Deciding how to make climate change adaptation decisions

Siders, A.R.; Pierce, Andrea

doi:10.1016/j.cosust.2021.03.017

Cited by 22 publications

(10 citation statements)

References 87 publications

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“…Our method, assessing pathway performances using simple, quantitative criteria, requires a more comprehensive evaluation, incorporating qualitative metrics to realistically reflect current and future needs (Bosomworth et al., 2017; Siders & Pierce, 2021). The independent, impact‐based ATP decision rules oversimplify the integrated understanding sectors might have of their dynamics.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

Evaluating Adaptation Pathways in a Complex Multi‐Risk System

Schlumberger,

Haasnoot,

Aerts

et al. 2024

Earth's Future

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Disaster Risk Management (DRM) is complex due to interacting climate risks from interacting hazards and sectors. We develop a synthetic multi‐risk test case to explore the effects of these interactions on decision‐making under deep uncertainty. The test case accounts for changes in hazard impacts and occurrence frequency due to interactions between floods and droughts. Interactions between the shipping, housing and agricultural sectors are considered as well. We use this test case to explore the utility of the Dynamic Adaptive Policy Pathways for Multi‐Risk (DAPP‐MR) framework. DAPP‐MR has been introduced to assess DRM policies' effectiveness under deep uncertainties and to develop integrated adaptive strategies considering interactions across hazards, sectors and time. With the test case, we show that the stage‐wise approach of DAPP‐MR, which gradually increases the complexity of the analysis, can facilitate the evaluation process. Earlier stages of the analysis can be used to establish the direct cause‐effect relations, later stages allow us to identify whether additional interacting factors have a significant effect on the direct cause‐effect relations. As a result, decision‐makers can gain insights into dependencies and their relevance for developing short‐to long‐term strategies under deep uncertainty. We show that multi‐risk interactions can lead to non‐linear effects that influence the outcome of the policy analysis, for example, the indirect influence of a decision in one sector on another. Future work could investigate further improving the operationalization of this staged approach as well as extending the set of uncertainties, dynamics and decision‐rules accounted for in the multi‐risk test case.

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Section: Discussion and Recommendationsmentioning

confidence: 99%

Evaluating Adaptation Pathways in a Complex Multi‐Risk System

Schlumberger,

Haasnoot,

Aerts

et al. 2024

Earth's Future

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show abstract

“…In individual decision making it has been shown that individuals can adapt their social integration strategies to the task at hand (Payne et al, 1988;Rieskamp & Otto, 2006;Griffiths et al, 2019), with strategies depending on cognitive abilities (Mata et al, 2007), time pressure (Rieskamp & Hoffrage, 2008), past performance (Billinger et al, 2014), and knowledge of cues (Binz et all., 2021). In social contexts, meta-level strategies have been studied in advice taking (Soll & Larrick, 2009;Molleman et al, 2020) and in teamwork (Giannoccaro et al, 2020), but more research is still needed on how collectives decide to use different strategies (Siders & Pierce, 2021), and in particular how they decide to restructure their networks (but see Akçay 2018;Smolla & Akçay, 2019;Almaatouq 2020). Answering these questions is further complicated by the presence of social norms and more formal institutions that limit the set of strategies and network structures that collectives can choose from.…”

Section: Research Questions Emerging From the Collective Adaptation F...mentioning

confidence: 99%

Beyond collective intelligence: Collective adaptation

Galešić

Barkoczi

Berdahl

et al. 2023

J. R. Soc. Interface.

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We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for ‘intelligent’ collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.

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“…We also include approaches for the sub-selection (e.g., merging, selecting and/or filtering) of climate models and associated information. Relationships between models are captured (e.g., models from the same model family with similar components or assumptions [62]) as well as their suitability for different adaptation management decision strategies [63]. Finally, we consider domain-specific information (i.e., exposure and vulnerability) related to the implementation into an adaptation management strategy and the decisions that need to be made.…”

Section: A Conceptual Framework For Applying Hybrid CI To Climate Ser...mentioning

confidence: 99%

Hybrid Collective Intelligence for Decision Support in Complex Open-Ended Domains

Trianni

Nuzzolese

Porciello

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Human knowledge is growing exponentially, providing huge and sometimes contrasting evidence to support decision making in the realm of complex problems. To fight knowledge fragmentation, collective intelligence leverages groups of experts (possibly from diverse domains) that jointly provide solutions. However, to promote beneficial outcomes and avoid herding, it is necessary to (i) elicit diverse responses and (ii) suitably aggregate them in a collective solution. To this end, AI can help with dealing with large knowledge bases, as well as with reasoning on expert-provided knowledge to support decision-making. A hybrid human-artificial collective intelligence can leverage the complementarity of expert knowledge and machine processing to deal with complex problems. We discuss how such a hybrid human-artificial collective intelligence can be deployed to support decision processes, and we present case studies in two different domains: general medical diagnostics and climate change adaptation management.

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Deciding how to make climate change adaptation decisions

Cited by 22 publications

References 87 publications

Evaluating Adaptation Pathways in a Complex Multi‐Risk System

Evaluating Adaptation Pathways in a Complex Multi‐Risk System

Beyond collective intelligence: Collective adaptation

Hybrid Collective Intelligence for Decision Support in Complex Open-Ended Domains

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