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Alain Dassargues - One of the best experts on this subject based on the ideXlab platform.
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assessment of conceptual Model Uncertainty for the regional aquifer pampa del tamarugal north chile
Hydrology and Earth System Sciences, 2010Co-Authors: Rodrigo Rojas, Alain Dassargues, Okke Batelaan, Luk FeyenAbstract:Abstract. In this work we assess the Uncertainty in Modelling the groundwater flow for the Pampa del Tamarugal Aquifer (PTA) – North Chile using a novel and fully integrated multi-Model approach aimed at explicitly accounting for uncertainties arising from the definition of alternative conceptual Models. The approach integrates the Generalized Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods. For each member of an ensemble M of potential conceptualizations, Model weights used in BMA for multi-Model aggregation are obtained from GLUE-based likelihood values. These Model weights are based on Model performance, thus, reflecting how well a conceptualization reproduces an observed dataset D. GLUE-based cumulative predictive distributions for each member of M are then aggregated obtaining predictive distributions accounting for conceptual Model uncertainties. For the PTA we propose an ensemble of eight alternative conceptualizations covering all major features of groundwater flow Models independently developed in past studies and including two recharge mechanisms which have been source of debate for several years. Results showed that accounting for heterogeneities in the hydraulic conductivity field (a) reduced the Uncertainty in the estimations of parameters and state variables, and (b) increased the corresponding Model weights used for multi-Model aggregation. This was more noticeable when the hydraulic conductivity field was conditioned on available hydraulic conductivity measurements. Contribution of conceptual Model Uncertainty to the predictive Uncertainty varied between 6% and 64% for ground water head estimations and between 16% and 79% for ground water flow estimations. These results clearly illustrate the relevance of conceptual Model Uncertainty.
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sensitivity analysis of prior Model probabilities and the value of prior knowledge in the assessment of conceptual Model Uncertainty in groundwater Modelling
Hydrological Processes, 2009Co-Authors: Rodrigo Rojas, Alain Dassargues, Luc FeyenAbstract:A key point in the application of multi-Model Bayesian averaging techniques to assess the predictive Uncertainty in groundwater Modelling applications is the definition of prior Model probabilities, which reflect the prior perception about the plausibility of alternative Models. In this work the influence of prior knowledge and prior Model probabilities on posterior Model probabilities, multi-Model predictions, and conceptual Model Uncertainty estimations is analysed. The sensitivity to prior Model probabilities is assessed using an extensive numerical analysis in which the prior probability space of a set of plausible conceptualizations is discretized to obtain a large ensemble of possible combinations of prior Model probabilities. Additionally, the value of prior knowledge about alternative Models in reducing conceptual Model Uncertainty is assessed by considering three example knowledge states, expressed as quantitative relations among the alternative Models. A constrained maximum entropy approach is used to find the set of prior Model probabilities that correspond to the different prior knowledge states. For illustrative purposes, a three-dimensional hypothetical setup approximated by seven alternative conceptual Models is employed. Results show that posterior Model probabilities, leading moments of the predictive distributions and estimations of conceptual Model Uncertainty are very sensitive to prior Model probabilities, indicating the relevance of selecting proper prior probabilities. Additionally, including proper prior knowledge improves the predictive performance of the multi-Model approach, expressed by reductions of the multi-Model prediction variances by up to 60% compared with a non-informative case. However, the ratio between-Model to total variance does not substantially decrease. This suggests that the contribution of conceptual Model Uncertainty to the total variance cannot be further reduced based only on prior knowledge about the plausibility of alternative Models. These results advocate including proper prior knowledge about alternative conceptualizations in combination with extra conditioning data to further reduce conceptual Model Uncertainty in groundwater Modelling predictions. Copyright 2009 John Wiley & Sons, Ltd.
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Sensitivity Analysis of Prior Model Probabilities and the Value of Prior Knowledge in the Assessment of Conceptual Model Uncertainty in Groundwater Modelling
'Wiley', 2009Co-Authors: Rojas Mujica Rodrigo Felipe, Feyen Luc, Alain DassarguesAbstract:A key point in the application of multi-Model Bayesian averaging techniques to assess the predictive Uncertainty in groundwater Modelling applications is the definition of prior Model probabilities, which reflect the prior perception about the plausibility of alternative Models. In this work the influence of prior knowledge and prior Model probabilities on posterior Model probabilities, multi-Model predictions, and conceptual Model Uncertainty estimations is analysed. The sensitivity to prior Model probabilities is assessed using an extensive numerical analysis in which the prior probability space of a set of plausible conceptualizations is discretized to obtain a large ensemble of possible combinations of prior Model probabilities. Additionally, the value of prior knowledge about alternative Models in reducing conceptual Model Uncertainty is assessed by considering three example knowledge states, expressed as quantitative relations among the alternative Models. A constrained maximum entropy approach is used to find the set of prior Model probabilities that correspond to the different prior knowledge states. For illustrative purposes, a three-dimensional hypothetical setup approximated by seven alternative conceptual Models is employed. Results show that posterior Model probabilities, leading moments of the predictive distributions and estimations of conceptual Model Uncertainty are very sensitive to prior Model probabilities, indicating the relevance of selecting proper prior probabilities. Additionally, including proper prior knowledge improves the predictive performance of the multi-Model approach, expressed by reductions of the multi-Model prediction variances by up to 60% compared with a non-informative case. However, the ratio between-Model to total variance does not substantially decrease. This suggests that the contribution of conceptual Model Uncertainty to the total variance cannot be further reduced based only on prior knowledge about the plausibility of alternative Models. These results advocate including proper prior knowledge about alternative conceptualizations in combination with extra conditioning data to further reduce conceptual Model Uncertainty in groundwater Modelling predictions.JRC.H.7-Land management and natural hazard
Yan Wang - One of the best experts on this subject based on the ideXlab platform.
