Dynamical Symmetries and Model Validation
Conference paper
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Abstract
I introduce a new method for validating models—including stochastic models—that gets at the reliability of a model’s predictions under intervention or manipulation of its inputs and not merely at its predictive reliability under passive observation. The method is derived from philosophical work on natural kinds, and turns on comparing the dynamical symmetries of a model with those of its target, where dynamical symmetries are interventions on model variables that commute with time evolution. I demonstrate that this method succeeds in testing aspects of model validity for which few other tools exist.
Notes
Acknowledgements
I am grateful to the participants in the 2015 Algorithms and Complexity in Mathematics, Epistemology and Science (ACMES) conference for insightful discussion of an early algorithm for discovering dynamical kinds, to Cosmo Grant for pointing out a physical inconsistency in the first version of one of my examples, and to Nicolas Fillion for helpful comments on a previous draft of this paper. The work presented here was supported by the National Science Foundation under Grant No. 1454190.
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