Expertise,safety, reliability,and decision making: practical industrial experience

This paper presents our practical experience of expert opinion deduced from concrete field applications, concerning mainly safety and reliability modeling. First, expertise is defined and the approach used for eliciting expertise is shortly described. Quality and value of information from experts are important challenges for decision making from risk analysis. Expertise is very useful for supplying data when not enough field observations are available for estimating parameter uncertainties and quantifying risk analysis models, which are inputs to a decision-making process. Applications presented herein concern mainly the role of expert judgment in reliability.     

Group utility indifference and resource management decision making

Natural resource managers often rely on the advice of specialists to aid decision making. However, disagreement among these specialists about the relative value of particular management objectives or the risks associated with implementing certain management strategies may complicate the decision effort. Multiattribute utility analysis can facilitate decision making by indicating how attributes of a problem are weighed by individual specialists. This information can then be used to outline bands of potential problem solutions that are acceptable to the advising group and may allow management to further its own objectives (possibly increased efficiency).An example is presented that relates to fire management planning efforts on national forests. Multiattribute utility functions developed from a survey of fire management professionals are used to identify utility-maximizing fire management strategies based on each strategy's level of economic efficiency and risk. Bands of utility-indifferent potential solutions are outlined based on measures of group consensus. It is pointed out that a subset of these would further management's goal (increased efficiency) without significantly altering the value assigned to the risk attribute by the specialists. Finally, the robustness of the technique is discussed with particular reference to environmental management problems and the role that proxy information often plays in decision making.     

Importance of scientific uncertainty in decision making

Uncertainty in environmental decision making should not be thought of as a problem that is best ignored. In fact, as is illustrated in a simple example, we often informally make use of awareness of uncertainty by hedging decisions away from large losses. This hedging can be made explicit and formalized using the methods of decision analysis. While scientific uncertainty is undesirable, it can still be useful in environmental management as it provides a basis for the need to fund additional monitoring, experimentation, or information acquisition to improve the scientific basis for decisions.     

Farmer decision making and spatial variables in northern Thailand

This research has two interrelated objectives. The first is to determine the extent to which a relationship exists between farmer characteristics and farming practices in three villages in northern Thailand. The second is to use standard statistical methods for incorporating spatial variables into the analysis and to assess the effects of these variables on farmer decision making. The data base includes information on the location and size of villages, roads, streams, and fields; a digital elevation model with information on elevation, slope, and aspect; and information keyed to individual fields on crops and cropping methods and the ethnicity, income, and religion of farmers. The map data (517 plots) were entered into a computerized geographic information systems (GIS). Results suggest several hypotheses about the relationships between land use and owner characteristics. More significantly, the study concludes that spatial analysis appears to be most useful when the dependent variable is either continuous or ordinal. The outlook is not quite as optimistic when the dependent variable is a nonordinal categorical variable. Before spatial analysis can be applied regularly to social science data, better computational tools need to be developed.     

Use of experimental ecosystems in regulatory decision making

Tiered testing for the effects of chemicals on aquatic ecosystems has begun to include tests at the ecosystem level as a component in pesticide regristration. Because such tests are expensive, regulators and industry need to know what additional information they can gain from such tests relative to the costs of the simpler single-species toxicity bioassays. Requirements for ecosystem-level testing have developed because resource managers have not fully understood the implications of potential damage to resources without having evaluations of the predicted impacts under field conditions. We review approaches taken in the use of experimental ecosystems, discuss benefits and limitations of small- and large-scale ecosystem tests, and point to correlative approaches between laboratory and field toxicity testing.Laboratory experimental ecosystems (microcosms) have been successfully used to measure contaminant bioavailability, to determine routes of uptake in moderately complex aquatic systems, and to isolate factors modifying contaminant uptake into the biota. Such factors cannot be as readily studied in outdoor experimental ecosystems because direct cause-and-effect relations are often confounded and difficult to isolate. However, laboratory tests can be designed to quantify the relations among three variables: known concentrations of Stressors; specific sublethal behavioral, biochemical, and physiological effects displayed by organisms; and responses that have been observed in ecosystem-level analyses. For regulatory purposes, the specificity of test results determines how widely they can be applied. Ecotoxicological research should be directed at attempts to identify instances where single-species testing would be the appropriate level of analysis for identifying critical ecological endpoints and for clarifying relationships between ecosystem structure and function, and where it would be inadequate for a given level of analysis.     

