Sterman John D
MIT Sloan School of Management, 30 Wadsworth Street, Room E53-351, Cambridge, Massachusetts 02142, USA.
Am J Public Health. 2006 Mar;96(3):505-14. doi: 10.2105/AJPH.2005.066043. Epub 2006 Jan 31.
Policies to promote public health and welfare often fail or worsen the problems they are intended to solve. Evidence-based learning should prevent such policy resistance, but learning in complex systems is often weak and slow. Complexity hinders our ability to discover the delayed and distal impacts of interventions, generating unintended "side effects." Yet learning often fails even when strong evidence is available: common mental models lead to erroneous but self-confirming inferences, allowing harmful beliefs and behaviors to persist and undermining implementation of beneficial policies. Here I show how systems thinking and simulation modeling can help expand the boundaries of our mental models, enhance our ability to generate and learn from evidence, and catalyze effective change in public health and beyond.
促进公众健康和福利的政策往往会失败，或者使它们原本试图解决的问题恶化。基于证据的学习应该能够防止这种政策阻力，但在复杂系统中的学习往往薄弱且缓慢。复杂性阻碍了我们发现干预措施的延迟和间接影响的能力，从而产生意想不到的“副作用”。然而，即使有强有力的证据，学习也常常失败：常见的思维模式会导致错误但自我证实的推断，使有害的信念和行为持续存在，并破坏有益政策的实施。在这里，我展示了系统思维和模拟建模如何有助于扩展我们思维模式的边界，增强我们从证据中生成和学习的能力，并促进公共卫生及其他领域的有效变革。
