Integrating multicriteria decision analysis and scenario planning : review and extension

Scenario planning and multiple criteria decision analysis (MCDA) are two key management science tools used in strategic planning. In this paper, we explore the integration of these two approaches in a coherent manner, recognizing that each adds value to the implementation of the other. Various approaches that have been adopted for such integration are reviewed, with a primary focus on the process of constructing preferences both within and between scenarios. Biases that may be introduced by inappropriate assumptions during such processes are identified, and used to motivate a framework for integrating MCDA and scenario thinking, based on applying MCDA concepts across a range of "metacriteria" (combinations of scenarios and primary criteria). Within this framework, preferences according to each primary criterion can be expressed in the context of different scenarios. The paper concludes with a hypothetical but non-trivial example of agricultural policy planning in a developing country

Knowledge and perceptions in participatory policy processes: lessons from the delta-region in the Netherlands

Water resources management issues tend to affect a variety of uses and users. Therefore, they often exhibit complex and unstructured problems. The complex, unstructured nature of these problems originates from uncertain knowledge and from the existence of divergent perceptions among various actors. Consequently, dealing with these problems is not just a knowledge problem; it is a problem of ambiguity too. This paper focuses on a complex, unstructured water resources management issue, the sustainable development—for ecology, economy and society—of the Delta-region of the Netherlands. In several areas in this region the ecological quality decreased due to hydraulic constructions for storm water safety, the Delta Works. To improve the ecological quality, the Dutch government regards the re-establishment of estuarine dynamics in the area as the most important solution. However, re-establishment of estuarine dynamics will affect other uses and other users. Among the affected users are farmers in the surrounding areas, who use freshwater from a lake for agricultural purposes. This problem has been addressed in a participatory decision-making process, which is used as a case study in this paper. We investigate how the dynamics in actors’ perceptions and the knowledge base contribute to the development of agreed upon and valid knowledge about the problem–solution combination, using our conceptual framework for problem structuring. We found that different knowledge sources—expert and practical knowledge—should be integrated to create a context-specific knowledge base, which is scientifically valid and socially robust. Furthermore, we conclude that for the convergence of actors’ perceptions, it is essential that actors learn about the content of the process (cognitive learning) and about the network in which they are involved (strategic learning). Our findings form a plea for practitioners in water resources management to adopt a problem structuring approach in order to deal explicitly with uncertainty and ambiguity

Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters

Demands on the disaster response capacity of the European Union are likely to increase, as the impacts of disasters continue to grow both in size and frequency. This has resulted in intensive research on issues concerning spatially-explicit information and modelling and their multiple sources of uncertainty. Geospatial support is one of the forms of assistance frequently required by emergency response centres along with hazard forecast and event management assessment. Robust modelling of natural hazards requires dynamic simulations under an array of multiple inputs from different sources. Uncertainty is associated with meteorological forecast and calibration of the model parameters. Software uncertainty also derives from the data transformation models (D-TM) needed for predicting hazard behaviour and its consequences. On the other hand, social contributions have recently been recognized as valuable in raw-data collection and mapping efforts traditionally dominated by professional organizations. Here an architecture overview is proposed for adaptive and robust modelling of natural hazards, following the Semantic Array Programming paradigm to also include the distributed array of social contributors called Citizen Sensor in a semantically-enhanced strategy for D-TM modelling. The modelling architecture proposes a multicriteria approach for assessing the array of potential impacts with qualitative rapid assessment methods based on a Partial Open Loop Feedback Control (POLFC) schema and complementing more traditional and accurate a-posteriori assessment. We discuss the computational aspect of environmental risk modelling using array-based parallel paradigms on High Performance Computing (HPC) platforms, in order for the implications of urgency to be introduced into the systems (Urgent-HPC).Comment: 12 pages, 1 figure, 1 text box, presented at the 3rd Conference of Computational Interdisciplinary Sciences (CCIS 2014), Asuncion, Paragua

Integrated Analysis and Synthesis of the Dynamic Behaviour of a Carbonate Field

Imperial Users onl

Conceptualising uncertainty in environmental decision-making: The example of the EU Water Framework Directive

