Rawlsian Maximin and Risk Position¶

Why It Matters¶

Provides a systematic way to think about distributive fairness without interpersonal utility comparison. Also reveals how risk attitudes shape fairness intuitions - maximin is extremely risk-averse; utilitarianism is risk-neutral.

The Key Move¶

When evaluating alternative distributions, ask: If I didn't know which position I'd occupy (veil of ignorance), which would I choose? This forces consideration of worst-case positions, not just average or best-case outcomes.

Classic Application¶

Rawls's argument for redistributive social insurance. Behind veil of ignorance (not knowing if you'll be talented, healthy, born to advantage), rational to prefer system that maximizes minimum position - even if it somewhat reduces average welfare.

Surprising Application¶

Game design for random team assignment. If players don't know which team they'll join, they design fairer team balance - no incentive to bias toward one side. Used in some competitive gaming to force fairness consideration.

Failure Modes¶

• Assuming maximin is THE correct principle rather than modeling extreme risk aversion
• Ignoring that real people know their positions (veil of ignorance is hypothetical)
• Using maximin to justify any sacrifice of better-off for minimal worst-off gain (leads to extreme leveling)
• Treating all uncertainty as morally equivalent to veil-of-ignorance uncertainty

Go Deeper¶

Rawls, "A Theory of Justice" Part I; Harsanyi, "Can the Maximin Principle Serve as a Basis for Morality?" for critique.