Dynamical Models in Geology: Sensitivity Analysis and Scientific Risk

1993 ◽  
Vol 11 (3-4) ◽  
pp. 329-356 ◽  
Author(s):  
Rene O. Thomsen
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Mathematical Models ◽  
Systematic Approach ◽  
Probabilistic Approach ◽  
Sensitivity Analyses ◽  
System Response ◽  
Dynamical Models ◽  
Fundamental Limitations ◽  
Probabilistic Evaluation

Dynamical models are used routinely throughout various branches of geology and decisions are often based on the results of such models. Awareness of some fundamental limitations of any model used is therefore vital in order to avoid decision making based on highly uncertain results. Computer models are based on mathematical models which describe essential features of processes or system behaviour and a systematic approach to investigate and understand model limitations can therefore be applied. Sensitivity analysis provides a feel for the system response to uncertainties in both assumptions and observations. However, sensitivity analyses do not provide a feel for the level of confidence one should assign modelling results. A probabilistic approach to evaluation of uncertainties and modelling results is therefore demonstrated. Two cases are used as examples for the method and it is demonstrated how the combined sensitivity analyses and probabilistic evaluation greatly improve the use of even uncertain modelling results.

Prioritizing Invasive Plant Management with Multi-Criteria Decision Analysis

2013 ◽  
Vol 6 (3) ◽  
pp. 339-351 ◽  
Author(s):  
Matthew G. Hohmann ◽  
Michael G. Just ◽  
Peter J. Frank ◽  
Wade A. Wall ◽  
Janet B. Gray
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Invasive Plants ◽  
Invasive Plant ◽  
Sensitivity Analyses ◽  
Plant Management ◽  
Decision Making Process ◽  
Invasive Plant Management ◽  
Spatial Sensitivity ◽  
Management Area

AbstractPrioritizing management of invasive plants is important for large land management entities, such as federal and state public land stewards, because management resources are limited and multiple land uses and management objectives are differentially impacted. Management decisions also have important consequences for the likelihood of success and ultimate cost of control efforts. We applied multi-criteria decision analysis methods in a geographic information system using natural resource and land use data from Fort Bragg, North Carolina. Landscape-scale prioritization was based on a hierarchical model designed to increase invasive plant management efficiencies and reduce the risk of impacts to key installation management goals, such as training-land management and protected species conservation. We also applied spatial sensitivity analyses to evaluate the robustness of the prioritization to perturbations of the model weights, which were used to describe the relative importance of different elements of the hierarchical model. Based on stakeholders' need for confidence in making management investments, we incorporated the results of the sensitivity analysis into the decision-making process. We identified high-priority sites that were minimally affected by the weight perturbations as being suitable for up-front management and evaluated how adopting this strategy affected management area, locations, and costs. We found that incorporating the results of the sensitivity analysis led to a reduced management area, different target locations, and lower costs for an equal area managed. Finally, we confirmed the distinctiveness of the approach by comparing this same subset of prioritized sites with locations representing species-centric strategies for three invasive plants and their aggregate distribution. By supplying pragmatic information about the localized effects of weighting uncertainty, spatial sensitivity analyses enhanced the invasive plant management decision-making process and increased stakeholder confidence.

scholarly journals Inverse sensitivity analysis of mathematical models avoiding the curse of dimensionality

2018 ◽  
Author(s):  
Ben Lambert ◽  
David J. Gavaghan ◽  
Simon Tavener
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Mathematical Models ◽  
Biological Systems ◽  
Sensitivity Analyses ◽  
Deterministic Models ◽  
Deterministic Systems ◽  
Input Parameters ◽  
Insight Into

