Structuring and evaluating decision support processes to enhance the robustness of complex human–natural systems

https://doi.org/10.1016/j.envsoft.2019.104551Get rights and content

Highlights

  • Achieving sustainability under global change requires robust decisions.

  • We review a broad array of methodological constructs in robust decision making.

  • We analyse human factors that can lead to biased choices and misleading inferences.

  • We analyse the combined effects of methods and human factors on robust inferences.

Abstract

Decision-making in the context of complex human–natural systems requires a transition towards robust model-based inferences which are effective despite uncertainties of human and climate driven change. Supporting robust decision-making needs a sequence of interactive methodological choices for setting the problem context, framing the decision problem, evaluating possible solutions, and making recommendations. These methodological choices are influenced by a variety of human factors, originating from cognitive, behavioural, and mental frameworks of stakeholders. We review a broad array of methodological constructs to better emphasise the choices that are most appropriate given different levels of knowledge. Consideration of these methodological constructs clarifies how problems can be perceived and framed in rival decision support paths emerging from the cumulative effects of individual methodological choices and the challenging human factors that shape decision-making under deep uncertainty. We conclude that the careful consideration of rival decision support paths can enhance the confidence in decision recommendations and illuminate sensitivities to the methodological choices.

Introduction

The debate on global environmental and societal change has shifted from problems to solutions in recent years (Schellnhuber et al., 2011). A globally prominent example is the UN 2030 Agenda for Sustainable Development with its urgent call to action for achieving 17 Sustainable Development Goals (SDGs) (UN, 2015), aiming at multiple environmental and socio-economic aspects, such as decarbonising energy systems, conserving biodiversity, reducing poverty, and improving gender equality. To achieve such diverse goals, decision-makers need to make robust decisions insensitive to well-characterised as well as deep uncertainties (Ben-Haim, 2006; Lempert et al., 2003) that are characteristics of complex and coupled human–natural systems. Deep uncertainty emerges from the limited and contested knowledge among stakeholders about interacting human–natural systems and their boundaries, the state (e.g., the likelihood) of key drivers of these systems (e.g., population growth, water availability, energy demand), and the relative importance of the outcomes (e.g., trade-offs between goals for economic development and climate actions) (Khatami et al., 2019; Kwakkel et al., 2016a; Singh et al., 2015). The shift towards robust decisions requires careful accounting of candidate costs or regrets associated with highly precautionary or risk-averse decision alternatives (Dessai and Hulme, 2007; Lincke and Hinkel, 2018; Small and Xian, 2018; Weaver et al., 2013). We aim to develop a unified framework that can inform robust inferences in complex human–natural systems by classifying, sequencing, and evaluating alternative framings of decision support. We specifically focus on model-based decision support under different degrees of uncertainty (from well-characterised to deep uncertainty) where a model (i.e., conceptual or quantitative) of the system of interest is used for the evaluation of assumptions and hypotheses (Walker, 2000).

Decision support in this context requires a sequence of interactive methodological choices about how to set the problem context (e.g., deep or well-defined uncertainty?), how to frame the problem (e.g., limited scenario narratives or many quantitative scenarios?), how to evaluate candidate decisions (e.g., model simulation or qualitative evaluation?), and how to drive robust inferences and provide decision recommendations (Tsoukiàs, 2008). Alternative combinations of the variety of choices made by analysts can lead to fundamentally different outcomes for the systems of focus (see Kwakkel, 2017). Decision support is also a subjective and social process, involving various stakeholders who directly or indirectly influence the methodological choices underlying any analysis (Voinov et al., 2014). Within this subjective process, methodological choices can be influenced deliberately (e.g., through self-interest) or unintentionally (e.g., through cognitive bias or ignorance) (Argyris and Schön, 1978; Lahtinen and Hämäläinen, 2016; Mayer et al., 2017). For example, in new projects, analysts often tend to favour methods and approaches with which they are familiar and have used previously, potentially in unrelated contexts (Lahtinen et al., 2017). These traits can influence the evaluation of robust decisions by making one particular methodological choice (e.g., deterministic simulation) to be selected over other—possibly more effective—ones (e.g., a robustness analysis which considers deep uncertainty). The risks of biased methodological choices are in sub-optimal, or even misleading, decision implications for the project (Kasprzyk et al., 2009; Lempert and Collins, 2007; Oddo et al., 2017; Singh et al., 2015).

