Using value of information to prioritize research needs for migratory bird management under climate change: a case study using federal land acquisition in the United States

In response to global habitat loss, many governmental and non‐governmental organizations have implemented land acquisition programs to protect critical habitats permanently for priority species. The ability of these protected areas to meet future management objectives may be compromised if the effects of climate change are not considered in acquisition decisions. Unfortunately, the effects of climate change on ecological systems are complex and plagued by uncertainty, making it difficult for organizations to prioritize research needs to improve decision‐making. Herein, we demonstrate the use of qualitative value of information analysis to identify and prioritize which sources of uncertainty should be reduced to improve land acquisition decisions to protect migratory birds in the face of climate change. The qualitative value of information analysis process involves four steps: (i) articulating alternative hypotheses; (ii) determining the magnitude of uncertainty regarding each hypothesis; (iii) evaluating the relevance of each hypothesis to acquisition decision‐making; and (iv) assessing the feasibility of reducing the uncertainty surrounding each hypothesis through research and monitoring. We demonstrate this approach using the objectives of 3 U.S. federal land acquisition programs that focus on migratory bird management. We used a comprehensive literature review, expert elicitation, and professional judgement to evaluate 11 hypotheses about the effect of climate change on migratory birds. Based on our results, we provide a list of priorities for future research and monitoring to reduce uncertainty and improve land acquisition decisions for the programs considered in our case study. Reducing uncertainty about how climate change will influence the spatial distribution of priority species and biotic homogenization were identified as the highest priorities for future research due to both the value of this information for improving land acquisition decisions and the feasibility of reducing uncertainty through research and monitoring. Research on how changes in precipitation patterns and winter severity will influence migratory bird abundance is also expected to benefit land acquisition decisions. By contrast, hypotheses about phenology and migration distance were identified as low priorities for research. By providing a rigorous and transparent approach to prioritizing research, we demonstrate that qualitative value of information is a valuable tool for prioritizing research and improving management decisions in other complex, high‐uncertainty cases where traditional quantitative value of information analysis is not possible. Given the inherent complexity of ecological systems under climate change, and the difficulty of identifying management‐relevant research priorities, we expect this approach to have wide applications within the field of natural resource management.     

Value of information in natural resource management: technical developments and application to pink‐footed geese

The “value of information” (VOI) is a generic term for the increase in value resulting from better information to guide management, or alternatively, the value foregone under uncertainty about the impacts of management (Yokota and Thompson, Medical Decision Making 2004; 24 : 287). The value of information can be characterized in terms of several metrics, including the expected value of perfect information and the expected value of partial information. We extend the technical framework for the value of information by further developing the relationship between value metrics for partial and perfect information and describing patterns of their performance. We use two different expressions for the expected value of partial information to highlight its relationship to the expected value of perfect information. We also develop the expected value of partial information for hierarchical uncertainties. We highlight patterns in the value of information for the Svalbard population of the pink‐footed goose (Anser brachyrhynchus), a population that is subject to uncertainty in both reproduction and survival functions. The framework for valuing information is seen as having widespread potential in resource decision making, and serves as a motivation for resource monitoring, assessment, and collaboration.     

The Joy Before Cooking: Preparing Ourselves to Write a Risk Research Recipe

Rather than the conventional practice of compiling a list of interesting research projects and then attempting to make the case that each represents a high priority, I will attempt an approach rooted in decision analysis. The information of greatest value, according to decision theory, is that which most enables us to make more reliable, transparent, and cost-effective decisions. Therefore, I begin with a brief discussion of how and why typical decisions relying on cancer dose-response information can fall short, in an attempt to assess where and how this aspect of risk assessment is “broken” before generating a list of research projects to “fix” it. I discuss the problem of model uncertainty in dose-response assessment, and conclude it is impossible to gauge how valuable it might be to know the correct model until we agree on guidelines for how to make decisions given imperfect information in this regard. After discussing four broad research areas that arguably represent particularly high priorities given this framework, I conclude by identifying three overarching areas of risk assessment and management that, if not given commensurate attention, threaten to render even perfect dose-response information of dubious value.     

