Sensemaking and learning during the Covid-19 pandemic: A complex adaptive systems perspective on policy decision-making

Governments worldwide are under enormous pressure to effectively and promptly address the increasingly complex crisis presented by the Covid-19 pandemic. To understand the difficulties inherent to policymakers’ sensemaking and learning processes during this unprecedented challenge, this article develops a perspective rooted in complexity theory. We highlight that, just as complex adaptive systems, societies affected by the pandemic and by the subsequent containment policies present non-linear and unpredictable outcomes, which highly depend on the social systems’ initial states and on the behavioral rules governing the actions and interactions of the agents composing the systems. This analysis underlines that any decision-making process in a highly complex crisis such as the Covid-19 pandemic is inherently inaccurate and short-sighted. Far, however, from suggesting a policy paralysis, with this perspective we highlight the need to embed complexity thinking in policy decision-making and we present a roadmap for learning based on a flexible and adaptive approach, locally optimal solutions, and the need for international cooperation and transparent dissemination of data.