Part II: Dealing with parameter uncertainty and uncertainty due to choices in life cycle assessment

Results of product assessments are often criticised as to their handling of uncertainty. Therefore, it is necessary to develop a comprehensive methodology reflecting parameter uncertainty in combination with uncertainty due to choices in the outcome of LCAs. This paper operationalises the effect of combined parameter uncertainties in the inventory and in the characterisation factors for global warming and acidification for the comparison of two exemplary types of roof gutters. For this purpose, Latin Hypercube sampling is used in the matrix (inventory) method. To illustrate the influence of choices, the effect on LCA outcomes is shown of two different allocation procedures in open-loop recycling and three time horizons for global warming potentials. Furthermore, an uncertainty importance analysis is performed to show which parameter uncertainties mainly contribute to uncertainties in the comparison and the separate environmental profiles of the product systems. These results can be used to prioritise further data research.     

Managing uncertainty in family practice.

Because patients present in the early stages of undifferentiated problems, the family physician often faces uncertainty, especially in diagnosis and management. The physician''s uncertainty may be unacceptable to the patient and may lead to inappropriate use of diagnostic procedures. The problem is intensified by the physician''s hospital training, which emphasizes mastery of available knowledge and decision-making based on certainty. Strategies by which a physician may manage uncertainty include (a) a more open doctor-patient relationship, (b) understanding the patient''s reason for attending the office, (c) a thorough assessment of the problem, (d) a commitment to reassessment and (e) appropriate consultation.     

Evaluating model reduction under parameter uncertainty

Background

The dynamics of biochemical networks can be modelled by systems of ordinary differential equations. However, these networks are typically large and contain many parameters. Therefore model reduction procedures, such as lumping, sensitivity analysis and time-scale separation, are used to simplify models. Although there are many different model reduction procedures, the evaluation of reduced models is difficult and depends on the parameter values of the full model. There is a lack of a criteria for evaluating reduced models when the model parameters are uncertain.

Results

We developed a method to compare reduced models and select the model that results in similar dynamics and uncertainty as the original model. We simulated different parameter sets from the assumed parameter distributions. Then, we compared all reduced models for all parameter sets using cluster analysis. The clusters revealed which of the reduced models that were similar to the original model in dynamics and variability. This allowed us to select the smallest reduced model that best approximated the full model. Through examples we showed that when parameter uncertainty was large, the model should be reduced further and when parameter uncertainty was small, models should not be reduced much.

Conclusions

A method to compare different models under parameter uncertainty is developed. It can be applied to any model reduction method. We also showed that the amount of parameter uncertainty influences the choice of reduced models.

     

Accounting for uncertainty in marine reserve design

Ecosystems and the species and communities within them are highly complex systems that defy predictions with any degree of certainty. Managing and conserving these systems in the face of uncertainty remains a daunting challenge, particularly with respect to developing networks of marine reserves. Here we review several modelling frameworks that explicitly acknowledge and incorporate uncertainty, and then use these methods to evaluate reserve spacing rules given increasing levels of uncertainty about larval dispersal distances. Our approach finds similar spacing rules as have been proposed elsewhere – roughly 20–200 km – but highlights several advantages provided by uncertainty modelling over more traditional approaches to developing these estimates. In particular, we argue that uncertainty modelling can allow for (1) an evaluation of the risk associated with any decision based on the assumed uncertainty; (2) a method for quantifying the costs and benefits of reducing uncertainty; and (3) a useful tool for communicating to stakeholders the challenges in managing highly uncertain systems. We also argue that incorporating rather than avoiding uncertainty will increase the chances of successfully achieving conservation and management goals.     

Management procedures in a fishery based on highly variable stocks and with conflicting objectives: experiences in the South African pelagic fishery