Multicriteria Bayesian Analysis of Lower Trophic Level Uncertainties and Value of Research in Lake Erie

Human activities have severely disrupted the Lake Erie ecosystem. Recent changes in the structure of the lower trophic level associated with exotic species invasions and reduced nutrient loading have created ecological uncertainties for fisheries management. Decisions that naïvely assume certainty may be different and suboptimal compared to choices that consider uncertainty. Here we illustrate how multiobjective Bayesian decision analysis can recognize the multiple goals of management in evaluations of the effect of ecological uncertainties on management and the value of information from ecological research. Value judgments and subjective probabilities required by the decision analysis were provided by six Lake Erie fishery agency biologists. The Lake Erie Ecological Model was used to project the impacts of each combination of management actions and lower trophic level parameter values. The analysis shows that explicitly considering lower trophic level uncertainties can alter decisions concerning Lake Erie fishery harvests. Of the research projects considered, investigation of goby predation of zebra mussels (Dreissena sp.) and lakewide estimation of secondary production appear to have the greatest expected value for fisheries management. We also find that changes in the weights assigned to management goals affects decisions and value of information more than do changes in probability judgments.     

Adaptive Management and the Value of Information: Learning Via Intervention in Epidemiology

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding.     

Using social values in the prioritization of research: Quantitative examples and generalizations

1. Identifying critical uncertainties about ecological systems can help prioritize research efforts intended to inform management decisions. However, exclusively focusing on the ecological system neglects the objectives of natural resource managers and the associated social values tied to risks and rewards of actions.
2. I demonstrate how to prioritize research efforts for a harvested population by applying expected value of perfect information (EVPI) to harvest decisions made with a density‐independent matrix population model. Research priorities identified by EVPI diverge from priorities identified by matrix elasticity analyses that ignore social utility.
3. Using a density‐dependent harvest model, the value of information about the intrinsic productivity of a population is shown to be sensitive to the socially determined penalty for implementing a harvest rate that deviates from the goal because of imperfection in estimation.
4. Synthesis and applications. The effect of including social values into harvest decision‐making depends on the assumed population model, uncertainty in population vital rates, and the particular form of the utility function used to represent risk/reward of harvest. EVPI analyses that include perceived utility of different outcomes can be used by managers seeking to optimize monitoring and research spending. Collaboration between applied ecologists and social scientists that quantitatively measure peoples'' values is needed in many structured decision‐making processes.

     

Adequacy Conditions for Reporting Uncertainty in Chemical Risk Assessments

Uncertainty may influence decision-making. A prerequisite for a decision to be well founded is thus that scientific experts inform decision-makers about all decision relevant uncertainty. A set of conditions is provided for adequate characterization of scientific uncertainty for the purposes of regulatory decision-making. These conditions require specification of (1) the character and degree of uncertainty about the assessment variables, (2) the possibility of reducing the uncertainty, and (3) the degree of agreement among experts. Furthermore, it is required that (4) the information covered by the previous conditions is presented in a clear and comprehensible way. The point of departure is that characterizing scientific uncertainty conceptually means specifying all potentially important possibilities that are consistent with the state of scientific knowledge. The conditions are intended to be applied to human health risk assessment of chemicals. However, the basic approach, to consider potentially important possibilities, should be useful also to environmental, and site-specific risk assessment.     

长三角平原区县域土壤磷素流失风险及其空间不确定性的快速评估

磷素是水体富营养化的关键限制因子,其中从农田土壤中流失的磷往往是水体磷素的主要来源。然而,土壤磷素的流失风险不仅与土壤磷素水平直接相关,其他环境因子,如距受纳水体距离、磷肥施用量、地表径流潜力等也强烈影响其流失风险。同时,基于有限样本预测得到的流失风险必然具有一定的空间不确定性。以长三角典型县域金坛区为研究案例,首先结合多个环境因子构建快速磷指数(RPI)评估模型,再利用稳健地统计学方法识别土壤全磷的空间离群值,并利用序贯高斯模型(SGS)模拟土壤全磷可能的空间分布格局,最后将其多个可能的模拟结果及上述主要因子输入到RPI模型,用以快速评估土壤磷素流失风险及其空间不确定性。结果显示,金坛区土壤磷素流失的高风险区和土壤全磷高值区分布格局在研究区北部、中部具有一定的相似性,而在中西部的旱地区两者出现差异性。高风险区主要沿着河流呈现条带状及斑块状分布,较高及以上风险区(快速磷指数值大于0.93)的面积占金坛区面积的65.88%。概率阈值分别设定为0.50、0.75、0.85、0.95时,其超标面积占金坛区总面积分别达到16.71%、5.74%、2.84%、1.04%。引入多个相关环境因子并结合经稳健处理的SGS进行流失风险指数的空间模拟和不确定性评估,可以快速评估区域农田土壤磷素流失风险及不确定性,进而为区域土壤磷素调控提供必要的空间决策支持。     

