animals each intervention aims to help, dominate. This favours humans and chickens. GWWC moves several dollars per dollar to effective charities, mostly in global health and animal welfare. It leads under model 1, largely through the people saved and their descendants. Under model 2, cage-free campaigns lead, except at the highest exponents, where GWWC does.

Farmland per dollar

The ranges from the 5th to the 95th percentile of the eight interventions adding the most farmland per dollar overlap substantially. The Navigation Fund's cage-free campaigns fund has the highest mean, closely followed by GWWC and GiveWell's grant. Of the candidates not modelled, only promoting beef plausibly adds clearly more, on weak evidence (farmland per dollar).

Robustness

Few results are robust. Whether soil organisms have negative lives, and whether farming worsens them, are close to coin flips. So at low exponents the sign of most interventions' effects is unclear. At high exponents, the leaders have a high chance of a positive effect. Under model 1, GWWC keeps its lead at the highest exponents in nearly every sensitivity analysis. The exceptions are with GWWC's conservative multiplier of 1.5 and without the weighting for uncertainty over time, where GiveWell's grant leads instead. Under model 2, the leader just below the highest exponents depends on the intensities of pain and on the weighting for uncertainty over time. Some leads are also statistical ties within the noise of the simulation (using the results).

So which cause to prioritise turns on the form and exponent of the moral-weight function, the welfare of soil organisms, the intensity of excruciating pain and whether stunning reduces suffering. Research could narrow all of these (key assumptions).