1AbstractBiological systems have evolved a degree of robustness with respect to perturbations in their environment and this capability is essential for their survival. In applications ranging from therapeutics to conservation, it is important to understand not only the sensitivity of biological systems to changes in their environments, but which features of these systems are necessary to achieve a given outcome. Mathematical models are increasingly employed to understand these mechanisms. Sensitivity analyses of such mathematical models provide insight into the responsiveness of the system when experimental manipulation is difficult. One common approach is to seek the probability distribution of the outputs of the system corresponding to a known distribution of inputs. By contrast, inverse sensitivity analysis determines the probability distribution of model inputs which produces a known distribution of outputs. The computational complexity of the methods used to conduct inverse sensitivity analyses for deterministic systems has limited their application to models with relatively few parameters. Here we describe a novel Markov Chain Monte Carlo method we call “Contour Monte Carlo”, which can be used to invert systems with a large number of parameters. We demonstrate the utility of this method by inverting a range of frequently-used deterministic models of biological systems, including the logistic growth equation, the Michaelis-Menten equation, and an SIR model of disease transmission with nine input parameters. We argue that the simplicity of our approach means it is amenable to a large class of problems of practical significance and, more generally, provides a probabilistic framework for understanding the inversion of deterministic models.2Author summaryMathematical models of complex systems are constructed to provide insight into their underlying functioning. Statistical inversion can probe the often unobserved processes underlying biological systems, by proceeding from a given distribution of a model’s outputs (the aggregate “effects”) to a distribution over input parameters (the constituent “causes”). The process of inversion is well-defined for systems involving randomness and can be described by Bayesian inference. The inversion of a deterministic system, however, cannot be performed by the standard Bayesian approach. We develop a conceptual framework that describes the inversion of deterministic systems with fewer outputs than input parameters. Like Bayesian inference, our approach uses probability distributions to describe the uncertainty over inputs and outputs, and requires a prior input distribution to ensure a unique “posterior” probability distribution over inputs. We describe a computational Monte Carlo method that allows efficient sampling from the posterior distribution even as the dimension of the input parameter space grows. This is a two-step process where we first estimate a “contour volume density” associated with each output value which is then used to define a sampling algorithm that yields the requisite input distribution asymptotically. Our approach is simple, broadly applicable and could be widely adopted.

scholarly journals Sensitivity analysis of agent-based models: a new protocol

2022 ◽  
Author(s):  
Emanuele Borgonovo ◽  
Marco Pangallo ◽  
Jan Rivkin ◽  
Leonardo Rizzo ◽  
Nicolaj Siggelkow
Keyword(s):  
Sensitivity Analysis ◽  
Factorial Design ◽  
Conditional Expectation ◽  
Systematic Approach ◽  
Sensitivity Analyses ◽  
Agent Based Models ◽  
Agent Based ◽  
Garbage Can Model ◽  
Stochastic Nature ◽  
Common Goals

AbstractAgent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions.

Selection of Khadi fabrics for optimal comfort properties using multi-criteria decision-making technique

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ashis Mitra
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Sensitivity Analyses ◽  
Multi Criteria Decision Making ◽  
Phase Sensitivity ◽  
Two Phase ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Hierarchy Process ◽  
Selection Of

Purpose Khadi fabrics are known for their unique comfort properties which are attributed to their unique structural and functional properties. For getting optimal comfort from a collection of available Khadi fabrics, further exploration is needed. Ranking the Khadi fabrics from a competitive lot for optimal comfort is a challenging job, which has not been addressed so far by any researcher. The purpose of this study is to present one such selection problem using the multi-criteria decision-making (MCDM) technique, a popular branch of operations research, which can handle almost any decision problem involving a finite number of alternatives and multiple decision criteria. Design/methodology/approach Two widely popular methods/exponents of MCDM, namely, analytic hierarchy process (AHP) and multiplicative analytic hierarchy process (MAHP) have been deployed in this study for ranking a competitive lot of 15 Khadi fabrics and selecting the best alternative for optimal summer comfort based on three comfort attributes, namely, drape coefficient, thermal insulation value and air permeability. Findings Both the approaches yield a similar ranking pattern with Spearman’s rank correlation coefficient of 0.9857, Khadi fabric K1 achieving Rank 1 (best in terms of optimal comfort) and sample K6 acquiring Rank 15 (worst choice). Two-phase sensitivity analyses were performed subsequently to demonstrate the stability of the two approaches: sensitivity analysis by changing weightage levels of the criteria and sensitivity analysis in dynamic decision conditions by changing the elements of the initial decision matrix. During sensitivity analyses, no occurrence of rank reversal is observed for the best and worst alternatives in either of the two approaches. This corroborates the robustness of the two models. Practical implications Khadi fabrics are widely acclaimed for their intrinsic comfort properties for both summer and winter. Although the popularity of Khadi fabrics is increasing day by day, this domain is under-researched, and hence, needs to be explored further. The present approach demonstrates how the MCDM technique can serve as a useful tool for ranking the available Khadi fabrics in terms of optimal comfort in summer. The same approach can be extended to other domains of the textile industry, in general, as well. Originality/value This study is the first-ever theoretical approach/research on the selection of Khadi fabrics for optimal summer comfort using the MCDM tool. Another novelty of the present study is that the efficacy of AHP and MAHP approaches, in this study, has been validated through a two-phase sensitivity analysis. This validation part has been ignored in most of the hitherto published applications of AHP and MAHP in other domains.

