An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together?

A highly uncertain future due to changes in climate, technology and socio-economics has led to the realisation that identification of “best-guess” future conditions might no longer be appropriate. Instead, multiple plausible futures need to be considered, which requires (i) uncertainties to be described with the aid of scenarios that represent coherent future pathways based on different sets of assumptions, (ii) system performance to be represented by metrics that measure insensitivity (i.e. robustness) to changes in future conditions, and (iii) adaptive strategies to be considered alongside their more commonly used static counterparts. However, while these factors have been considered in isolation previously, there has been a lack of discussion of the way they are connected. In order to address this shortcoming, this paper presents a multidisciplinary perspective on how the above factors fit together to facilitate the devel- opment of strategies that are best suited to dealing with a deeply uncertain future

A double-blinded randomised controlled trial exploring the effect of anodal transcranial direct current stimulation and uni-lateral robot therapy for the impaired upper limb in sub-acute and chronic stroke

BACKGROUND:Neurorehabilitation technologies such as robot therapy (RT) and transcranial Direct Current Stimulation (tDCS) can promote upper limb (UL) motor recovery after stroke. OBJECTIVE:To explore the effect of anodal tDCS with uni-lateral and three-dimensional RT for the impaired UL in people with sub-acute and chronic stroke. METHODS:A pilot randomised controlled trial was conducted. Stroke participants had 18 one-hour sessions of RT (Armeo®Spring) over eight weeks during which they received 20 minutes of either real tDCS or sham tDCS during each session. The primary outcome measure was the Fugl-Meyer assessment (FMA) for UL impairments and secondary were: UL function, activities and stroke impact collected at baseline, post-intervention and three-month follow-up. RESULTS:22 participants (12 sub-acute and 10 chronic) completed the trial. No significant difference was found in FMA between the real and sham tDCS groups at post-intervention and follow-up (p = 0.123). A significant ‘time’ x ‘stage of stroke’ was found for FMA (p = 0.016). A higher percentage improvement was noted in UL function, activities and stroke impact in people with sub-acute compared to chronic stroke. CONCLUSIONS:Adding tDCS did not result in an additional effect on UL impairment in stroke. RT may be of more benefit in the sub-acute than chronic phase

Uncertainty, sensitivity and scenario analysis: how do they fit together?

Session J5. Advances and applications in decision making in the face of multiple plausible futuresDealing with uncertainty is becoming increasingly important in model-based decision support. Various methods have been developed in order to do this, including uncertainty, sensitivity and scenario analysis. Although these different methods serve their purpose, the availability of a large number of methods can make it difficult for practitioners to understand the similarities and differences between them and when the use of one is more suitable than another, resulting in confusion. In addition, researchers often identify with belonging to a group dealing with a particular approach, which can lead to a lack of crossfertilisation and understanding. In order to assist with bridging the gap between researchers working on different approaches to dealing with uncertainty and eliminate confusion for practitioners, the objective of this paper is to examine the relationship between uncertainty, sensitivity and scenario analysis in the context of model-based decision support, and to take the first steps towards establishing common ground between these methods and assess the contexts under which they are most suitable. This is achieved by conceptualising the various methods as different approaches to “sampling” the hyperspace of model inputs, although this is done from different perspectives and for different ends (Figure 1). It is therefore also necessary to think about the assumptions each method is making about the space being explored, and there are benefits to be gained in thinking about how best to sample the space for each purpose. The approaches identified in this conference paper provide a first level of coarse characterisations. Further refinements in categorisation is possible (with the differentiation between narrative and stress testing scenarios as a first example), and likely to be useful. There are connections to be made to other disciplines, such as philosophy and decision theory, regarding the assumptions each method makes.H.R. Maier, J.H.A. Guillaume, C. McPhail, S. Westra, J.H. Kwakkel, S. Razavi, H. van Delden, M.A. Thyer, S.A. Culley and A.J. Jakema

Is real options analysis fit for purpose in supporting climate adaptation planning and decision-making?

Even though real options analysis (ROA) is often thought as the best tool available for evaluating flexible strategies, there are profound problems with the assumptions underpinning ROA rendering it unsuitable for use in supporting planning and decision-making on climate adaptation. In the face of dynamic and deep uncertainty about the future, flexible strategies which can be adapted in response to how the uncertainty is resolving are attractive. Traditional cost-benefit analysis cannot account for the value created through optionality. ROA sets out to amend this. There are however several profound problems with how ROA tries to do this. It is typically not clear what is the baseline plan, without options, against which value is to be estimated. Different baselines significantly change option value. Even if option value can unequivocally be established for a given scenario, ROA relies on expected values over a set of scenarios. First, this requires assigning weights, or probabilities, to scenarios. Given the long-time horizon involved in climate adaptation, these probabilities are meaningless. Second, the expected value over a set of scenarios need not obtain in any single scenario and is thus not a meaningful summary of option value. This article is categorized under:. Climate Economics > Iterative Risk-Management Policy Portfolios.Policy Analysi