Multiple criteria decision making: Facilitating a learning environment

Effective decision making for sustainability requires consideration of multiple evaluation criteria. A numerical weight, assigned to each criterion, is the most common tool used to formalize preferences in Multiple Criteria Decision Making (MCDM). However, there are methods other than applying weights, which can be used to explore and articulate preferences. Two such groups have been identified as aspirational and holistic methods. The authors are interested in establishing if the different approaches to MCDM vary in their ability to facilitate a learning environment. There has been little examination of how this might best be achieved. An attempt is made to set out some hypotheses about which characteristics of MCDM tools may best support such learning. Additionally, three MCDM tools, representing the weighting, aspirational and holistic methods, are evaluated through a workshop for their ability to support individual learning. This includes a new tool, referred to as Target Ordering, which explores preferences through criteria targets rather than applying a weight to the criteria themselves.     

An experimental investigation of resilience decision making in repeated disasters

Environment Systems and Decisions - Given the growing prevalence of catastrophic events and health epidemics, policymakers are increasingly searching for effective strategies to encourage firms to...     

Towards Participatory Geographic Information Systems for community-based environmental decision making

This article discusses the potential of Geographic Information Systems (GIS) to become an information technology enabling groups of people to participate in decisions shaping their communities and promoting sustainable use of natural resources. It explains the concept of participation in the context of planning and decision making. In this context Participatory GIS (PGIS) offers tools that can be used to help the public become meaningfully involved in decision making processes affecting their communities. Following an overview of research on PGIS and its current status the article presents two recent studies of PGIS in water resource planning; one involving the use of computer generated maps representing simple information structures and the other involving the use of more sophisticated information tools. The synthesis of both studies provides the bases for discussing the prospects of PGIS to empower citizens in making decisions about their communities and resources.     

The role of analytical science in natural resource decision making

There is a continuing debate about the proper role of analytical (positivist) science in natural resource decision making. Two diametrically opposed views are evident, arguing for and against a more extended role for scientific information. The debate takes on a different complexion if one recognizes that certain kinds of problem, referred to here as “wicked” or “trans-science” problems, may not be amenable to the analytical process. Indeed, the mistaken application of analytical methods to trans-science problems may not only be a waste of time and money but also serve to hinder policy development. Since many environmental issues are trans-science in nature, then it follows that alternatives to analytical science need to be developed. In this article, the issues involved in the debate are clarified by examining the impact of the use of analytical methods in a particular case, the spruce budworm controversy in New Brunswick. The article ends with some suggestions about a “holistic” approach to the problem.     

Space perception as a factor in land use decision making

Land use decisions involve the allocation of space to competing demands. Space is perceived subjectively, particularly in respect to recreational use. Five groups involved in the decision-making process were asked to select from a list of acreages or percentages the closest approximation for each of five land use categories. The responses produced considerable variation from the correct answers. Designated park areas tended to be overestimated and designated forest lands underestimated. While some differences were associated with group affiliation; visibility of areas, political importance, experience of individual, mode of access, proximity, and role of group member may also have been important determinants. The importance of space perception is dependent on type of process in decision making; in particular whether the process is incremental or comprehensive.     

Ecological information in the political decision making of urban land-use planning

The objective of this paper is to examine what factors determine the importance of ecological information in political decision making of urban land use in Helsinki, southern Finland. Results from the interviews of politicians and city officials show that ecological information becomes more important if there are significant conservation values connected to a plan area, contacts and appeals from plan participants, strong media attention or if a decision maker focuses more on the issue due to personal interests and values. Balancing the weight of recreational and cultural values may be one of the most difficult tasks for decision makers to handle.     

Hierarchical modeling of seed variety yields and decision making for future planting plans

Eradicating hunger and malnutrition is a key development goal of the twenty first century. This paper addresses the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision making framework. Specifically, a novel hierarchical machine learning mechanism for predicting crop yield (the yield of different seed varieties of the same crop) is introduced. This prediction mechanism is then integrated with a weather forecasting model and three different approaches for decision making under uncertainty to select seed varieties for planting so as to balance yield maximization and risk. The model was applied to the problem of soybean variety selection given in the 2016 Syngenta Crop Challenge. The prediction model achieved a median absolute error of 235 kg/ha and thus provides good estimates for input into the decision models. The decision models identified the selection of soybean varieties that appropriately balance yield and risk as a function of the farmer’s risk aversion level. More generally, the models can support farmers in decision making about which seed varieties to plant.