Intensive case finding and isoniazid preventative therapy in HIV infected individuals in Africa: economic model and value of information analysis

BACKGROUND: Tuberculosis (TB) accounts of much of the morbidity and mortality associated with HIV. We evaluate the cost-effectiveness of different strategies to actively screen for TB disease in HIV positive individuals, where isoniazid preventative therapy (IPT) is given to those screening negative, and use value of information analysis (VOI) to identify future research priorities. METHODOLOGY/ PRINCIPAL FINDINGS: We built an individual sampling model to investigate the costs (2010 US Dollars) and consequences of screening for TB, and providing TB treatment or IPT in adults testing HIV positive in Sub-Saharan Africa. A systematic review and meta-analysis was conducted to assess performance of the nine different TB screening strategies evaluated. Probabilistic sensitivity analysis was conducted to incorporate decision uncertainty, and expected value of perfect information for the entire model and for groups of parameters was calculated. Screening all HIV infected individuals with sputum microscopy was the least costly strategy, with other strategies not cost-effective at WHO recommended thresholds. Screening those with TB symptoms with sputum microscopy and CXR would be cost-effective at a threshold ICER of $7,800 per quality-adjusted life year (QALY), but associated with significant uncertainty. VOI analysis suggests further information would be of value. CONCLUSIONS/ SIGNIFICANCE: Resource-constrained countries in sub-Saharan Africa wishing to scale up TB preventative services in their HIV infected populations should consider expanding laboratory facilities to enable increased screening for TB with sputum microscopy, whilst improved estimates of the TB prevalence in the population to be screened are needed, as it may influence the optimal strategy.     

Abuse of hypothesis testing statistics in ecological risk assessment

Statistical hypothesis testing is commonly used inappropriately to analyze data, determine causality, and make decisions about significance in ecological risk assessment. Hypothesis testing is conceptually inappropriate in that it is designed to test scientific hypotheses rather than to estimate risks. It is inappropriate for analysis of field studies because it requires replication and random assignment of treatments. It discourages good toxicity testing and field studies, it provides less protection to ecosystems or their components that are difficult to sample or replicate, and it provides less protection when more treatments or responses are used. It provides a poor basis for decision‐making because it does not generate a conclusion of no effect, it does not indicate the nature or magnitude of effects, it does not address effects at untested exposure levels, and it confounds effects and uncertainty. Attempts to make hypothesis testing less problematical cannot solve these problems. Rather, risk assessors should focus on analyzing the relationship between exposure and effects, on presenting a clear estimate of expected or observed effects and associated uncertainties, and on providing the information in a manner that is useful to decision‐makers and the public.     

Using the Value of Information to improve conservation decision making

Conservation decisions are challenging, not only because they often involve difficult conflicts among outcomes that people value, but because our understanding of the natural world and our effects on it is fraught with uncertainty. Value of Information (VoI) methods provide an approach for understanding and managing uncertainty from the standpoint of the decision maker. These methods are commonly used in other fields (e.g. economics, public health) and are increasingly used in biodiversity conservation. This decision‐analytical approach can identify the best management alternative to select where the effectiveness of interventions is uncertain, and can help to decide when to act and when to delay action until after further research. We review the use of VoI in the environmental domain, reflect on the need for greater uptake of VoI, particularly for strategic conservation planning, and suggest promising areas for new research. We also suggest common reporting standards as a means of increasing the leverage of this powerful tool. The environmental science, ecology and biodiversity categories of the Web of Knowledge were searched using the terms ‘Value of Information,’ ‘Expected Value of Perfect Information,’ and the abbreviation ‘EVPI.’ Google Scholar was searched with the same terms, and additionally the terms decision and biology, biodiversity conservation, fish, or ecology. We identified 1225 papers from these searches. Included studies were limited to those that showed an application of VoI in biodiversity conservation rather than simply describing the method. All examples of use of VOI were summarised regarding the application of VoI, the management objectives, the uncertainties, the models used, how the objectives were measured, and the type of VoI. While the use of VoI appears to be on the increase in biodiversity conservation, the reporting of results is highly variable, which can make it difficult to understand the decision context and which uncertainties were considered. Moreover, it was unclear if, and how, the papers informed management and policy interventions, which is why we suggest a range of reporting standards that would aid the use of VoI. The use of VoI in conservation settings is at an early stage. There are opportunities for broader applications, not only for species‐focussed management problems, but also for setting local or global research priorities for biodiversity conservation, making funding decisions, or designing or improving protected area networks and management. The long‐term benefits of applying VoI methods to biodiversity conservation include a more structured and decision‐focused allocation of resources to research.     