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forward backward stochastic differential games for optimal investment and dividend problem of an insurer under Model Uncertainty
Applied Mathematical Modelling, 2017Co-Authors: Yan Wang, Lei WangAbstract:Abstract We consider optimal investment and dividend problem of an insurer, where the insurer decides dividend payment policy and invests his surplus into the financial market to manage his risk exposure. The insurer’s control problem, with the presence of Model Uncertainty, is formulated as zero-sum, forward–backward games between insurer and market. In the framework of game theory, we develop the games between insurer and market to the more general forward–backward stochastic differential games, where the system is governed by forward–backward stochastic differential equations; the control processes are regular-singular controls; and the informations available to the two players are asymmetric partial informations. Then the maximum principles are established to give sufficient and necessary optimality conditions for the saddle points of the general forward–backward games. Finally, we apply the maximum principles to solve the optimal investment and dividend problem of an insurer under Model Uncertainty.
Rodrigo Rojas - One of the best experts on this subject based on the ideXlab platform.
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on the value of conditioning data to reduce conceptual Model Uncertainty in groundwater Modeling
Water Resources Research, 2010Co-Authors: Rodrigo Rojas, Luc Feye, Okke Atelaa, Alai DassarguesAbstract:[1] Recent applications of multiModel methods have demonstrated their potential in quantifying conceptual Model Uncertainty in groundwater Modeling applications. To date, however, little is known about the value of conditioning to constrain the ensemble of conceptualizations, to differentiate among retained alternative conceptualizations, and to reduce conceptual Model Uncertainty. We address these questions by conditioning multiModel simulations on measurements of hydraulic conductivity and observations of system-state variables and evaluating the effects on (1) the posterior multiModel statistics and (2) the contribution of conceptual Model Uncertainty to the predictive Uncertainty. MultiModel aggregation and conditioning is performed by combining the Generalized Likelihood Uncertainty Estimation (GLUE) method and Bayesian Model Averaging (BMA). As an illustrative example we employ a 3-dimensional hypothetical system under steady state conditions, for which Uncertainty about the conceptualization is expressed by an ensemble (M) of seven Models with varying complexity. Results show that conditioning on heads allowed for the exclusion of the two simplest Models, but that their information content is limited to further differentiate among the retained conceptualizations. Conditioning on increasing numbers of conductivity measurements allowed for a further refinement of the ensemble M and resulted in an increased precision and accuracy of the multiModel predictions. For some groundwater flow components not included as conditioning data, however, the gain in accuracy and precision was partially offset by strongly deviating predictions of a single conceptualization. Identifying the conceptualization producing the most deviating predictions may guide data collection campaigns aimed at acquiring data to further eliminate such conceptualizations. Including groundwater flow and river discharge observations further allowed for a better differentiation among alternative conceptualizations and drastic reductions of the predictive variances. Results strongly advocate the use of observations less commonly available than groundwater heads to reduce conceptual Model Uncertainty in groundwater Modeling.
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assessment of conceptual Model Uncertainty for the regional aquifer pampa del tamarugal north chile
Hydrology and Earth System Sciences, 2010Co-Authors: Rodrigo Rojas, Alain Dassargues, Okke Batelaan, Luk FeyenAbstract:Abstract. In this work we assess the Uncertainty in Modelling the groundwater flow for the Pampa del Tamarugal Aquifer (PTA) – North Chile using a novel and fully integrated multi-Model approach aimed at explicitly accounting for uncertainties arising from the definition of alternative conceptual Models. The approach integrates the Generalized Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods. For each member of an ensemble M of potential conceptualizations, Model weights used in BMA for multi-Model aggregation are obtained from GLUE-based likelihood values. These Model weights are based on Model performance, thus, reflecting how well a conceptualization reproduces an observed dataset D. GLUE-based cumulative predictive distributions for each member of M are then aggregated obtaining predictive distributions accounting for conceptual Model uncertainties. For the PTA we propose an ensemble of eight alternative conceptualizations covering all major features of groundwater flow Models independently developed in past studies and including two recharge mechanisms which have been source of debate for several years. Results showed that accounting for heterogeneities in the hydraulic conductivity field (a) reduced the Uncertainty in the estimations of parameters and state variables, and (b) increased the corresponding Model weights used for multi-Model aggregation. This was more noticeable when the hydraulic conductivity field was conditioned on available hydraulic conductivity measurements. Contribution of conceptual Model Uncertainty to the predictive Uncertainty varied between 6% and 64% for ground water head estimations and between 16% and 79% for ground water flow estimations. These results clearly illustrate the relevance of conceptual Model Uncertainty.
