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How do historians learn from the past to identify patterns, make predictions, and improve their work? Are their heuristics helpful for further advancing the development of machine learning? In this workshop, Professor Marnie Hughes-Warrington explored examples of how prize-winning historians reason about the past, including through dynamic spatio-temporal scaling, the use of conditionals and counterfactuals, and in the citation of temporal markers to fix and give credence to information.