Confronting Uncertainty in Life Cycle Assessment Used for Decision Support

The aim of this article is to help confront uncertainty in life cycle assessments (LCAs) used for decision support. LCAs offer a quantitative approach to assess environmental effects of products, technologies, and services and are conducted by an LCA practitioner or analyst (AN) to support the decision maker (DM) in making the best possible choice for the environment. At present, some DMs do not trust the LCA to be a reliable decision‐support tool—often because DMs consider the uncertainty of an LCA to be too large. The standard evaluation of uncertainty in LCAs is an ex‐post approach that can be described as a variance simulation based on individual data points used in an LCA. This article develops and proposes a taxonomy for LCAs based on extensive research in the LCA, management, and economic literature. This taxonomy can be used ex ante to support planning and communication between an AN and DM regarding which type of LCA study to employ for the decision context at hand. This taxonomy enables the derivation of an LCA classification matrix to clearly identify and communicate the type of a given LCA. By relating the LCA classification matrix to statistical principles, we can also rank the different types of LCA on an expected inherent uncertainty scale that can be used to confront and address potential uncertainty. However, this article does not attempt to offer a quantitative approach for assessing uncertainty in LCAs used for decision support.     

Weighting in Practice:Implications for the Use of Life-Cycle Assessment in Decision Making

This article investigates how environmental trade-offs are handled in life-cycle assessment (LCA) studies in some Nordic companies. Through interviews, the use and understanding of weighting methods in decision making was studied. The analysis shows that the decision makers require methods with which to aggregate and help interpret the complex information from life-cycle inventories. They agreed that it was not their own values that should be reflected in such methods, but they were found to have different opinions concerning the value basis that should be used. The analysis also investigates the difficulties arising from using such methods. The decision makers seemed to give a broader meaning to the term weighting, and were more concerned with the comparison between environmental and other aspects than the weighting of different environmental impacts. A conclusion is that decision makers need to be more involved in modeling and interpretation. The role of the analyst should be to interpret the information needs of the decision maker, and help him or her make methodological choices that are consistent with these needs and relevant from his or her point of view. To achieve this, it is important that decision makers do not view LCA as a highly standardized calculation tool, but as a flexible process of collecting, organizing, and interpreting environmental information. Such an approach to LCA increases the chances that the results will be regarded as relevant and useful.     

Uncertainty and Denial: A Resource-Rational Model of the Value of Information

Classical decision theory predicts that people should be indifferent to information that is not useful for making decisions, but this model often fails to describe human behavior. Here we investigate one such scenario, where people desire information about whether an event (the gain/loss of money) will occur even though there is no obvious decision to be made on the basis of this information. We find a curious dual trend: if information is costless, as the probability of the event increases people want the information more; if information is not costless, people''s desire for the information peaks at an intermediate probability. People also want information more as the importance of the event increases, and less as the cost of the information increases. We propose a model that explains these results, based on the assumption that people have limited cognitive resources and obtain information about which events will occur so they can determine whether to expend effort planning for them.     

Intrinsic Valuation of Information in Decision Making under Uncertainty

In a dynamic world, an accurate model of the environment is vital for survival, and agents ought regularly to seek out new information with which to update their world models. This aspect of behaviour is not captured well by classical theories of decision making, and the cognitive mechanisms of information seeking are poorly understood. In particular, it is not known whether information is valued only for its instrumental use, or whether humans also assign it a non-instrumental intrinsic value. To address this question, the present study assessed preference for non-instrumental information among 80 healthy participants in two experiments. Participants performed a novel information preference task in which they could choose to pay a monetary cost to receive advance information about the outcome of a monetary lottery. Importantly, acquiring information did not alter lottery outcome probabilities. We found that participants were willing to incur considerable monetary costs to acquire payoff-irrelevant information about the lottery outcome. This behaviour was well explained by a computational cognitive model in which information preference resulted from aversion to temporally prolonged uncertainty. These results strongly suggest that humans assign an intrinsic value to information in a manner inconsistent with normative accounts of decision making under uncertainty. This intrinsic value may be associated with adaptive behaviour in real-world environments by producing a bias towards exploratory and information-seeking behaviour.     

Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective

For a patient who is facing a treatment decision, the added value of information provided by a biomarker depends on the individual patient’s expected response to treatment with and without the biomarker, as well as his/her tolerance of disease and treatment harm. However, individualized estimators of the value of a biomarker are lacking. We propose a new graphical tool named the subject-specific expected benefit curve for quantifying the personalized value of a biomarker in aiding a treatment decision. We develop semiparametric estimators for two general settings: (i) when biomarker data are available from a randomized trial; and (ii) when biomarker data are available from a cohort or a cross-sectional study, together with external information about a multiplicative treatment effect. We also develop adaptive bootstrap confidence intervals for consistent inference in the presence of nonregularity. The proposed method is used to evaluate the individualized value of the serum creatinine marker in informing treatment decisions for the prevention of renal artery stenosis.     

Managing uncertainty: information and insurance under the risk of starvation

In an uncertain world, animals face both unexpected opportunities and danger. Such outcomes can select for two potential strategies: collecting information to reduce uncertainty, or insuring against it. We investigate the relative value of information and insurance (energy reserves) under starvation risk by offering model foragers a choice between constant and varying food sources over finite foraging bouts. We show that sampling the variable option (choosing it when it is not expected to be good) should decline both with lower reserves and late in foraging bouts; in order to be able to reap the reduction in uncertainty associated with exploiting a variable resource effectively, foragers must be able to afford and compensate for an initial increase in the risk of an energetic shortfall associated with choosing the option when it is bad. Consequently, expected exploitation of the varying option increases as it becomes less variable, and when the overall risk of energetic shortfall is reduced. In addition, little activity on the variable alternative is expected until reserves are built up early in a foraging bout. This indicates that gathering information is a luxury while insurance is a necessity, at least when foraging on stochastic and variable food under the risk of starvation.     

Modeling process and material alternatives in life cycle assessments

Background, Aims and Scope  Although LCA is frequently used in product comparison, many practitioners are interested in identifying and assessing improvements within a life cycle. Thus, the goals of this work are to provide guidelines for scenario formulation for process and material alternatives within a life cycle inventory and to evaluate the usefulness of decision tree and matrix computational structures in the assessment of material and process alternatives. We assume that if the analysis goal is to guide the selection among alternatives towards reduced life cycle environmental impacts, then the analysis should estimate the inventory results in a manner that: (1) reveals the optimal set of processes with respect to minimization of each impact of interest, and (2) minimizes and organizes computational and data collection needs. Methods  A sample industrial system is used to reveal the complexities of scenario formulation for process and material alternatives in an LCI. The system includes 4 processes, each executable in 2 different ways, as well as 1 process able to use 2 different materials interchangeably. We formulate and evaluate scenarios for this system using three different methods and find advantages and disadvantages with each. First, the single branch decision tree method stays true to the typical construction of decision trees such that each branch of the tree represents a single scenario. Next, the process flow decision tree method strays from the typical construction of decision trees by following the process flow of the product system, such that multiple branches are needed to represent a single scenario. In the final method, disaggregating the demand vector, each scenario is represented by separate vectors which are combined into a matrix to allow the simultaneous solution of the inventory problem for all scenarios. Results  For both decision tree and matrix methods, scenario formulation, data collection, and scenario analysis are facilitated in two ways. First, process alternatives that cannot actually be chosen should be modeled as sub-inventories (or as a complete LCI within an LCI). Second, material alternatives (e.g., a choice between structural materials) must be maintained within the analysis to avoid the creation of artificial multi-functional processes. Further, in the same manner that decision trees can be used to estimate ‘expected value’ (the sum of the probability of each scenario multiplied by its ‘value’), we find that expected inventory and impact results can be defined for both decision tree and matrix methods. Discussion  For scenario formulation, naming scenarios in a way that differentiate them from other scenarios is complex and important in the continuing development of LCI data for use in databases or LCA software. In the formulation and assessment of scenarios, decision tree methods offer some level of visual appeal and the potential for using commercially available software/ traditional decision tree solution constructs for estimating expected values (for relatively small or highly aggregated product systems). However, solving decision tree systems requires the use of sequential process scaling which is difficult to formalize with mathematical notation. In contrast, preparation of a demand matrix does not require use of the sequential method to solve the inventory problem but requires careful scenario tracking efforts. Conclusions  Here, we recognize that improvements can be made within a product system. This recognition supports the greater use of LCA in supply chain formation and product research, development, and design. We further conclude that although both decision tree and matrix methods are formulated herein to reveal optimal life cycle scenarios, the use of demand matrices is preferred in the preparation of a formal mathematical construct. Further, for both methods, data collection and assessment are facilitated by the use of sub-inventories (or as a complete LCI within an LCI) for process alternatives and the full consideration of material alternatives to avoid the creation of artificial multi-functional processes. Recommendations and Perspectives  The methods described here are used in the assessment of forest management alternatives and are being further developed to form national commodity models considering technology alternatives, national production mixes and imports, and point-to-point transportation models. ESS-Submission Editor: Thomas Gloria, PhD (t.gloria@fivewinds.com)     

