ISSN:1080-7039    

DOI:10.1080/10807039991289365

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

ISSN:1080-7039    

DOI:10.1080/10807039991289374

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

The assessment of human and ecological risks and associated risk-management decisions are characterized by only partial knowledge of the relevant systems. Typically, large variability and measurement errors in data create challenges for estimating risks and identifying appropriate management strategies. The formal quantitative method of decision analysis can help deal with these challenges because it takes uncertainties into account explicitly and quantitatively. In recent years, research in several areas of natural resource management has demonstrated that decision analysis can identify policies that are appropriate in the presence of uncertainties. More importantly, the resulting optimal decision is often different from the one that would have been chosen had the uncertainties not been taken into account quantitatively. However, challenges still exist to effective implementation of decision analysis.

ISSN:1080-7039    

DOI:10.1080/10807039991289383

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

Risk assessment and uncertainty analysis are important tools for improving environmental decision making. However, their value is limited when the environmental endpoints assessed by scientists do not coincide with the publicly-meaningful attributes that are of concern to decision makers. Approaches for addressing this disconnect are presented using examples from water quality assessment and management. Recommendations to scientists for maximizing the usefulness of uncertainty analysis are given.

ISSN:1080-7039    

DOI:10.1080/10807039991289392

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

Advances in computer technology and applied statistics have provided the opportunity for the non-statistician to investigate uncertainty in a quantitative manner. The following discussion argues, notwithstanding the possible misuse of uncertainty analysis, that uncertainty is always present and that decisions based on human or ecological risk assessment would benefit from disclosure of uncertainty in the estimated risks.

ISSN:1080-7039    

DOI:10.1080/10807039991289400

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

Why are formal statistical methods for risk-based decision-making so seldom used in the practice of watershed management? I contend that complex formal methods, while internally consistent, are often inappropriate to real world decision-making. The primary purpose of risk analysis is to support risk management, and decision methods need to be effective not just in evaluating risk, but also in communicating risk among stakeholders and decision makers. Useful methods must be not only correct, but also readily communicable. Many formal risk-based decision methods have real obstacles to practical application in one of the following areas: (1) many important components of risk that matter to stakeholders are difficult to express in quantitative terms, and any method which turns “fuzzy” information and subjective opinion into hard numbers is prone to be regarded with suspicion; (2) methods which are not understandable and convincing to decision makers have little practical value; (3) a complex formal analysis will be seen as misguided or irrelevant if it does not represent the full spectrum of management goals. This paper compares the process of watershed management with the process of ecological risk assessment, highlighting similarities and key differences. A practical decision method which balances quantitative rigor with ability to communicate to and forge consensus among stakeholders is then outlined with reference to a successful case study.

ISSN:1080-7039    

DOI:10.1080/10807039991289419

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

This debate series paper argues that the scientific and socio-economic dimensions of environmental problems are inherently inseparable. The author proposes that understanding this inseparability is the foundation of successful environmental problem-solving, and a prerequisite to the effective use of formal decision-making tools.

ISSN:1080-7039    

DOI:10.1080/10807039991289428

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

An important application of decision analysis is determining the value that information has to a decision maker. The expected value of information (EVOI) is the expected increase in the value (or decrease in the loss) associated with obtaining more information about quantities relevant to the decision process. The EVOI can be thought of as a measure of the importance of the uncertainty about a quantity in terms of the expected improvement in the decision that might be obtained from having additional information about it. Examples of EVOI quantities useful in risk management situations include the expected value of including uncertainty (EVIU), the expected value of perfect information (EVPI), and the expected value of sample information (EVSI). Value of information (VOI) analysis is useful because it makes the losses associated with decision errors explicit, balances competing probabilities and costs, helps identify the decision alternative that minimizes the expected loss, prioritizes spending on research, quantifies the value of the research to the decision maker, and provides an upper bound on what should be spent on getting information.

ISSN:1080-7039    

DOI:10.1080/10807039991289437

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

As risk analysts learn and use more advanced statistical methods for characterizing uncertainty in their assessments, care must be taken to avoid systematic errors in model specification and subsequent inference. We argue that misspecifcation of the likelihood function in Bayesian analysis, due to underestimated errors, failure to account for correlations in model-data errors, and failure to consider omitted confounding variables, is a particularly pervasive and difficult problem with potentially serious consequences. An illustrative example with an idealized exposure-risk model is used to demonstrate how such errors can lead to false precision - posterior estimates that appear precise but are in fact inaccurate. Initial guidance is suggested for considering the sensitivity of model results to these types of errors.

ISSN:1080-7039    

DOI:10.1080/10807039991289446

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor

摘要:

Analytica is an easy-to-learn, easy-to-use modeling tool that allows modelers to represent what they know through influence diagrams. These diagrams show which model quantities are derived from which others and indicate by shape and color the roles that different nodes play in the model, e.g., decision variables, chance variables, outcome variables, deterministic functions, or abstractions of sub-models. A wide variety of built-in probability distributions allow uncertainties about input values to be painlessly specified and propagated through the model via a fast, professional Monte-Carlo simulation engine. Resulting uncertainties and sensitivities about any quantity in the model can be viewed with admirable ease and flexibility by selecting among probability density, cumulative distribution, confidence band, sensitivity analysis, and other displays. Analytica features clever hierarchical model management and navigation features that serious model-builders will appreciate and that novice modelers will learn from as they are led to develop well-structured, well-documented models. Simple continuous (compartmental-flow) and Markov chain dynamic simulation models can be built by paying some detailed attention to arrays and indices, although Analytica does not support true discrete-event simulation. Within its chosen domain—uncertainty propagation through influence diagram models—Analytica is by far the easiest and best tool that we have seen.

ISSN:1080-7039    

DOI:10.1080/10807039991289455

出版商:TAYLOR & FRANCIS    

年代:1999

数据来源: Taylor