Chapter Four Complexity and Uncertainty: Rethinking the Modelling Activity

Brugnach, Marcela; Pahl‐Wostl, Claudia; Lindenschmidt, Karl–Erich; Janssen, Judith; Filatova, Tatiana; Mouton, Ans; Holtz, Georg; Keur, Peter van der; Gaber, Noha

doi:10.1016/s1574-101x(08)00604-2

Cited by 29 publications

(28 citation statements)

References 21 publications

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“…With the advent of an improved understanding of complex systems and the increasing interest in complex adaptive systems, a much richer understanding of the role of models has developed (Epstein 2008;Brugnach et al 2008). I would venture to say that this applies to all scientific disciplines 1 even when perceptions of what a model is and how it should be used may differ.…”

Section: Simulation Models As Virtual Laboratoriesmentioning

confidence: 99%

Water Governance in the Face of Global Change

Pahl‐Wostl¹

2015

Water Governance - Concepts, Methods, and Practice

218

116

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This book series aims at providing a platform for developing an integrated perspective on major advances in the field of water governance. Contributions will build bridges across established fields of expertise, across disciplinary perspectives, across levels from global to local and across geographical regions. Topics to be covered include conceptual and methodological advances capturing the complexity of water governance systems, institutional settings, actor constellations, diagnostic approaches, and comparative studies of governance systems. The book series encompasses monographs, textbooks and edited, coordinated volumes addressing one theme from different perspectives. The book series will address mainly a wider scientific audience, but will also provide valuable knowledge to interested practitioners. The sustainable management of fresh water resources and the ecosystem services that they provide is one of the key challenges of the 21st century. Many water related problems can be attributed to governance failure at multiple levels of governance rather than to the resource base itself. At the same time our knowledge on water governance systems and conditions for success of water governance reform is still quite limited. The notion of water governance aims at capturing the complexity of processes that determine the delivery of water related services for societal needs and that provide the context within which water management operates. Water governance is a fast growing field of scholarly expertise which has largely developed over the past decade. The number of publications in peer reviewed journals has increased from less than 20 in the year 2000 to nearly 400 in the year 2013. The increasing popularity of the term in science and policy has not lead to conceptual convergence but rather to an increasing vagueness and competing interpretations of how the concept should be understood, studied, and analyzed; which disciplines are involved; and what methodological approaches are most suitable for the study and analysis of water governance. The time seems to be ripe for comprehensive synthesis and integration.More information about this series at

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Section: Simulation Models As Virtual Laboratoriesmentioning

confidence: 99%

Water Governance in the Face of Global Change

Pahl‐Wostl¹

2015

Water Governance - Concepts, Methods, and Practice

218

116

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“…Acknowledging the multiple purposes of modeling. The purposes for which models are built include prediction, exploration, learning and communication (Brugnach et al, 2008;Kelly et al, 2013). Quantitative prediction is arguably the purpose most commonly associated with modelling.…”

Section: The Way Forwardmentioning

confidence: 99%

“…The main requirement for constructing a faster running surrogate is that the response surface of the expensive model is smooth. Some of the most popular surrogate types include polynomial chaos expansions (Sudret, 2008), Gaussian processes (Rasmussen and Williams, 2006), and sparse grids (Bungartz and Griebel, 2004). The most efficient are goal-oriented in nature and target very specific uncertainty measures.…”

Section: The Way Forwardmentioning

confidence: 99%

Eight grand challenges in socio-environmental systems modeling

Elsawah

Filatova

Jakeman

et al. 2020

SESMO

131

120

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Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.

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“…In current industrial practice, robustness is integrated in activities such as design verification, tolerances, and design choices. This in turn primarily reduces the performance risk, development cost risk, and has a subsequent effect on the market and business risk [44].…”

Section: Findmentioning

confidence: 99%