scholarly journals IMMU-04. IMMUNE-RELATED ADVERSE EVENTS STRONGLY PREDICT SUPERIOR OUTCOMES IN BRAIN METASTASES PATIENTS RECEIVING LOCAL TREATMENT AND IMMUNE CHECKPOINT INHIBITORS

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii105-ii105
Author(s):  
Alexander Hulsbergen ◽  
Asad Lak ◽  
Yu Tung Lo ◽  
Nayan Lamba ◽  
Steven Nagtegaal ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Adverse Events ◽  
Brain Metastases ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Local Treatment ◽  
Sensitivity Analyses ◽  
Immortal Time Bias ◽  
The Brain

Abstract INTRODUCTION In several cancers treated with immune checkpoint inhibitors (ICIs), a remarkable association between the occurrence of immune-related adverse events (irAEs) and superior oncological outcomes has been reported. This effect has hitherto not been reported in the brain. This study aimed to investigate the relation between irAEs and outcomes in brain metastases (BM) patients treated with both local treatment to the brain (LT; i.e. surgery and/or radiation) and ICIs. METHODS This study is a retrospective cohort analysis of patients treated for non-small cell lung cancer (NSCLC) BMs in a tertiary institution in Boston, MA. Outcomes of interest were overall survival (OS) and intracranial progression-free survival (IC-PFS), measured from the time of LT. Sensitivity analyses were performed to account for immortal time bias (i.e., patients who live longer receive more cycles of ICIs and thus have more opportunity to develop an irAE). RESULTS A total of 184 patients were included; 62 (33.7%) were treated with neurosurgical resection and 122 (66.3%) with upfront brain radiation. irAEs occurred in 62 patients (33.7%). After adjusting for lung-Graded Prognostic Assessment, type of LT, type of ICI, newly diagnosed vs. recurrent BM, BM size and number, targetable mutations, and smoking status, irAEs were strongly associated with better OS (HR 0.33, 95% CI 0.19 – 0.58, p < 0.0001) and IC-PFS (HR 0.41; 95% CI 0.26 – 0.65; p = 0.0001). Landmark analysis including only patients who received more than 3 cycles of ICI (n = 133) demonstrated similar results for OS and IC-PFS, as did sensitivity analysis adjusting for the number of cycles administered (HR range 0.36 – 0.51, all p-values < 0.02). CONCLUSIONS After adjusting for known prognostic factors, irAEs strongly predict superior outcomes after LT in NSCLC BM patients. Sensitivity analysis suggests that this is unlikely due to immortal time bias.

An algebraic approach to N-soft sets with application in decision-making using TOPSIS

2021 ◽  
pp. 1-21
Author(s):  
Muhammad Shabir ◽  
Rimsha Mushtaq ◽  
Munazza Naz
Keyword(s):  
Decision Making ◽  
Mathematical Models ◽  
Real World ◽  
Algebraic Approach ◽  
Multi Criteria Decision Making ◽  
Commutative Monoids ◽  
Soft Sets ◽  
Algebraic Properties ◽  
Different Types ◽  
Selection Of

In this paper, we focus on two main objectives. Firstly, we define some binary and unary operations on N-soft sets and study their algebraic properties. In unary operations, three different types of complements are studied. We prove De Morgan’s laws concerning top complements and for bottom complements for N-soft sets where N is fixed and provide a counterexample to show that De Morgan’s laws do not hold if we take different N. Then, we study different collections of N-soft sets which become idempotent commutative monoids and consequently show, that, these monoids give rise to hemirings of N-soft sets. Some of these hemirings are turned out as lattices. Finally, we show that the collection of all N-soft sets with full parameter set E and collection of all N-soft sets with parameter subset A are Stone Algebras. The second objective is to integrate the well-known technique of TOPSIS and N-soft set-based mathematical models from the real world. We discuss a hybrid model of multi-criteria decision-making combining the TOPSIS and N-soft sets and present an algorithm with implementation on the selection of the best model of laptop.

