Risk Decision Analysis Research Articles

Risk decision analysis of commercial insurance based on neural network algorithm

Risk decision analysis of commercial insurance based on neural network algorithm

Sandia's Uncertainty Calculator: A Case Study in the Graded Approach

Software is used everywhere in a calibration lab, from controlling measurement equipment to performing statistical calculations to storing calibration records in a database. Yet the importance and details of software validation and quality assurance are not often discussed. A graded approach to software quality assurance (SQA) is typically used, where software with a higher risk of adverse consequences upon failure requires a higher level of rigor. The Sandia National Laboratories Uncertainty Calculator (Suncal) is open-source, free software for performing various statistical calculations related to metrology, including Guide to the Expression of Uncertainty in Measurement (GUM) and Monte Carlo uncertainty propagation, decision risk analysis, uncertainty in curve fitting, and analysis of variance. The SQA activities for the Suncal evolved over time. In its infancy, Suncal was simply a few code functions shared among a handful of coworkers, and SQA activities were almost nonexistent. As Suncal grew into a more formal library of algorithms with a full-featured user interface, and especially after it was released to the public, the SQA rigor level increased to match the tool’s increased risk and visibility. Suncal, and the SQA activities to ensure it operates correctly are discussed below.

Risk Decision Analysis Method for Emergency Response Based on Co-occurrence Network and Prospect Theory

From the point of co-occurrence analysis, the article uses theme search to identify a collection of emergencies, which selected from about 70 years of research literature in China Journal full-text database and Web of Science database between 1959 and 2012. Through analysing the frequency of emergencies, the article builds a co-occurrence matrix, creates an emergency network according to co-occurrence rate between emergencies, and identifies a number of hidden disaster chains within the emergency network model. Secondly, losses occur due to the intervention of decision markers, and the feeling of psychological and the weight of possible scenarios are described using the theory of prospect. According to the values, weights and alternative costs, the overall prospect value of each alternative is calculated to determine the rank of schemes. Finally, the analogy simulation is given to illustrate the feasibility and validity of the emergency network and the proposed method.

Model-based decision analysis applied to petroleum field development and management

This work describes a new methodology for integrated decision analysis in the development and management of petroleum fields considering reservoir simulation, risk analysis, history matching, uncertainty reduction, representative models, and production strategy selection under uncertainty. Based on the concept of closed-loop reservoir management, we establish 12 steps to assist engineers in model updating and production optimization under uncertainty. The methodology is applied to UNISIM-I-D, a benchmark case based on the Namorado field in the Campos Basin, Brazil. The results show that the method is suitable for use in practical applications of complex reservoirs in different field stages (development and management). First, uncertainty is characterized in detail and then scenarios are generated using an efficient sampling technique, which reduces the number of evaluations and is suitable for use with numerical reservoir simulation. We then perform multi-objective history-matching procedures, integrating static data (geostatistical realizations generated using reservoir information) and dynamic data (well production and pressure) to reduce uncertainty and thus provide a set of matched models for production forecasts. We select a small set of Representative Models (RMs) for decision risk analysis, integrating reservoir, economic and other uncertainties to base decisions on risk-return techniques. We optimize the production strategies for (1) each individual RM to obtain different specialized solutions for field development and (2) all RMs simultaneously in a probabilistic procedure to obtain a robust strategy. While the second approach ensures the best performance under uncertainty, the first provides valuable insights for the expected value of information and flexibility analyses. Finally, we integrate reservoir and production systems to ensure realistic production forecasts. This methodology uses reservoir simulations, not proxy models, to reliably predict field performance. The proposed methodology is efficient, easy-to-use and compatible with real-time operations, even in complex cases where the computational time is restrictive.

Challenges to implementing bottom-up flood risk decision analysis frameworks: how strong are social networks of flooding professionals?

Abstract. Recent developments in bottom-up vulnerability-based decision analysis frameworks present promising opportunities for flood practitioners to simplify complex decisions regarding risk mitigation and climate adaptation. This family of methodologies relies on strong social networks among flood practitioners and the public to support careful definition of stakeholder-relevant thresholds and vulnerabilities to hazards. In parallel, flood researchers are directly considering distinct atmospheric mechanisms that induce flooding to readily incorporate information on future climate projections. We perform a case study of flood professionals actively engaged in flood risk mitigation within Tompkins County, New York, USA, a community dealing with moderate flooding, to gage how much variance exists among professionals from the perspective of establishing a bottom-up flood mitigation study from an atmospheric perspective. Results of this case study indicate disagreement among flooding professionals as to which socioeconomic losses constitute a flood, disagreement on anticipated community needs, weak understanding of climate–weather–flood linkages, and some disagreement on community perceptions of climate adaptation. In aggregate, the knowledge base of the Tompkins County flood practitioners provides a well-defined picture of community vulnerability and perceptions. Our research supports the growing evidence that collaborative interdisciplinary flood mitigation work could reduce risk, and potentially better support the implementation of emerging bottom-up decision analysis frameworks for flood mitigation and climate adaptation.

