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...It focuses on flexible model construction rather than a single fixed estimator, making it useful for researchers who want to experiment with different utility functions and optimization setups. ax is especially relevant for machinelearning and econometrics workflows that need scalable, differentiable approaches to choice modeling. Its main value is giving researchers a modern, accelerator-friendly framework for estimating and analyzing discrete choice behavior.