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We recount recent history behind building compact models of nonlinear,complex processes and identifying their relevant macroscopic patterns or"macrostates". We give a synopsis of computational mechanics, predictiverate-distortion theory, and the role of information measures in monitoringmodel complexity and predictive performance. Computational mechanics provides amethod to extract the optimal minimal predictive model for a given process.Rate-distortion theory provides methods for systematically approximating suchmodels. We end by commenting on future prospects for developing a generalframework that automatically discovers optimal compact models. As a response tothe manuscript cited in the title above, this brief commentary correctspotentially misleading claims about its state space compression method andplaces it in a broader historical setting.

Understanding and Designing Complex Systems: Response to "A framework  for optimal high-level descriptions in science and engineering---preliminary  report"