scholarly journals Multiple-Criteria Decision-Making and Sensitivity Analysis for Selection of Materials for Knee Implant Femoral Component

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2084
Author(s):  
Raman Kumar ◽  
Rohit Dubey ◽  
Sehijpal Singh ◽  
Sunpreet Singh ◽  
Chander Prakash ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Femoral Component ◽  
Human Life ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Biomedical Science ◽  
Human Beings ◽  
Weighted Sum Method ◽  
Selection Of

Total knee replacement (TKR) is a remarkable achievement in biomedical science that enhances human life. However, human beings still suffer from knee-joint-related problems such as aseptic loosening caused by excessive wear between articular surfaces, stress-shielding of the bone by prosthesis, and soft tissue development in the interface of bone and implant due to inappropriate selection of TKR material. The choice of most suitable materials for the femoral component of TKR is a critical decision; therefore, in this research paper, a hybrid multiple-criteria decision-making (MCDM) tactic is applied using the degree of membership (DoM) technique with a varied system, using the weighted sum method (WSM), the weighted product method (WPM), the weighted aggregated sum product assessment method (WASPAS), an evaluation based on distance from average solution (EDAS), and a technique for order of preference by similarity to ideal solution (TOPSIS). The weights of importance are assigned to different criteria by the equal weights method (EWM). Furthermore, sensitivity analysis is conducted to check the solidity of the projected tactic. The weights of importance are varied using the entropy weights technique (EWT) and the standard deviation method (SDM). The projected hybrid MCDM methodology is simple, reliable and valuable for a conflicting decision-making environment.

scholarly journals Complex T-spherical fuzzy Dombi aggregation operators and their applications in multiple-criteria decision-making

2021 ◽  
Author(s):  
Faruk Karaaslan ◽  
Mohammed Allaw Dawood Dawood
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Fuzzy Set ◽  
Multiple Criteria Decision Making ◽  
Aggregation Operators ◽  
Multi Criteria Decision Making ◽  
Two Dimensional ◽  
Weighted Arithmetic ◽  
Mcdm Method ◽  
Dombi Operations

AbstractComplex fuzzy (CF) sets (CFSs) have a significant role in modelling the problems involving two-dimensional information. Recently, the extensions of CFSs have gained the attention of researchers studying decision-making methods. The complex T-spherical fuzzy set (CTSFS) is an extension of the CFSs introduced in the last times. In this paper, we introduce the Dombi operations on CTSFSs. Based on Dombi operators, we define some aggregation operators, including complex T-spherical Dombi fuzzy weighted arithmetic averaging (CTSDFWAA) operator, complex T-spherical Dombi fuzzy weighted geometric averaging (CTSDFWGA) operator, complex T-spherical Dombi fuzzy ordered weighted arithmetic averaging (CTSDFOWAA) operator, complex T-spherical Dombi fuzzy ordered weighted geometric averaging (CTSDFOWGA) operator, and we obtain some of their properties. In addition, we develop a multi-criteria decision-making (MCDM) method under the CTSF environment and present an algorithm for the proposed method. To show the process of the proposed method, we present an example related to diagnosing the COVID-19. Besides this, we present a sensitivity analysis to reveal the advantages and restrictions of our method.

scholarly journals A signed network perspective on the government formation process in parliamentary democracies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Angela Fontan ◽  
Claudio Altafini
Keyword(s):  
Decision Making ◽  
Formation Process ◽  
Signed Graph ◽  
Balance Theory ◽  
Collective Decision Making ◽  
Dynamical Models ◽  
Average Correlation ◽  
Parliamentary Democracies ◽  
Signed Network ◽  
The Government

AbstractIn parliamentary democracies, government negotiations talks following a general election can sometimes be a long and laborious process. In order to explain this phenomenon, in this paper we use structural balance theory to represent a multiparty parliament as a signed network, with edge signs representing alliances and rivalries among parties. We show that the notion of frustration, which quantifies the amount of “disorder” encoded in the signed graph, correlates very well with the duration of the government negotiation talks. For the 29 European countries considered in this study, the average correlation between frustration and government negotiation talks ranges between 0.42 and 0.69, depending on what information is included in the edges of the signed network. Dynamical models of collective decision-making over signed networks with varying frustration are proposed to explain this correlation.

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