Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.

We develop a framework for inducting compact state representations (finite automata) that summarize estimated next-token probabilities. This provides a common language between black-box sequence models and the dynamic restrictions imposed by economic models. We illustrate the framework through applications to collusive behavior and cooperation in repeated games.

This talk presents work-in-progress.