Abstract

This work extends the core Machine Decision-Making Ability (MDMA) framework into a broader scientific context by proposing a structured set of cross-disciplinary theoretical extensions. While the core preprint formalized MDMA as a measure of autonomous decision capacity grounded in physical state transitions, here we examine how fundamental constraints and affordances from physics, information theory, and complexity shape that capacity. We organize nine extensions into four families (Scaling Laws; Quantum and Relativistic Limits; Mind and Complexity; Post-Silicon Frontiers) and formulate each as an MDMA-aware principle with an associated research question. The goal is not to settle debates, but to offer a coherent roadmap for measurement, limits, and design that can guide future experimental and theoretical work across substrates.