Approximations can aim at having close to optimal value or, alternatively, they can aim at structurally resembling an optimal solution. Whereas value-approximation has been extensively studied by complexity theorists over the last three decades, structural-approximation has not yet been defined, let alone studied. However, structuralapproximation is theoretically no less interesting, and has important applications in cognitive science. Building on analogies with existing valueapproximation algorithms and classes, we develop a general framework for analyzing structural (in)approximability. We identify dissociations between solution value and solution structure, and generate a list of open problems that may stimulate future research.

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