CCF: The Spreadsheet in Your Head Is Misallocating Your Giving

Published on October 8, 2026 6:18 PM GMT Opinion piece by Marcus A. Davis, CEO of Rethink Priorities TL;DR: The problem: Major donors choose between causes like AI safety, global health, and animal welfare without writing down why. In interviews, many couldn’t say what their gifts achieved or how they weighed one cause against another. Why it happens: Skipping the spreadsheet feels simpler and more humble, but every gut call is still a model, just one nobody can see or check. How it goes wrong: The hidden model makes strong claims by accident. An even split says every cause is equally good at the margin. “I’ll adjust a bit for my doubts” could mean very different strategies. Anything you didn’t think about gets counted as zero. A model you only follow when it agrees with you corrects nothing. Why it gets worse with harder questions: Cross-cause decisions are complex and give no feedback, which is exactly when intuition is least reliable, and mistakes are hardest to notice. What it costs: Millions can be given with no all-things-considered comparison behind them, so money is likely being misallocated. What to do: Small donors can rely on a fund or evaluator. Large donors should pay for in-depth analysis, since even a marginally better decision can justify it. Donors in between can use shared analysis rather than building their own. Whatever you rely on, ask it to show its work: which assumptions drive the answer, what’s being counted as zero, and what would change the recommendation.   If it’s possible to be famous for your spreadsheets, GiveWell, the charity evaluator, is. To decide whether malaria nets do more good per dollar than deworming pills, they build models with dozens of inputs, such as how many nets are actually used, how common malaria is in each region, and how much a child’s health today affects their earnings decades later. It’s a heroic effort, built and refined over the last 19 years, that tries to pin down all the uncertainties they have about this complex question. Now, every so often, someone looks at this complex model and says something like, “Why bother? Malaria kills children. Nets prevent malaria. Worms are bad, but they usually don’t kill you. Nets win. I worked that out in ten seconds from a high-level argument without opening a spreadsheet.” The problem is that person isn’t truly skipping the spreadsheet. They’ve built one in their head. They just aren’t showing their work, even to themselves. Congratulations, that was a model A model is really any story about how an action leads to outcomes. No one opens a spreadsheet to decide when to leave for work. But you have a model: typical traffic, how much lateness you can tolerate, whether it’s raining, and so on. Many people, unfortunately, find out that this is a model when they are an hour late to work because a bridge was closed. The same issue applies to high-level stories people tell themselves about why to give to one area rather than another or how to split across them. The person assuming “bednets win” has built a model in their head that depends on: Bednets actually being used Malaria being common where the nets go The long-term benefits of deworming being small or unproven, and A child’s life being worth more than a lifetime of somewhat higher income for many children Each of these is a number. Change a couple, and the answer can flip. GiveWell’s spreadsheet isn’t more complicated structurally than the reasoning underlying the ten-second model. It’s equally complicated, just visible and inspectable. The lessons here are: If you disagree with someone’s spreadsheet, you owe them your numbers. “Models are dumb” isn’t a rebuttal when your alternative is a secret model doing computation in your head that can’t be interrogated. Anything you believe can, in principle, be written down. The real question is whether writing it down loses more than it gains. Usually it gains a lot, and I’m pretty sure cause prioritization is one of those times. The critics of models have a point, but it doesn’t prove what they think Some skeptics go further. They don’t just think models are unnecessary; they think models are misleading, and that experienced judgment is the safer guide. They raise some fair points: Point estimates hide uncertainty. Many inputs are best guesses with wide error bars. The evidence might suggest a condition reduces quality of life by about 20%, but with a plausible range anywhere from 5% to 50%. A spreadsheet that just enters “20%” makes the answer look more certain than it is. Averages aren’t everything. Even when the odds are well understood, the average isn’t the whole story. A safe bet that reliably helps 10 people and a long shot with a 1% chance of helping 1,000 have the same average, but many people would reasonably prefer the safe bet. The spreadsheet can’t see what it left out. It can carefully track one effect, like lives saved, while missing another entirely, like effects on the local economy. One wrong i