The pelagic fishery in South Africa targets mainly anchovy, Engraulis capensis, and sardine, Sardinops sagax, both of which have varied substantially in abundance during the history of the fishery. Since 1988, there has been progress in this fishery towards the use of management procedures as the basis for determination of management regulations, where a management procedure is defined as a set of rules, derived by simulation and normally implemented for three to five years, specifying how the regulatory mechanism is set, the data collected for this purpose and how these data are to be analysed and used. Advantages of management procedures include formal consideration of uncertainty, the ability to choose decision rules based on their predicted medium-term consequences and a saving in workload compared with annual assessments.This paper discusses the lessons learned in application of management procedures and their precursors in this fishery. The high variability in abundance of the two stocks, the trend in their relative abundance, the substantial uncertainties in information, strong pressure to meet socio-economic goals and the conflicting objectives which arose between the directed anchovy and directed sardine fishery are identified as major problems in implementation of procedures and management of the resources. However, the use of management procedures is considered to have led to greatly improved communication with the industry and to substantial input by them into the management process. The procedures and the simulations upon which they were based also enabled consideration of the major sources of uncertainty in understanding of the resource dynamics and facilitated the development of procedures that were robust to them.It is argued that biological uncertainty greatly exacerbated the problems in application of the procedures but probably cannot be markedly reduced in the near future. Management procedures must be robust to likely variability and uncertainty. Of equal importance are identification and selection of achievable objectives, and allocation to the political decision makers and not to the scientists, of responsibility for determining acceptable trade-offs between conservation and socio-economic goals. Other issues, including the importance of long-term rights and allowance for flexibility in fishing practice, are also highlighted     

Confronting Uncertainty in Wildlife Management: Performance of Grizzly Bear Management

Scientific management of wildlife requires confronting the complexities of natural and social systems. Uncertainty poses a central problem. Whereas the importance of considering uncertainty has been widely discussed, studies of the effects of unaddressed uncertainty on real management systems have been rare. We examined the effects of outcome uncertainty and components of biological uncertainty on hunt management performance, illustrated with grizzly bears (Ursus arctos horribilis) in British Columbia, Canada. We found that both forms of uncertainty can have serious impacts on management performance. Outcome uncertainty alone – discrepancy between expected and realized mortality levels – led to excess mortality in 19% of cases (population-years) examined. Accounting for uncertainty around estimated biological parameters (i.e., biological uncertainty) revealed that excess mortality might have occurred in up to 70% of cases. We offer a general method for identifying targets for exploited species that incorporates uncertainty and maintains the probability of exceeding mortality limits below specified thresholds. Setting targets in our focal system using this method at thresholds of 25% and 5% probability of overmortality would require average target mortality reductions of 47% and 81%, respectively. Application of our transparent and generalizable framework to this or other systems could improve management performance in the presence of uncertainty.     

Applying Fuzzy and Probabilistic Uncertainty Concepts to the Material Flow Analysis of Palladium in Austria

Material flow analysis (MFA) is a widely applied tool to investigate resource and recycling systems of metals and minerals. Owing to data limitations and restricted system understanding, MFA results are inherently uncertain. To demonstrate the systematic implementation of uncertainty analysis in MFA, two mathematical concepts for the quantification of uncertainties were applied to Austrian palladium (Pd) resource flows and evaluated: (1) uncertainty ranges expressed by fuzzy sets and (2) uncertainty ranges defined by normal distributions given as mean values and standard deviations. Whereas normal distributions represent the traditional approach for quantifying uncertainties in MFA, fuzzy sets may offer additional benefits in relation to uncertainty quantification in cases of scarce information. With respect to the Pd case study, the fuzzy representation of uncertain quantities is more consistent with the actual data availability in cases of incomplete databases, and fuzzy sets serve to highlight the effect of uncertainty on resource efficiency indicators derived from the MFA results. For both approaches, data reconciliation procedures offer the potential to reduce uncertainty and evaluate the plausibility of the model results. With respect to Pd resource management, improved formal collection of end‐of‐life (EOL) consumer products is identified as a key factor in increasing the recycling efficiency. In particular, the partial export of EOL vehicles represents a substantial loss of Pd from the Austrian resource system, whereas approximately 70% of the Pd in the EOL consumer products is recovered in waste management. In conclusion, systematic uncertainty analysis is an integral part of MFA required to provide robust decision support in resource management.     

Probabilistic methods for addressing uncertainty and variability in biological models: application to a toxicokinetic model

Population variability and uncertainty are important features of biological systems that must be considered when developing mathematical models for these systems. In this paper we present probability-based parameter estimation methods that account for such variability and uncertainty. Theoretical results that establish well-posedness and stability for these methods are discussed. A probabilistic parameter estimation technique is then applied to a toxicokinetic model for trichloroethylene using several types of simulated data. Comparison with results obtained using a standard, deterministic parameter estimation method suggests that the probabilistic methods are better able to capture population variability and uncertainty in model parameters.     

