Alfonso Arroyo-Santos , Luis A. Bojorquez-Tapia, Yosune Miquelajauregui

There is a growing concern about the unforeseen negative consequences of climate change. In response, important scholarly efforts have produced valuable frameworks to help decisionmakers construct adaptation plans. Drawing on the success and failures of current adaptation plans, these frameworks have been developed to prevent maladaptations, meaning the unforeseen negative consequences of adaptation plans. We argue that while current frameworks focusing on planning and risk management are crucial, the inherent uncertainty of climate change requires a more nuanced approach. We propose a novel "adaptation grid" that aligns existing frameworks with Decision Making under Deep Uncertainty (DMDU). This grid leverages insights from current frameworks to structure different kinds of uncertainty and how they impact adaptation planning. Our approach recognizes that adaptation strategies lie on a continuum of success and failure. We advocate for indicators that go beyond success measurement, instead focusing on acceptable degrees of failure, learning from past actions, and identifying early warning signals. By incorporating a richer understanding of uncertainty, DMDU offers a comprehensive cognitive, methodological and theoretical framework for constructing qualitative observations into measurable indicators, imagining alternative futures, and implementing a management-learning system to help us better navigate climate change uncertainties.