Balancing the Risk of Tipping: Early Warning Systems from Detection to Management - Florian Diekert; Daniel Heyen; Frikk Nesje; Soheil Shayegh (Center for Economic Studies and ifo Institute (CESifo), 2024)
Early warning signals (EWS) of imminent regime shifts can be identified through the observation of a system's behavior under increasing stress and before crossing a tipping point. Despite many advances in the detection of EWS in recent years, EWS are yet to find direct application in management. Here, we focus on operationalizing the EWS information in an early warning system consisting of a tipping indicator (e.g., autocorrelation), whose value increases as the system approaches the tipping point, and a trigger value, beyond which an EWS is sent. We demonstrate how such an early warning system allows managers to balance the risk of tipping by providing information for updating their belief about the location of the tipping point. In particular, deployment of an early warning system results in taking more cautious early steps while it encourages more risk taking behavior in later stages if no EWS is sent. We uncover a tension between better information about the location of the tipping point and increased risk of crossing it as a result of EWS. Our framework complements the emerging EWS knowledge in the natural sciences with a better understanding of how, when, and why EWS improve management.
Keywords: C61; D83; Q54; 330; catastrophic regime shifts; tipping points; early warning signals; learning; optimal ecosystem management