Standardisation of DNA quantitation by image analysis: quality control of instrumentation.

BACKGROUND: DNA image analysis is frequently performed in clinical practice as a prognostic tool and to improve diagnosis. The precision of prognosis and diagnosis depends on the accuracy of analysis and particularly on the quality of image analysis systems. It has been reported that image analysis systems used for DNA quantification differ widely in their characteristics (Thunissen et al.: Cytometry 27: 21-25, 1997). This induces inter-laboratory variations when the same sample is analysed in different laboratories. In microscopic image analysis, the principal instrumentation errors arise from the optical and electronic parts of systems. They bring about problems of instability, non-linearity, and shading and glare phenomena. METHODS: The aim of this study is to establish tools and standardised quality control procedures for microscopic image analysis systems. Specific reference standard slides have been developed to control instability, non-linearity, shading and glare phenomena and segmentation efficiency. RESULTS: Some systems have been controlled with these tools and these quality control procedures. Interpretation criteria and accuracy limits of these quality control procedures are proposed according to the conclusions of a European project called PRESS project (Prototype Reference Standard Slide). Beyond these limits, tested image analysis systems are not qualified to realise precise DNA analysis. CONCLUSIONS: The different procedures presented in this work determine if an image analysis system is qualified to deliver sufficiently precise DNA measurements for cancer case analysis. If the controlled systems are beyond the defined limits, some recommendations are given to find a solution to the problem.     

To perceive is to doubt: The relativity of perception

The uncertainty or entropy theory of perception is founded on the premise that for perception to occur, there must first of all be uncertainty. That is, perception or awareness is relative to the expectation of the perceiver. This view of perception leads to a seeming-paradox. How can there be uncertainty unless the alternatives have previously been perceived? But, by the premise of the theory, how can the alternatives have been perceived unless there was prior uncertainty? It is shown that this paradox may result physiologically in the concurrence of sensory and motor (or “active”) events during the process of perceiving. It is shown, further, that a close analogy exists between systems of formal logic and systems which perceive through uncertainty. This, in turn, suggests a basis for a calculus of perception.     

Aspects of statistics in studies of cell proliferation. II. Standard errors of estimated parameters

Statistical methods such as regression analysis are often used to estimate cell kinetic parameters, such as cell birth rate or tumour growth rate. The standard errors which the statistical procedures provide may be considerable underestimates of the true uncertainty surrounding the parameter of interest.     

Expert systems in histopathology. V. DS theory, certainty factors and possibility theory.

Uncertainty management for the evaluation of evidence based on linguistic and conceptual data is taking advantage of developments in the Dempster-Shafer (DS) theory of evidence, possibility theory and fuzzy logic. The DS theory offers the capability to assess the uncertainty of different subsets of assertions in a domain and the way in which uncertainty is affected by accumulating evidence. The DS theory goes beyond probability theory in its ability to represent ignorance about certain aspects of a situation. However, the theory is very sensitive to the numerical assessments provided by users and can lead to intuitively unexpected and even undesirable results. Certainty factors are widely used in various expert systems. Their definition and updating may follow either a probabilistic model or fuzzy set theoretic concept.     

AUGIST: inferring species trees while accommodating gene tree uncertainty

SUMMARY: AUGIST (accomodating uncertainty in genealogies while inferring species tress) is a new software package for inferring species trees while accommodating uncertainty in gene genealogies. It is written for the Mesquite software system and provides sampling procedures to incorporate uncertainty in gene tree reconstruction while providing confidence estimates for inferred species trees. AVAILABILITY: http://www.lycaenid.org/augist/     

Traceability, reference systems and result comparability

The standardisation of measurements is of high priority in laboratory medicine, its purpose being to achieve closer comparability of results obtained using routine measurement procedures. At present, there is international cooperation in developing reference measurement systems (reference methods, reference materials, and reference laboratory networks) for analytes of clinical significance. These reference systems will reduce, wherever possible, measurement uncertainty and promote the comparability of results. The implementation of measurement traceability through the reference system provides one of the most important tools that supports the standardisation process in laboratory medicine. It aims to achieve result comparability regardless of the measurement procedure (test kit) and the clinical laboratory where analyses are carried out. The aim of this review is to discuss some concepts related to the achievement of standardisation by the implementation of a metrologically-correct measurement system and to provide some examples that illustrate the complexity of this approach and the impact of these activities on patient care.     