The treatment of uncertainty in airport strategic planning

The treatment of uncertainty in the long-term planning of infrastructures in general and of mainports such as airports and seaports is a key challenge for decisionmakers. Moreover, these uncertainties have increased over the last decades due to changes in owner structure, changes in rules and regulations, and the ever increasing connectedness of the world. This dissertation explores how the treatment of uncertainties in airport planning can be improved. Currently, the treatment is limited to one or a few forecasts for the future. Such an approach limits the exploration of the multiplicity of futures to those that are judged to be most likely. However, if the last decade has taught is anything, then it is that the future will be substantially different from the one we are anticipating now. The implication of this for decisionmaking is that any plan or policy optimized for one or a few forecasts is likely to perform poorly. An alternative approach that is capable of handling the multiplicity of futures and accepts the limits on predictability is needed. Such an approach should result in a plans consist of time-urgent low regret options that can be taken immediately, while establishing a framework for guiding future actions. Thus the decisionmaker is able to adapt the plan to the way in which the future unfolds. This dissertation presents such a dynamic adaptive planning approach, tailors this approach to the specifics of airport planning, and provides computational evidence for the efficacy of plans that are designed utilizing this approach.Policy AnalysisTechnology, Policy and Managemen

The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making

There is a growing interest in model-based decision support under deep uncertainty, reflected in a variety of approaches being put forward in the literature. A key idea shared among these is the use of models for exploratory rather than predictive purposes. Exploratory modeling aims at exploring the implications for decision making of the various presently irresolvable uncertainties. This is achieved by conducting series of computational experiments that cover how the various uncertainties might resolve. This paper presents an open source library supporting this. The Exploratory Modeling Workbench is implemented in Python. It is designed to (i) support the generation and execution of series of computational experiments; and (ii) support the visualization and analysis of the results from the computational experiments. The Exploratory Modeling Workbench enables users to easily perform exploratory modeling with existing models, identify the policy-relevant uncertainties, assess the efficacy of policy options, and iteratively improve candidate strategies.Policy Analysi

Strategies to combat salt water intrusion in coastal aquifers: A model-based exploratory analysis

Coastal communities dependent upon groundwater resources for drinking water and irrigation are vulnerable to salinization of the groundwater reserve. The increasing uncertainty associated with changing climatic conditions, population and economic development, and technological advances in agriculture, water treatment, and water purification, poses significant challenges for freshwater management. The research reported in this paper offers an approach for investigating and addressing the challenges to freshwater management using innovative exploratory modeling techniques. We present a generic systems model of a low lying coastal region that depends on its groundwater resources. This systems model covers population, agriculture, industry, and the groundwater reserve. The model captures the key dynamics of these subsystems and their interactions (adapted from Hoekstra, 1998). The systems model in turn is coupled to a powerful scenario generator, which is capable of producing a comprehensive range of plausible future scenarios (Lempert et al., 2003). Each scenario describes a unique future pathway of the evolution of population, the economy, agricultural and water purification technologies. We explore the behavior of the systems model across the wide range of scenarios and analyze the implications of these scenarios for freshwater management in the coastal region. In particular, the results are summarized in a decision tree that provides insights into the expected outcomes given the various uncertainties, thus supporting the development of effective policies for managing the coastal aquifer.Multi Actor SystemsTechnology, Policy and Managemen

A system dynamics mode-based exploratory analysis of salt water intrusion in coastal aquifers

Coastal communities dependent upon groundwater resources for drinking water and irrigation are vulnerable to salinization of the groundwater reserve. The increasing uncertainty associated with changing climatic conditions, population and economic development, and technological advances poses significant challenges for freshwater management. The research reported in this paper offers an approach for investigating and addressing the challenges to freshwater management using innovative exploratory modeling techniques. We present a generic system dynamics model of a low lying coastal region that depends on its groundwater resources. This systems model covers population, agriculture, industry, and the groundwater reserve. The system model in turn is coupled to a powerful scenario generator, which is capable of producing a comprehensive range of plausible future scenarios. Each scenario describes a unique future pathway of the evolution of population, the economy, agricultural and water purification technologies. We explore the behavior of the systems model across a wide range of scenarios and analyze the implications of these scenarios for freshwater management in the coastal region. In particular, the results are summarized in a decision tree that provides insights into the expected outcomes given the various uncertainties, thus supporting the development of effective policies for managing the coastal aquifer.Multi Actor SystemsTechnology, Policy and Managemen