Extended TODIM method for multi-attribute risk decision making problems in emergency response

Decision-making problems in emergency response are multi-attribute risk decision making (i.e., MARDM) problems. In the emergency MARDM process, the decision-maker’s (DM’s) psychological behavior should be considered. This paper provides a risk decision analysis method based on the TODIM method, in which attribute values and the probabilities that states will occur are in the format of interval numbers. Firstly, the formulation and resolve procedure of the MARDM problem in emergency response are given. Then, the gain matrix and loss matrix for each response action are constructed. Further, the dominance degree of each response action and the comprehensive dominance value of each response action are calculated. Moreover, the overall dominance probability of each response action superior to the other response actions is calculated. According to the overall dominance probabilities, a ranking of all response actions can be determined. Finally, an example of the emergency evacuation in a tourist attraction is used to illustrate the use of the proposed method.

Optimal probabilistic charging of electric vehicles in distribution systems

In this study, a probabilistic approach for the optimal charging of electric vehicles (EVs) in distribution systems is proposed. The costs of both demand and energy losses in the system are minimised, subjected to a set of constraints that consider EVs smart charging characteristics and operative aspects of the electric network. The stochastic driving patterns for EVs' owners, battery capacity and active and reactive power demanded at load nodes are considered. The optimal charging of EVs connected to the system benefits the system's operation, as it does a strategy to minimise the cost of energy losses and evaluate the capability of the system to charge EVs' batteries fully under certain penetration scenarios. Priority periods of EVs' recharge and the variation of energy price contribute to an adequate demand response, assisting the network operator for complying with quality indices (decrement of power losses) set forward by regulatory entities and developing studies of risk analysis for decision making. On the other hand, there is a valuable participation of the EVs' owners in improving the operation of the distribution system. Monte Carlo simulation (MCS) is used to assess the stochastic nature of the problem in a secondary (low voltage) distribution network.

The sentiment premium and macroeconomic announcements

Limits to arbitrage imply that market-wide investor sentiment should be a priced factor in the US equity market. While previous studies (Baker and Wurgler in J Financ 61:1645–1680, 2006) focus on the factor loading on market-wide investor sentiment, we study its factor premium in the present paper. This is important, because both factor loadings and premiums are required to estimate expected returns on stocks, which are essential for capital budgeting, portfolio evaluation, investment, and risk analysis decisions. If overpricing is more prevalent than underpricing (Stambaugh et al. in J Financ Econ 104:288–302, 2012), the premium on market-wide investor sentiment should be negative. Furthermore, the sentiment premium should be particularly significant on days without macroeconomic announcements, because there is a lack of information about the state of the economy at such times. We test these hypotheses in this paper, and find supporting evidence. Our findings have important theoretical as well as practical implications.

Information Security Management: ANP Based Approach for Risk Analysis and Decision Making

In information systems security, the objectives of risk analysis process are to help to identify new threats and vulnerabilities, to estimate their business impact and to provide a dynamic set of tools to control the security level of the information system. The identification of risk factors as well as the estimation of their business impact require tools for assessment of risk with multi-value scales according to different stakeholders' point of view. Therefore, the purpose of this paper is to model risk analysis decision making problem using semantic network to develop the decision network and the Analytical Network Process (ANP) that allows solving complex problems taking into consideration quantitative and qualitative data. As a decision support technique ANP also measures the dependency among risk factors related to the elicitation of individual judgement. An empirical study involving the Forestry Company is used to illustrate the relevance of ANP. (Less)

Risk Decision Analysis of Logistics Purchasing Based on Mathematical Expectation

The mathematical expectation value method is a commonly used method in risk decision-making. This paper studies the problem of how to arrange the purchase plan in order to get the maximum expected profit; considering the applicable principles of mathematical expectation, the decision method for getting the optimal decision scheme is given. Finally, we do simulation and stability analysis on an example and obtain the reasonable result. This result shows that mathematical expectation value method is effective in solving the problem of risk decision.

Multicriteria and Multiobjective Models for Risk, Reliability and Maintenance Decision Analysis - A Book Review

The objective of this work is the book “Multicriteria and Multiobjective Models for Risk, Reliability and Maintenance Decision Analysis”, 978-3-319-17968-1, from Springer Series “ International Series in Operations Research & Management Science ” review.

Market states and momentum in sector exchange-traded funds

We provide a clean out-of-sample test of momentum effects by focusing on a new sample period and a new set of test assets. More specifically, we examine market states and momentum in sector exchange-traded funds (ETFs) in the post-2000 period. Our results suggest that there is no momentum in sector ETFs, and that momentum does not depend on market states in the recent decade. Our findings have important theoretical as well as practical implications. In terms of theoretical implications, models attempting to explain momentum now have a higher hurdle to meet in that these models need to explain why momentum does not seem to exist in the recent decade. In terms of practical implications, our findings suggest that in capital budgeting, portfolio evaluation, investment and risk analysis decisions, caution should be exercised in using the models that take into account momentum effects.