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sensitivity analysis of prior Model probabilities and the value of prior knowledge in the assessment of conceptual Model Uncertainty in groundwater Modelling
Hydrological Processes, 2009Co-Authors: Rodrigo Rojas, Alain Dassargues, Luc FeyenAbstract:A key point in the application of multi-Model Bayesian averaging techniques to assess the predictive Uncertainty in groundwater Modelling applications is the definition of prior Model probabilities, which reflect the prior perception about the plausibility of alternative Models. In this work the influence of prior knowledge and prior Model probabilities on posterior Model probabilities, multi-Model predictions, and conceptual Model Uncertainty estimations is analysed. The sensitivity to prior Model probabilities is assessed using an extensive numerical analysis in which the prior probability space of a set of plausible conceptualizations is discretized to obtain a large ensemble of possible combinations of prior Model probabilities. Additionally, the value of prior knowledge about alternative Models in reducing conceptual Model Uncertainty is assessed by considering three example knowledge states, expressed as quantitative relations among the alternative Models. A constrained maximum entropy approach is used to find the set of prior Model probabilities that correspond to the different prior knowledge states. For illustrative purposes, a three-dimensional hypothetical setup approximated by seven alternative conceptual Models is employed. Results show that posterior Model probabilities, leading moments of the predictive distributions and estimations of conceptual Model Uncertainty are very sensitive to prior Model probabilities, indicating the relevance of selecting proper prior probabilities. Additionally, including proper prior knowledge improves the predictive performance of the multi-Model approach, expressed by reductions of the multi-Model prediction variances by up to 60% compared with a non-informative case. However, the ratio between-Model to total variance does not substantially decrease. This suggests that the contribution of conceptual Model Uncertainty to the total variance cannot be further reduced based only on prior knowledge about the plausibility of alternative Models. These results advocate including proper prior knowledge about alternative conceptualizations in combination with extra conditioning data to further reduce conceptual Model Uncertainty in groundwater Modelling predictions. Copyright 2009 John Wiley & Sons, Ltd.
Lei Wang - One of the best experts on this subject based on the ideXlab platform.
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forward backward stochastic differential games for optimal investment and dividend problem of an insurer under Model Uncertainty
Applied Mathematical Modelling, 2017Co-Authors: Yan Wang, Lei WangAbstract:Abstract We consider optimal investment and dividend problem of an insurer, where the insurer decides dividend payment policy and invests his surplus into the financial market to manage his risk exposure. The insurer’s control problem, with the presence of Model Uncertainty, is formulated as zero-sum, forward–backward games between insurer and market. In the framework of game theory, we develop the games between insurer and market to the more general forward–backward stochastic differential games, where the system is governed by forward–backward stochastic differential equations; the control processes are regular-singular controls; and the informations available to the two players are asymmetric partial informations. Then the maximum principles are established to give sufficient and necessary optimality conditions for the saddle points of the general forward–backward games. Finally, we apply the maximum principles to solve the optimal investment and dividend problem of an insurer under Model Uncertainty.
Igor Cialenco - One of the best experts on this subject based on the ideXlab platform.
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time inconsistent markovian control problems under Model Uncertainty with application to the mean variance portfolio selection
International Journal of Theoretical and Applied Finance, 2021Co-Authors: Tomasz R Bielecki, Tao Chen, Igor CialencoAbstract:In this paper, we study a class of time-inconsistent terminal Markovian control problems in discrete time subject to Model Uncertainty. We combine the concept of the sub-game perfect strategies wit...
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time inconsistent markovian control problems under Model Uncertainty with application to the mean variance portfolio selection
arXiv: Optimization and Control, 2020Co-Authors: Tomasz R Bielecki, Tao Chen, Igor CialencoAbstract:In this paper we study a class of time-inconsistent terminal Markovian control problems in discrete time subject to Model Uncertainty. We combine the concept of the sub-game perfect strategies with the adaptive robust stochastic to tackle the theoretical aspects of the considered stochastic control problem. Consequently, as an important application of the theoretical results, by applying a machine learning algorithm we solve numerically the mean-variance portfolio selection problem under the Model Uncertainty.
