In this issue, we highlight a call to arms to the Artificial Life community to join the effort to address complex, whole-systems problems in health care. Many problems that society wishes to address in population health are clearly problems of managing complex adaptive systems. They involve making interventions in systems with multiple interacting causal connections, which span domains from physiological to economic. Additionally, of course, the individuals whose health we ultimately wish to improve adapt and change their behavior in response to medical or policy interventions. But how do we do it? In complex adaptive systems, as the following article reminds us, individuallevel interventions are often not the most effective. Rather we might wish to change the conditions or structures of interaction in which individual behaviors play out. Taking a whole-systems view of the problem can allow us to identify higher-level levers that may allow us to reach broader swathes of the population simultaneously. We might be able to change what emerges or is stable in the system, without relying on the limited agency of individuals in a constrained and tangled context. Systems-based and complexity methods can help decision makers to understand their system at a level that makes such interventions possible to envisage and design.

中文翻译：

---

从人口健康政策的压倒性转变为可操作的复杂性：ALife 可以提供帮助吗？

在本期中，我们强调呼吁人工生命社区加入解决医疗保健中复杂的全系统问题的努力。社会希望在人口健康方面解决的许多问题显然是管理复杂的适应系统的问题。它们涉及对具有多种相互作用因果关系的系统进行干预，这些因果关系跨越从生理到经济的各个领域。此外，当然，我们最终希望改善健康状况的个人会根据医疗或政策干预来适应和改变他们的行为。但是我们该怎么做呢？在复杂的适应系统中，正如下面的文章提醒我们的那样，个体层面的干预往往不是最有效的。相反，我们可能希望改变个体行为发生的交互条件或结构。从整个系统的角度看待问题可以让我们确定更高级别的杠杆，这可能使我们能够同时覆盖更广泛的人群。我们或许能够改变系统中出现的或稳定的东西，而无需在受限和纠结的环境中依赖个人的有限能动性。基于系统和复杂性的方法可以帮助决策者在一定程度上了解他们的系统，从而可以设想和设计此类干预措施。