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

===== 17.3.1.1.2 Types and capacity of models to support decision-making =====

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‘Descriptive models’ of socio-biophysical systems and their responses to different drivers ( [[#Argyris--2017|Argyris and French, 2017]] ; [[#French--2018|French and Argyris, 2018]] ; [[#Saltelli--2020|Saltelli et al., 2020]] ) and ‘prescriptive models’, which capture the beliefs, values and objectives of decision makers and stakeholders ( [[#Parnell--2013|Parnell et al., 2013]] ; [[#Keisler--2014|Keisler et al., 2014]] ; [[#French--2018|French and Argyris, 2018]] ), provide the foundations of sense making ( ''high confidence'' ) and thereby influencing the options and choices available in the phase of analysis and exploration ( ''medium confidence'' ) ( [[#Gorddard--2016|Gorddard et al., 2016]] ).

Socio-biophysical models may be qualitative network models, statistical models or dynamic mathematical models ( [[#Melbourne-Thomas--2017|Melbourne-Thomas et al., 2017]] ). Qualitative network modelling can help assess the nature and consequences of the interactions, as well as facilitate understanding of possible structures to be used in dynamic models for assessing long-term adaptation options ( [[#Reckien--2013|Reckien et al., 2013]] ; [[#Reckien--2014|Reckien, 2014]] ; [[#Reckien--2014|Reckien and Luedeke, 2014]] ; [[#Symstad--2017|Symstad et al., 2017]] ). These approaches help articulate the direct and indirect effects of fixed, long-term engineering or structural adaptations. Dynamic stochastic modelling ( [[#Fulton--2014|Fulton and Link, 2014]] ; [[#Ianelli--2016|Ianelli et al., 2016]] ) has been used to assess short- to medium-term interactions of more dynamic and variable sectors, such as those with annual adjustments and management of water, agriculture, land and marine uses ( [[#Holsman--2019|Holsman et al., 2019]] ; [[#Hollowed--2020|Hollowed et al., 2020]] ; [[#Bahri--2021|Bahri et al., 2021]] ). On a longer time frame, scenarios are used to test long-term interactions but often with less variability and chance ( [[#Giupponi--2013|Giupponi et al., 2013]] ; [[#Adam--2014|Adam et al., 2014]] ; [[#Rosenzweig--2017|Rosenzweig et al., 2017]] ).

Many sensitivity analyses based on scenarios, including procedures to randomise across model uncertainty, relate to descriptive dynamic mathematical models with the user of the models characterised as an objective observer ( [[#Borgonovo--2016|Borgonovo and Plischke, 2016]] ; [[#Ferretti--2016|Ferretti et al., 2016]] ; [[#Symstad--2017|Symstad et al., 2017]] ; [[#French--2020|French, 2020]] ). Bayesian approaches enable these descriptive analyses to take account of the subjective choices in model construction and implementation ( [[#Abbas--2015|Abbas and Howard, 2015]] ; [[#Sperotto--2017|Sperotto et al., 2017]] ; [[#Jäger--2018|Jäger et al., 2018]] ; [[#Sperotto--2019|Sperotto et al., 2019]] ; [[#French--2020|French, 2020]] ). Organising descriptive analyses and deciding on a suitable option across a diversity of opinions among stakeholders use prescriptive processes, which can be supported with prescriptive modelling tools ( [[#Williamson--2012|Williamson and Goldstein, 2012]] ; [[#Gelman--2013|Gelman et al., 2013]] ; [[#Abbas--2015|Abbas and Howard, 2015]] ; [[#Dias--2018|Dias et al., 2018]] ; [[#Phan--2019|Phan et al., 2019]] ; [[#Hanea--2021|Hanea et al., 2021]] ). These approaches are subjective, in that they are constrained or directed by the particular views and emphases of the decision-making group ( [[#Gorddard--2016|Gorddard et al., 2016]] ). Not all tools are appropriate for all these activities.

Decision makers will be better able to choose decision-analytic methods when they have an understanding of the types, scale and breadth of uncertainties around the climate risk ( ''high confidence'' ) ( [[#Symstad--2017|Symstad et al., 2017]] ). The ''Cynefin'' framework ( [[#Snowden--2002|Snowden, 2002]] ; [[#French--2013|French, 2013]] ) is a policy-driven framework that broadly categorises the decision context of uncertainty within which decision makers and policy analysts may find themselves ( ''medium confidence'' ) ( [[#Hurlbert--2019|Hurlbert et al., 2019]] ; [[#Helmrich--2020|Helmrich and Chester, 2020]] ). As ''Cynefin'' has helped frame previous IPCC presentations on contexts of uncertainty ( [[#Hurlbert--2019|Hurlbert et al., 2019]] ) and has a community of practice to consult on its use ( [[#French--2020|French, 2020]] ), it is used here, also because it considers the uncertainty in knowledge around cause and effect in general terms, rather than specifically focusing on uncertainty in formal models. [[#Helmrich--2020|Helmrich and Chester (2020)]] show how ''Cynefin'' can be used to frame climate adaptation decision-making in the infrastructure sector.

The ''Cynefin'' contexts relate to how well the system is understood for knowing precisely the outcomes of actions that may be taken, ranging from known, knowable and complex to chaotic. If a context is known or knowable, then it will be possible to build sophisticated models and make sound predictions. If the context is complex and chaotic the outcomes of actions will be less predictable, no matter how complex the models may be, although more complex dynamic models may be useful to test ‘what if’ scenarios in these cases ( [[#Marchau--2019|Marchau et al., 2019]] ). Under complex and chaotic circumstances an ensemble of models and approaches may be needed to help categorise a satisfactory ‘solution space’ across the broad knowledge of relationships and dependencies, but will need to have iterative processes to update and refine adaptations as knowledge improves ( [[#Marchau--2019|Marchau et al., 2019]] ).

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