Incentives, Equilibria, and the Limits of Healthcare AI: A Game-Theoretic Perspective 文章

arXiv:2603.28825v2 Announce Type: replace-cross Abstract: Using a stylised coordination problem drawn from inpatient capacity management, three archetypal forms of AI deployment are described: effort-reducing technologies, observability-oriented systems, and interventions that alter underlying incentive structures. Effort reduction and observability may improve performance within existing patterns of behaviour but do not, in general, change which actions are individually rational. As a result, such interventions are typically absorbed into existing equilibria. By contrast, interventions that modify how local actions map to downstream consequences by redistributing or bounding local risk can change stable system behaviour. These mechanism-level interventions differ not in technical sophistication but in their interaction with institutional incentives.