The Diet Problem
Updated 2026-08-08
INTRODUCTION
English translation pending.
CORE DEFINITION
A classic linear programming problem formulated and solved by George Stigler in 1945. The task is to select quantities of available foods that satisfy a set of nutritional requirements at the lowest total cost. Stigler's solution relied on flour, cabbage, and dried beans, which met every constraint yet no person could eat for long. The case became a standard illustration of optimization ignoring soft constraints.
SCAFFOLDING EFFECT
Reduce cognitive load
- Model soft constraints: list the human preferences and habits your optimization is silently treating as free - Test feasibility: ask whether the optimal solution would actually be followed by real people - Iterate the model: add the ignored constraints and re-solve rather than defending the mathematical answer
Anchor fast decisions
A linear program finds the cheapest point that satisfies its stated constraints, and anything not written into those constraints is treated as costless. Taste, variety, culture, and satiety are therefore ignored, so the solver exploits them freely. The result is mathematically optimal and practically useless, which shows that the failure lies in the model's constraints rather than in the optimization itself.
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/Stigler_dietverified
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