Editing IPCC:AR6/WGII/Chapter-17 (section)

===== 17.3.1.1.3 Uncertainty and attitudes to risk =====

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Uncertainty does not just relate to what might happen given climate drivers or adaptations, but also to how much one values potential consequences ( [[#Butler--2016|Butler et al., 2016]] ; [[#Beven--2018a|Beven et al., 2018a]] ; Cross-Chapter Boc DEEP; [[#Beven--2018b|Beven et al., 2018b]] ; [[#French--2020|French, 2020]] ) ( ''high confidence'' ); the balance between how particular decision analyses address uncertainties relating to the external world (descriptive models) and those relating to the values driving the decision-making (prescriptive models) is important ( [[#Butler--2016|Butler et al., 2016]] ). Some analyses partially ignore uncertainties relating to the former in order to focus on conflicts in the values held by different stakeholders and help structure debate ( [[#Korhonen--2020|Korhonen and Wallenius, 2020]] ; [[#French--2020|French, 2020]] ), while others build very sophisticated models of the external world to predict potential consequences, but in doing so lose transparency and risk becoming untrustworthy black boxes to many stakeholders ( ''low confidence'' ) ( [[#Peterson--2020|Peterson and Thompson, 2020]] ).

Much of the readily available literature on how uncertainties affect decision-making relates to the uncertainty in the biophysical models, with a recognition that the choice of tools will be influenced by the types of uncertainty to be addressed ( [[#Le%20Cozannet--2017|Le Cozannet et al., 2017]] ; [[#Symstad--2017|Symstad et al., 2017]] ; [[#Beven--2018a|Beven et al., 2018a]] ; [[#Beven--2018b|Beven et al., 2018b]] ; [[#Durbach--2020b|Durbach and Stewart, 2020b]] ; [[#French--2020|French, 2020]] ). While terminology varies among disciplines, three types of uncertainty are important in understanding assessments of the future from descriptive models: epistemic (uncertainty in model construction relating to the lack of knowledge about the system being represented), analytic (the degree to which a model fits observations, and its accuracy) and stochastic (the natural variability or randomness in the system). The probability of an event arising in the future is determined from all three uncertainties, noting that stochastic uncertainty is a property of the system rather than a limitation of research ( [[#Le%20Cozannet--2017|Le Cozannet et al., 2017]] ; [[#Beven--2018a|Beven et al., 2018a]] ; [[#Beven--2018b|Beven et al., 2018b]] ).

Uncertainty in what constitutes a risk of concern is increasingly identified as important to consider when managing risk (Chapter 16; [[#Butler--2016|Butler et al., 2016]] ; [[#Prober--2017|Prober et al., 2017]] ; [[#French--2020|French et al., 2020]] ; [[#Reis--2020|Reis and Shortridge, 2020]] ). The uncertainty here arises from what is an acceptable risk. Acceptability relates to the value or importance of the consequence, which may include moral and ethical uncertainties ( [[#Prober--2017|Prober et al., 2017]] ), as well as how ambiguous the understanding of the consequence may be between different groups ( [[#Beven--2018a|Beven et al., 2018a]] ; [[#Beven--2018b|Beven et al., 2018b]] ). The development of strategies to ameliorate risk will benefit from considering these two uncertainties in specifying the risk to be managed ( [[#Prober--2017|Prober et al., 2017]] ; [[#French--2020|French et al., 2020]] ) because they can help set boundaries on a required likelihood of success, rather than simply casting stakeholders or decision makers as risk averse or risk tolerant, and can help identify and accept pathways of success ( [[#Gregory--2012|Gregory et al., 2012]] ). This can be important when decisions need to be made well in advance of the actions needing to take effect, such as for many climate risks (Chapter 1; Chapter 16; [[#17.2|Section 17.2.3]] ; Cross-Chapter Box DEEP in this Chapter).

Elicitation methods help reduce these uncertainties ( ''high confidence'' ) ( [[#Butler--2016|Butler et al., 2016]] ; [[#Prober--2017|Prober et al., 2017]] ; [[#Symstad--2017|Symstad et al., 2017]] ; [[#Beven--2018b|Beven et al., 2018b]] ). In addition, informal decision processes can assist in developing consensus in approaches and outcomes ( [[#Orlove--2020|Orlove et al., 2020]] ).

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