The propagation of uncertainty in human mortality processes operating in stochastic environments

This paper presents a model describing how the uncertainty due to influential exogenous processes combines with stochasticity intrinsic to physiological aging processes and propagates through time to generate uncertainty about the future physiological state of the population. Variance expressions are derived for (a) the future values of the physiological variables under the assumption that external factors evolve under a linear stochastic diffusion process, and (b) the cohort survival functions and cohort life expectancies which reflect the uncertainty in the future values of the physiological variables. The model implies that a major component of uncertainty in forecasts of the physiological characteristics of a closed cohort is due to differential rates of survival associated with different realizations of the external process. This suggests that the limits to forecasting may be different in physiological systems subject to systematic mortality than in physical systems such as weather where the concepts of closed cohorts and of mortality selection have no simple analog.     

Higher mental functions and the systems analysis of behavior under conditions of uncertainty

Superior psychical functions (skills) are usually formed in great subjective uncertainty conditions. Because of that exact knowledge of information mechanisms of behavior activity under uncertainty conditions is very importent. Some difficulties of traditional notions (classic behavior act model developed by P. K. Anokhin, systems quantization of behavior) are discussed. The model of probabilitive training allows to describe various training uncertainty conditions, and it operates as the Anokhin model in a specific case. The system-shaping factor in this model is an adaptive result. Afferent synthesis, aim for action, acceptor of the action result and the programme of actions have the traditional senses. Besides those it is proposed to use a concept of probability decision to alter the program of action, as well as an idea of memory buffer (results of search reactions). The organizational features of the functional system of behavior under great uncertainty conditions should be taken into account while specifying the informational mechanism responsible for systems quantization of behavior. The above mechanism can be used for explain the discreteness of an action in the program alteration process when the same quantum of behavior is implemented. These ideas are profitable for systems analysis of superior psychical functions, based on the reflex formation under environmental uncertainty conditions.     

The future of laboratory automation.

Among the many factors that will define the laboratory of the future are the development of advanced computer communications systems, artificial intelligence, robotic systems, and material storage and retrieval systems. This article examines some of these factors and challenges current automation justification procedures in light of the greater competitive environment of today.     

Characterizing,Propagating, and Analyzing Uncertainty in Life‐Cycle Assessment: A Survey of Quantitative Approaches

Life‐cycle assessment (LCA) practitioners build models to quantify resource consumption, environmental releases, and potential environmental and human health impacts of product systems. Most often, practitioners define a model structure, assign a single value to each parameter, and build deterministic models to approximate environmental outcomes. This approach fails to capture the variability and uncertainty inherent in LCA. To make good decisions, decision makers need to understand the uncertainty in and divergence between LCA outcomes for different product systems. Several approaches for conducting LCA under uncertainty have been proposed and implemented. For example, Monte Carlo simulation and fuzzy set theory have been applied in a limited number of LCA studies. These approaches are well understood and are generally accepted in quantitative decision analysis. But they do not guarantee reliable outcomes. A survey of approaches used to incorporate quantitative uncertainty analysis into LCA is presented. The suitability of each approach for providing reliable outcomes and enabling better decisions is discussed. Approaches that may lead to overconfident or unreliable results are discussed and guidance for improving uncertainty analysis in LCA is provided.     

Single particle tracking. Analysis of diffusion and flow in two-dimensional systems.

Analysis of the trajectories of small particles at high spatial and temporal resolution using video enhanced contrast microscopy provides a powerful approach to characterizing the mechanisms of particle motion in living cells and in other systems. We present here the theoretical basis for the analysis of these trajectories for particles undergoing random diffusion and/or systematic transport at uniform velocity in two-dimensional systems. The single particle tracking method, based on observations of the trajectories of individual particles, is compared with methods that characterize the motions of a large collection of particles such as fluorescence photobleaching recovery. Determination of diffusion coefficients or transport velocities either from correlation of positions or of velocities of the particles is discussed. A result of practical importance is an analysis of the dependence of the expected statistical uncertainty of these determinations on the number of position measurements. This provides a way of judging the accuracy of the diffusion coefficients and transport velocities obtained using this approach.