Improving forest decision-making through complex system representation: A viability theory perspective

Abstract

Forests are complex adaptive systems (CAS) featuring dynamics that can take centuries to unfold. Managing them for multiple objectives (e.g. financial performance, climate regulation, biodiversity conservation, watershed protection) in the face of multiple risks (e.g. market fluctuations, illegal logging, natural disturbance) involves making decisions under deep and pervasive uncertainty. Through a systematic literature review, we explore quantitative approaches for integrating uncertainty and complex-systems theory into forest management planning and examine common challenges like dimensionality, tractability and realism. In addition to comparatively well-studied techniques from operations research and portfolio theory, we highlight a largely-overlooked framework known as viability theory. Whereas approaches like stochastic programming and robust optimization seek to maximize performance given predefined outcome probabilities and uncertainty spaces, respectively, viability theory aims to identify executive rules that can delineate the boundaries of the safe-operating space based on system dynamics. We discuss the potential utility of this novel approach to capturing uncertainty and examine potential barriers to improving forest decision-making and management.