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This Article formulates a mathematical model that\u00a0frames the behavioral and cognitive framework of law search as a sequential decision process. The model has two components: first, a model of the legal corpus as a<jats:italic>search space<\/jats:italic>and second, a model of the search process (or<jats:italic>search strategy<\/jats:italic>) that is compatible with that environment. The search space has the structure of a \u201cmulti-network\u201d\u2014an interleaved structure of distinct networks\u2014developed in earlier work. In this Article, we develop and formally describe three related models of the search process. We then implement these models on a subset of the corpus of U.S. Supreme Court opinions and assess their performance against two benchmark prediction tasks. The first is to predict the citations in a document from its semantic content. The second is to predict the search results generated by human users. For both benchmarks, all search models outperform a null model with the learning-based model outperforming the other approaches. 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