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ICESCR Adequacy Standard

Minimum Cost of a Human Right

Jurisdiction

Ratifying states of the International Convenant on Economic, Social and Cultural Rights

Primary Rules

Art. 2 para. 1 and Art. 11 International Convenant on Economic, Social and Cultural Rights (ICESCR); CESCR General Comment 12

Target Audience

Litigants, courts

Project Hash ID

#I338

Project Category Name

Palamedes

When the right to adequate nutrition can be computed, the state's obligation becomes a question of mathematics, not judgment.

Article 11 of the International Covenant on Economic, Social and Cultural Rights obliges every ratifying state to ensure that every person has access to adequate food. Some national courts have consistently declined to treat this provision as directly applicable, holding that the adequacy standard is too indeterminate to generate individual, justiciable claims without implementing legislation.

This project challenges that reasoning at its root. Using a Mixed-Integer Linear Programme (MILP) with Branch-and-Cut solving, it demonstrates that the minimum cost of a nutritionally adequate seven-day diet is precisely computable from publicly available data: published dietary reference values, food composition databases and retail prices. If the minimum is computable, the indeterminacy objection dissolves.

The programme's objective function, minimize total food expenditure subject to nutritional constraints, directly mirrors the legal question a court must answer when reviewing the adequacy of a social assistance payment: does this amount purchase a nutritionally adequate diet? The model transforms that question from a matter of political judgment or household survey estimation into one of verifiable calculation.

The model operates as litigation support: an objective, reproducible benchmark for constitutional challenges, with Art. 11 ICESCR serving as interpretive authority for the content of the constitutional minimum.

What is a Mixed-Integer Linear Programme?

A Mixed-Integer Linear Programme (MILP) is a mathematical method for finding the best possible answer to a question that has rules. "Best" means something you can measure, in this project, lowest cost. The "rules" are constraints: conditions every answer must satisfy before it can even be considered. Here they are the nutritional requirements established by dietary science. And "linear" means that all relationships in the model are strictly proportional: twice the oats costs twice as much and provides twice the protein.

As the model is realistic, it is also computationally hard. The solver must search an astronomically large space of on/off combinations to find the cheapest one that satisfies every nutritional rule simultaneously.

Work Steps

Resource and structure the data

Two large data frames feed the model. The first maps every ingredient to its nutritional profile, protein, carbohydrates, fat and and calories per 100g, together with its retail prices drawn from multiple competing retailers so that the solver can identify the cheapest compliant source for each ingredient. The second is a structured recipe universe: per-portion ingredient lists, meal types (breakfast, lunch, dinner), food categories and cooking instructions. National food composition databases or the comprehensive Nutrition Coordinating Center Food & Nutrition Database (NCCDB) at the University of Minnesota, containing over 17'000 foods with lab-analyzed data on more than 70 nutrients, can be used.

Translate the legal question into an objective function

The legal question, what is the minimum cost of adequate nutrition for one person over seven days?, becomes the MILP objective function: minimize the sum of ingredient costs subject to nutritional constraints, portion logic, diversity requirements and meal weight bounds. Binary variables track whether each recipe is selected for each meal slot. Continuous variables control portion scaling. The Branch-and-Cut algorithm finds the global minimum, not a heuristic approximation, within a set solve window, making the result reproducible and court-presentable.

Compute

With the objective function and all constraints assembled into a single problem object, the CBC Branch-and-Cut solver is invoked. It does not try combinations at random. It systematically partitions the solution space, branching on binary recipe-selection variables, tightening bounds with cutting-plane inequalities at each node, until it can prove that no feasible solution with a lower cost exists. The result is not a good answer. It is the best possible answer, with a mathematical certificate of optimality attached.