Uncertainty in environmentally conscious decision making: beer or wine?

Purpose

Life cycle assessment (LCA) is being used increasingly in decision support situations. In actual cases, the sources of uncertainty are easily hidden in the complexity. Methods for taking uncertainty into account are recommended by LCA guidelines, but actual application remains rare. The aim of this study is to demonstrate the sources of uncertainty in a practical simple selection case wherein a customer makes a decision between beer and wine in a restaurant, considering the selected criteria and the given information. The uncertainty in LCA results is connected to the broader scope of decision analysis.

Methods

Life cycle inventories were collected for beer and wine production from existing literature. The functional unit was chosen to be one serving of alcohol: beer or wine. For illustrative purposes, only the global warming potential indicator was included in the LCA through carbon footprint (CF). Probabilistic uncertainty analysis was applied to the CF system using Monte Carlo simulation. Water footprint was also roughly considered. In addition, three non-environmental indicators were included in the decision: weight control, price, and taste. The comparison between the two products was constructed as a multiple-criteria decision analytical problem.

Results and discussion

The results indicated that beer had, on average, a higher CF value than wine did. However, the difference was not significant, and within the uncertainty range, also the opposite conclusion was possible. The ratio of wine to beer CF was dominated by the uncertainty in the N₂O emissions of wine production. When all of the decision criteria were included, the level of uncertainty prevented robust overall conclusions about preference for beer or wine. However, depending on the utility differences assigned to subjective indicators, there existed also cases wherein decisions could be made at a 10?% risk level regardless of high overall uncertainty.

Conclusions

In many cases, the uncertainties of LCA are dwarfed by the overall uncertainty of the decision situation. However, as shown by our example, in many cases, reasonable decisions can be made in spite of high uncertainties. The uncertainties of single LCA indicators should be considered in relation to the decision-making problem, which depends on the uncertainty of LCA indicators but also significantly on the weighting of the indicators and the related uncertainty. Successful decision making depends on both the magnitude of uncertainty and the differences in expected utility value between alternatives. More attention should be paid to uncertainty analysis considering the weighting factors.     

Theory of Choice in Bandit,Information Sampling and Foraging Tasks

Decision making has been studied with a wide array of tasks. Here we examine the theoretical structure of bandit, information sampling and foraging tasks. These tasks move beyond tasks where the choice in the current trial does not affect future expected rewards. We have modeled these tasks using Markov decision processes (MDPs). MDPs provide a general framework for modeling tasks in which decisions affect the information on which future choices will be made. Under the assumption that agents are maximizing expected rewards, MDPs provide normative solutions. We find that all three classes of tasks pose choices among actions which trade-off immediate and future expected rewards. The tasks drive these trade-offs in unique ways, however. For bandit and information sampling tasks, increasing uncertainty or the time horizon shifts value to actions that pay-off in the future. Correspondingly, decreasing uncertainty increases the relative value of actions that pay-off immediately. For foraging tasks the time-horizon plays the dominant role, as choices do not affect future uncertainty in these tasks.