AI在医疗系统中能否带来变革，关键看是否改变激励机制。
Incentives, Equilibria, and the Limits of Healthcare AI: A Game-Theoretic Perspective
- 用博弈论分析三种AI部署模式：减负、监控和改激励。
- 仅优化任务或信息流的AI常被现有行为模式吸收，难改结果。
- 真正有效的是重构风险分配的机制设计，影响稳定行为模式。
基于住院容量管理中的协调难题，本文描述了三种典型的AI部署形式：减少努力的技术、注重可观测性的系统，以及改变底层激励结构的干预措施。努力减少和可观测性提升可改善现有行为模式下的表现，但通常无法改变个体理性的最优选择，因而往往被纳入现有均衡之中。相比之下，通过重新分配或限定局部风险来改变局部行动与下游后果映射关系的干预，能够改变系统的稳定行为。这些机制层面的干预差异不在于技术复杂度，而在于其与制度激励的互动方式。研究指出，对AI带来的系统级收益预期，应取决于部署是否改变激励结构，而非仅优化任务或信息流。这对医疗机构和政策制定者在数字技术采购、治理与评估方面具有实际意义。
原文摘要 · Abstract (English)
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. The analysis suggests that expectations of system-level gains from AI should be conditioned on whether a deployment changes incentives rather than optimising tasks or information flows alone. For healthcare organisations and policymakers, this has practical implications for procurement, governance, and evaluation of digital technologies.
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