Decision Theory via Model-Free Generalized Fiducial Inference

Building on the recent development of the model-free generalized fiducial (MFGF) paradigm (Williams 2023) for predictive inference with finite-sample frequentist validity guarantees, in this paper, we develop an MFGF-based approach to decision theory. The MFGF paradigm establishes a formal connection between fiducial inference, conformal prediction, and imprecise probability theory. Beyond the utility of the new tools we contribute to the field of decision theory, our work builds on these connections. In our paper, we establish pointwise and uniform consistency of an MFGF upper risk function as an approximation to the true risk function via the derivation of nonasymptotic concentration bounds, and our work serves as the foundation for future investigations of the properties of the MFGF upper risk.