Several studies have reviewed and classified common methodological choices in robustness-focused decision support frameworks (Dittrich et al., 2016; Herman et al., 2015; Kwakkel and Haasnoot, 2019; Maier et al., 2016). However, they are limited in addressing how researchers and practitioners should (a) make the choices that are most appropriate given different levels of knowledge, (b) become critically aware of the human factors that influence their methodological choices, (c) evaluate the combined consequences of these choices and factors on planning outcomes, and (d) take measures to tackle their negative consequences. We clarify sequences and interdependencies of methodological choices under the influence of critical human factors (e.g., biases, beliefs, heuristics, values) from psychology and cognitive sciences. We articulate how alternative candidate options for undertaking decision support processes (termed decision support paths) emerge from the cumulative effects of choices made at different stages of the process (termed decision forks). This enables us to analyse how alternative decision support paths may lead to a diversity of final inferences that are sensitive to the path taken (i.e., path dependency). We also go beyond existing taxonomies in the literature, which mostly focus on choices of analytical components (e.g., how to generate decisions and scenarios), and review new methodological steps for framing and evaluating the decision support processes themselves.

The articulation of methodological choices at decision forks, the resulting decision support paths, their path dependency, and their influential human factors contributes to policy discussions around assessing global environmental and societal sustainability and change in three ways:

  • First, it can pave the way for the exploration and the deliberative improvement of robust decision-making by illuminating the presence of alternative—sometimes less well-understood—rival paths to be considered (Quinn et al., 2017; Saltelli and Giampietro, 2017; Wirtz and Nowak, 2017). Recognising and identifying rival paths leads to awareness about the variety of potential alternative ways of informing decision-making depending on the context without being locked into one dominant—but not necessarily relevant—implementation of decision support.

  • Second, the transparent articulation of available methodological choices and potential paths helps to demystify the decision support process and to facilitate a better understanding of the overall effects of methodological choices on final inferences. Carefully documenting decision support paths is critical for ensuring that decision recommendations are supported by credible evidence, and therefore are defensible (Cash et al., 2003; Cooke, 1991).

  • Third, discussion of the impact of influential human factors helps researchers and practitioners to be critically aware of the effects of mental and behavioural aspects on the decision support process and their range of consequences for conclusions. It also signifies the importance of recognition, evaluation, and management of influential human factors in research projects to improve the effectiveness of outcomes.

Section 2 presents the four conceptual bases of the current work. Section 3 explains methodological choices and human factors and their potential influence throughout the decision support process. Section 4 discusses frontier challenges, opportunities, and recommendations for future decision support innovations.

Section snippets

Conceptual basis

This section briefly introduces the four conceptual pillars for this article. We use concepts drawn from: a) constructive decision aiding to set out a generic description of the decision support process and different forks at which choices need to be made; b) a taxonomy of robustness frameworks to articulate methodological choices at decision forks; c) the path perspective to demonstrate the way in which particular methodological choices can lead to specific implementations of decision support;

Rival decision support paths

Rival decision support paths can emerge from the succession of methodological choices at different decision forks, their path dependency, and the effects of influential human factors (e.g., biases, values). Fig. 3 shows an overview of these decision forks with their potential methodological choices. The interdependencies between decision forks create cumulative impacts on the methodological design and on the final planning outcomes (path dependency). For example, limited stakeholder knowledge

Challenges, opportunities, and future research

Supporting scientific robust decision-making in complex human–natural systems undergoing global change becomes exceedingly difficult because of tensions between competing and sometimes conflicting frames of problems (e.g., competing objectives, high-dimensional and stochastic nature of systems) that are formulated under different stakeholder assumptions (Bosomworth et al., 2017). Stakeholder participation and engagement have been recognised as a determinant for achieving translational real

Declaration of competing interest

The authors whose names are listed for the submitted manuscript entitled “Structuring and evaluating decision support processes to enhance the robustness of complex human–natural systems” have NO conflict of interests in the current work.

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