Minimax Regret
Updated 2026-08-13
INTRODUCTION
English translation pending.
CORE DEFINITION
The decision rule computes, for each available action and each possible state of the world, the regret, defined as the difference between what that action yields in that state and what the best available action would have yielded there. It then selects the action whose maximum regret across all states is the smallest. Unlike maximizing expected utility, the target is not the best average outcome but the mildest worst-case remorse, which makes the rule suited to risk-averse decisions under deep uncertainty.
SCAFFOLDING EFFECT
Reduce cognitive load
- Compute regret per state: compare every option against the best alternative available in that state. - Take the worst case: find the largest regret that each option could possibly suffer. - Minimize that worst: pick the option whose largest regret turns out to be smallest.
Anchor fast decisions
Regret is measured against what was possible, not against what occurred, so it weights foregone benefit rather than realized loss. For every action, the worst state determines its exposure, and the rule selects the action with the lowest such ceiling. Because the criterion targets the worst-case gap rather than the average, it gives up the upside that a probability-weighted choice would pursue, in exchange for protection against the outcome that would hurt most to look back on.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Regret_(decision_theoryverified
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