Addressing the same problem, like lead poisoning, is far cheaper in low-income countries. This is due to lower costs for labor and services, and because wealthier governments already provide a baseline of support, making marginal improvements more expensive.
The for-profit world is hyper-competitive with clear feedback loops like profit. The non-profit sector lacks these, making it less efficient. This inefficiency creates an opportunity; a focused, effective individual or charity can achieve disproportionately large impact because there is simply less competition.
A complex spreadsheet model is often brittle; a single questionable assumption can cause stakeholders to reject the entire analysis. To counter this, models should make key assumptions transparent and easily adjustable, like with a slider, to allow for sensitivity analysis rather than outright dismissal.
Critics argue that marginal, cost-effective thinking in charity would eliminate art. This is a flawed argument. The real-world choice isn't 'all resources to malaria or art,' but rather how to allocate a small portion of one's income for maximum good, which poses no threat to the existence of art.
Expert philosophers disagree sharply on fundamental moral theories. Rather than trying to pick the 'correct' one with high confidence, a more robust approach is to acknowledge this uncertainty and aggregate across different worldviews when making high-stakes ethical decisions, such as by splitting a budget proportionally.
Critics claim explicit models for big decisions are flawed. However, relying on intuition is just using an opaque, implicit model you can't scrutinize. An explicit model, even if imperfect, makes assumptions transparent and challengeable, which is superior to a 'gut feeling' that cannot be dissected or debated.
Don't dismiss a model because its output is a wide, uncertain distribution. This is often the correct answer, as it accurately reflects the state of knowledge and prevents acting on a false sense of certainty from intuition. The model's value is in defining the bounds of what's possible.
When modeling a complex issue like malaria bed nets, don't start with every variable. Begin with a simple model of the 5-6 core drivers. This makes the model easier to understand, hold in your head, and debug. Add complexity later, once the basic dynamics are established and validated.
To compare disparate causes like funding art vs. saving lives, use extreme hypotheticals. If someone agrees saving 100 children is better than a tiny chance of art for billionaires, they've conceded comparability. The debate then shifts to negotiating where the line is drawn, not whether one can be drawn.
Thought experiments like the 'River of Drowning Children' suggest strict altruism requires sacrificing your entire life. However, most plausible ethical theories reject this maximal demandingness. They acknowledge that your own well-being, family, and personal projects also hold moral weight and should not be entirely sacrificed.
For robust, high-stakes grantmaking, separate analysis into three layers. First, empirical uncertainty (what will happen?). Second, normative uncertainty (what outcomes are most valuable?). Third, meta-normative uncertainty (how should we aggregate different moral views and risk preferences?). This framework clarifies thinking.
It's tempting to think you can intuit the few factors a decision hinges on. This is often wrong. Complex systems have non-obvious leverage points. The process of building an explicit model reveals which variables have the most impact—a discovery you can't reliably make with intuition alone.