Deep Uncertainty

Walker, Warren E.; Lempert, Robert J.; Kwakkel, Jan H.

doi:10.1007/978-1-4419-1153-7_1140

Cited by 173 publications

(182 citation statements)

References 28 publications

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“…However, the current EBSD approach does not fully explore the wider range of possible futures, the so called "deep" uncertainties (Walker et al 2013a), or the full range of potential solutions and trade-offs. Nor does it promote examination and security against the more extreme projected scenarios; such as severe changes in individual supply source availability at peak demand periods (Environment Agency et al 2012) or highly unexpected events (the so called black swans) (Bryant and Lempert 2010).…”

Section: The Current Approach Within the Uk As Stated In The Environmentioning

confidence: 99%

“…frameworks and approaches, which demonstrate notable potential in handling "deep" uncertainties in regard to WRM adaptive planning. Walker et al (2013a) defines the point at which uncertainties become "deep" as when one can enumerate multiple plausible alternatives of the future but cannot rank the alternatives in terms of perceived likelihood. In this paper "deep" uncertainty is defined as above and a DMM, in a WRM context, denotes any method that helps a decision maker identify the "best" adaptation strategy(ies) over a long term planning horizon that are either automatically generated or selected from a range of pre-defined solutions.…”

Section: The Current Approach Within the Uk As Stated In The Environmentioning

confidence: 99%

“…The same flow factors were used to perturb the inflows to Weir Wood reservoir and flows in the River Arun to ensure the system is modelling the same patterns of weather and climate change throughout the system at the same time. As the likelihoods of the different scenarios is not quantifiable the supply uncertainty is classified as "deep" (Walker et al 2013a). …”

Section: Supply Scenariosmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Roach

Kapelan

Ledbetter

et al. 2016

J. Water Resour. Plann. Manage.

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This paper evaluates two established decision making methods and analyses their performance and suitability within a Water Resources Management (WRM) problem. The methods under assessment are Info-Gap decision theory (IG) and Robust Optimisation (RO). The methods have been selected primarily to investigate a contrasting local vs global method of assessing water system robustness to deep uncertainty but also to compare a robustness model approach (IG) with a robustness algorithm approach (RO), whereby the former selects and analyses a set of prespecified strategies and the latter uses optimisation algorithms to automatically generate and evaluate solutions. The study presents a novel area-based method for IG robustness modelling and assesses the applicability of utilising the Future Flows climate change projections in scenario generation for water resource adaptation planning. The methods were applied to a case study 2 resembling the Sussex North Water Resource Zone in England, assessing their applicability at improving a risk-based WRM problem and highlighting the strengths and weaknesses of each method at selecting suitable adaptation strategies under climate change and future demand uncertainties. Pareto sets of robustness to cost are produced for both methods and highlight RO as producing the lower costing strategies for the full range of varying target robustness levels. IG produced the more expensive Pareto strategies due to its more selective and stringent robustness analysis, resulting from the more complex scenario ordering process.

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Section: The Current Approach Within the Uk As Stated In The Environmentioning

confidence: 99%

Section: The Current Approach Within the Uk As Stated In The Environmentioning

confidence: 99%

Section: Supply Scenariosmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Roach

Kapelan

Ledbetter

et al. 2016

J. Water Resour. Plann. Manage.

View full text Add to dashboard Cite

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“…And yet, despite some notable examples [6][7][8][9][10][11][12][13][14][15][16][17][18][19] the future, and how best to make sense of and orient to the future, remains an underserved issue in the policy literature. In accounts advocating and explaining the potential and actual use of strategic foresight in policy making, attention is drawn to an increasingly complex, changing and uncertain environment and the appetite this has created for knowledge as to how best to prepare and manoeuvre in a brave new world [20][21][22]. In contrast to more orthodox forms of planning, the value of strategic foresight is found in its potential to extend the horizons of what is recognised as possible and plausible futures [8,23].…”

Section: The Future and Policy Makingmentioning

confidence: 99%

An examination of independent fiscal councils and their orientation to the future and policy making

Connor

2017

Eur J Futures Res

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The aim of this paper is to review the rationale, nature and operation of independent fiscal councils (IFCs) in order to gain insights into how futures are being configured at executive levels of government and to examine how futures could be incorporated in policy making. The emphasis IFCs have placed on non-partisanship, transparency and forecasting is viewed as reflecting a particular understanding of the problem of public debt and a limited understanding of the future and policy making. Located within the context of increasing technocratic tendencies, it is argued that IFCs can be seen as part of efforts to secure the depoliticisation of policy making. Attempts to secure the de-politicisation of policy making may at best, be considered laudable, but naïve, and at worst, represents an ideological sleight of hand in attempts to colonise the future. Moving forward, it is argued that strategic foresight can make a vital contribution to an open form of policy making that deepens and extends an understanding of what the future could be -the necessary ficta of policy making.

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“…The phenomenon above is called "deep uncertainty" [38][39][40]. Under deep uncertainty, robust decision-making was proposed to provide policies that are robust across multiple scenarios, or alternative models at low cost [41].…”

Section: Robustness Of Decision Schemementioning

confidence: 99%

A Multi-Objective Chance-Constrained Programming Approach for Uncertainty-Based Optimal Nutrients Load Reduction at the Watershed Scale

Liu

Zhang

et al. 2017

Water

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Abstract:A multi-objective chance-constrained programming integrated with Genetic Algorithm and robustness evaluation methods was proposed to weigh the conflict between system investment against risk for watershed load reduction, which was firstly applied to nutrient load reduction in the Lake Qilu watershed of the Yunnan Plateau, China. Eight sets of Pareto solutions were acceptable for both system investment and probability of constraint satisfaction, which were selected from 23 sets of Pareto solutions out of 120 solution sets. Decision-makers can select optimal decisions from the solutions above in accordance with the actual conditions of different sub-watersheds under various engineering measures. The relationship between system investment and risk demonstrated that system investment increased rapidly when the probability level of constraint satisfaction was higher than 0.9, but it reduced significantly if appropriate risk was permitted. Evaluation of robustness of the optimal scheme indicated that the Pareto solution obtained from the model provided the ideal option, since the solutions were always on the Pareto frontier under various distributions and mean values of the random parameters. The application of the multi-objective chance-constrained programming to optimize the reduction of watershed nutrient loads in Lake Qilu indicated that it is also applicable to other environmental problems or study areas that contain uncertainties.

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Deep Uncertainty

Cited by 173 publications

References 28 publications

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

An examination of independent fiscal councils and their orientation to the future and policy making

A Multi-Objective Chance-Constrained Programming Approach for Uncertainty-Based Optimal Nutrients Load Reduction at the Watershed Scale

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