Risk Decision Analysis on the Electricity Transaction of Power Enterprises in Electricity and Derivative Transactions

In the environment of electricity and derivative transaction, the risk hedging and metastasis of power right transaction in China are needed to establish the risk decision-making for the generated energy/right on electricity generation side. And it will be the key point focused by decision-makers in power enterprises. The planning management of two-stage market transaction risk was conducted with the hedging function of generation rights transaction to establish a risk decision model of comprehensive electricity transaction. The relationship between the electricity price of two-stage market and the identity parameters of transaction participants was given for solving the model. The whole transaction process of generating companies was divided into two stages. The risk decision framework of block transaction was established for the independent transaction decision-making of each segment and the overall optimization. The results of example simulation verified the effectiveness and feasibility of the decisions.

Risk Decision Analysis for Power Suppliers under the Carbon Emission Trading Environment

Based on the existing three mechanisms in the Carbon emission trading system, this paper proposes a hybrid carbon trading mechanism which is compatible with the characteristics of all the participants, analyzes the operation modes, benefits and risks of the power suppliers, builds a risk decision model for emission right trading according to the Financial Portfolio Theory, and gives its efficient frontier. This study provides not only theoretical supports for the power supplier’s effective management of emissions risks, but also expands the application scope of the risk management theory. Furthermore, this paper provides a reference for the government to establish an open carbon trading system.

Projects Bidding Decision Risk Analysis Based on Multi-factor Clustering Analysis

Projects Bidding Decision Risk Analysis Based on Multi-factor Clustering Analysis

Risk Decision Analysis of Commercial Real Estate

With the increasing of construction of logistics parks, it is essential for commercial real estate project to study on risk decision to avoid redundant and blind construction. Based on risk essence, the paper analyses the attitude of investment decision-makers on risk benefits. Finally, the paper improves the expected utility theory and applies the prospect theory to risk decision of commercial real estate project to provide scientific and objective basis for Project Investment Decision.

Risk decision analysis in emergency response: A method based on cumulative prospect theory

Emergency response of a disaster is generally a risk decision-making problem with multiple states. In emergency response analysis, it is necessary to consider decision-maker's (DM's) psychological behavior such as reference dependence, loss aversion and judgmental distortion, whereas DM's behavior is neglected in the existing studies on emergency response. In this paper, a risk decision analysis method based on cumulative prospect theory (CPT) is proposed to solve the risk decision-making problem in emergency response. The formulation and solution procedure of the studied emergency response problem are given. Then, according to CPT, the values of potential response results concerning each criterion are calculated. Consider the interdependence or conflict among criteria, Choquet integral is used to determine the values of each potential response result. Accordingly, the weights of probabilities of all potential response results are calculated. Furthermore, by aggregating the values and weights of response results, the prospect value of each response action (alternative) is determined, and overall prospect value of each response action is obtained by aggregating the prospect value and the cost of each action. According to the obtained overall prospect values, a ranking of all response actions can be determined. Finally, based on the background of emergency evacuation from barrier lake downstream villages, an example is given to illustrate the feasibility and validity of the proposed method.

Decision risk analysis for an interval TOPSIS method

TOPSIS is a multi-attribute decision making (MADM) technique for ranking and selection of a number of externally determined alternatives through distance measures. When the collected data for each criterion is interval and the risk attitude for a decision maker is unknown, we present a new TOPSIS method for normalizing the collected data and ranking the alternatives. The results show that the decision maker with different risk attitude ranks the different alternatives.

Scenario-based Stochastic Capacity Planning Model and Decision Risk Analysis

Abstract To study the capacity planning problem under uncertainty in which market demand and product price are stochastic, multi period capacity planning model based on scenario was investigated in this paper. Two models were proposed: one is a prearranged planning model in which the capacity investment plan do not change with the stochastic market demand, and the other is an adaptive planning model in which capacity investment plan could trace the evolution progress of the stochastic market demand. The computational study compared the decision results of both models, which reveals that the adaptive planning model could suggest better decision. Moreover, based on downside risk analysis, the investment risk of stochastic capacity planning has been investigated, and a prearranged capacity planning model considering the expected downside risk of the objective revenue was proposed. In the model, a constraint of expected downside risk is added to the initial stochastic model to reflect the decision-maker's risk preference. Whether to consider the risk or not will result in different decisions, which, in the computational study, were compared.

Risk Regulation, EU Law and Emerging Technologies: Smother or Smooth?

Risk analysis as a regulatory driver has now become firmly entrenched in public health and environmental protection. Risk analysis at any level essentially has to accommodate two gut feelings of the constituency: whether society should be risk-prone or risk averse, and whether government and its institutions can be trusted to make the necessary decisions with a high or a low degree of discretion. The precautionary principle (or rejection thereof) arguably is the ultimate reflection of the promotion of risk to a societal value. There is no doubt that especially amongst the representatives of the Member States (as opposed to the officials at the European Commission), public (pre)caution with respect to the long-term environmental and public health implications of gene technology influenced the reluctance to allow marketing of GM foods and feeds until a strict regulatory regime had been rolled out. Industry would argue that the delay in regulation, as well as the eventual regime was of such a nature as to stifle the technology. This contribution reviews a number of features of standard EU risk analysis decisions, so as to assess its current propensity towards smothering rather than smoothing the introduction of new technology. The current development of a regulatory framework for nanotechnology serves as a case study.