The question of how to deal with uncertainty in environmental decision-making is cur-rently attracting considerable attention on the part of scientists as well as of politicians and those involved in government administration. The existence of uncertainty becomes particularly apparent in the field of environmental policy because environmental prob-lems are regarded as highly complex and long-term and because far-reaching changes have to be taken into account; moreover, the knowledge available to practitioners and policy makers alike is often fragmentary and not systemised. One key issue arising from this is the challenge to develop scientific decision support methods that are capable of dealing with uncertainty in a systematic and differentiated way, integrating scientific and practical knowledge. This paper introduces a conceptual framework for perceiving and describing uncertainty in environmental decision-making. It is argued that perceiv-ing and describing uncertainty is an important prerequisite for deciding and acting under uncertainty. The conceptual framework consists of a general definition of uncertainty along with five complementary perspectives on the phenomenon, each highlighting one specific aspect of it. By using the conceptual framework, decision-makers are able to re-flect on their knowledge base with regard to its completeness and reliability and to gain a broad picture of uncertainty from various standpoints. The theoretical ideas presented here are based on two empirical studies looking at how uncertainty is dealt with in the implementation process of the EU Water Framework Directive (WFD). The rather ab-stract differentiations are illustrated by a number of examples in the form of interview statements and excerpts from the WFD and the WFD guidance documents Impress, Wateco und Proclan. --uncertainty,probability,lack of knowledge,pure ignorance,environ-mental decision-making,EU Water Framework Directive (WFD)

Robust multi-fidelity design of a micro re-entry unmanned space vehicle

This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. Uncertainties on the aerodynamic forces and characteristics of the thermal protection material are incorporated into the vehicle model, and a Monte-Carlo sampling procedure is used to compute relevant statistical characteristics of the maximum heat flux and internal temperature. Then, the hybrid algorithm searches for geometries that minimize the mean value of the maximum heat flux, the mean value of the maximum internal temperature, and the weighted sum of their variance: the evolutionary part handles the shape parameters of the vehicle and the uncertain functions, while the direct transcription method generates the optimal control profile for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are utilized to approximate the aerodynamic forces required by the optimal control solver. The artificial neural networks are trained and updated by means of a multi-fidelity approach: initially a low-fidelity analytical model, fitted on a waverider type of vehicle, is used to train the neural networks, and through the evolution a mix of analytical and computational fluid dynamic, high-fidelity computations are used to update it. The data obtained by the high-fidelity model progressively become the main source of updates for the neural networks till, near the end of the optimization process, the influence of the data obtained by the analytical model is practically nullified. On the basis of preliminary results, the adopted technique is able to predict achievable performance of the small spacecraft and the requirements in terms of thermal protection materials

Orbital structure of the GJ876 extrasolar planetary system, based on the latest Keck and HARPS radial velocity data

We use full available array of radial velocity data, including recently published HARPS and Keck observatory sets, to characterize the orbital configuration of the planetary system orbiting GJ876. First, we propose and describe in detail a fast method to fit perturbed orbital configuration, based on the integration of the sensitivity equations inferred by the equations of the original $N$-body problem. Further, we find that it is unsatisfactory to treat the available radial velocity data for GJ876 in the traditional white noise model, because the actual noise appears autocorrelated (and demonstrates non-white frequency spectrum). The time scale of this correlation is about a few days, and the contribution of the correlated noise is about 2 m/s (i.e., similar to the level of internal errors in the Keck data). We propose a variation of the maximum-likelihood algorithm to estimate the orbital configuration of the system, taking into account the red noise effects. We show, in particular, that the non-zero orbital eccentricity of the innermost planet \emph{d}, obtained in previous studies, is likely a result of misinterpreted red noise in the data. In addition to offsets in some orbital parameters, the red noise also makes the fit uncertainties systematically underestimated (while they are treated in the traditional white noise model). Also, we show that the orbital eccentricity of the outermost planet is actually ill-determined, although bounded by $\sim 0.2$. Finally, we investigate possible orbital non-coplanarity of the system, and limit the mutual inclination between the planets \emph{b} and \emph{c} orbits by $5^\circ-15^\circ$, depending on the angular position of the mutual orbital nodes.Comment: 36 pages, 11 figures, 3 tables; Accepted to Celestial Mechanics and Dynamical Astronom