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Greater Uncertainty (greater + uncertainty)
Selected AbstractsProduct Development and Market Expansion: A Real Options ModelFINANCIAL MANAGEMENT, Issue 1 2007Andrea Gamba We create a model that values complementary and substitute products with potentially correlated revenues, which must be developed sequentially. The model also incorporates the effects of changing market conditions. We find that the value of a combined project increases in correlation, but the probability of investing in the initial product is a decreasing function of correlation. These results are reversed if the products are substitutes. Regardless of the correlation level, higher levels of substitutability reduce the value of the combined projects and increase the probability of investing. Despite greater uncertainty during the phase of limited competition, the firm is more likely to invest early than to postpone investment. [source] The influence of elevation error on the morphometrics of channel networks extracted from DEMs and the implications for hydrological modellingHYDROLOGICAL PROCESSES, Issue 11 2008John B. Lindsay Abstract Stream network morphometrics have been used frequently in environmental applications and are embedded in several hydrological models. This is because channel network geometry partly controls the runoff response of a basin. Network indices are often measured from channels that are mapped from digital elevation models (DEMs) using automated procedures. Simulations were used in this paper to study the influence of elevation error on the reliability of estimates of several common morphometrics, including stream order, the bifurcation, length, area and slope ratios, stream magnitude, network diameter, the flood magnitude and timing parameters of the geomorphological instantaneous unit hydrograph (GIUH) and the network width function. DEMs of three UK basins, ranging from high to low relief, were used for the analyses. The findings showed that moderate elevation error (RMSE of 1·8 m) can result in significant uncertainty in DEM-mapped network morphometrics and that this uncertainty can be expressed in complex ways. For example, estimates of the bifurcation, length and area ratios and the flood magnitude and timing parameters of the GIUH each displayed multimodal frequency distributions, i.e. two or more estimated values were highly likely. Furthermore, these preferential estimates were wide ranging relative to the ranges typically observed for these indices. The wide-ranging estimates of the two GIUH parameters represented significant uncertainty in the shape of the unit hydrograph. Stream magnitude, network diameter and the network width function were found to be highly sensitive to elevation error because of the difficulty in mapping low-magnitude links. Uncertainties in the width function were found to increase with distance from outlet, implying that hydrological models that use network width contain greater uncertainty in the shape of the falling limb of the hydrograph. In light of these findings, care should be exercised when interpreting the results of analyses based on DEM-mapped stream networks. Copyright © 2007 John Wiley & Sons, Ltd. [source] Meeting the ecological challenges of agricultural change: editors' introductionJOURNAL OF APPLIED ECOLOGY, Issue 6 2003S. J. Ormerod Summary 1The global need for agricultural production has never been greater. Nor has it ever been more complex due to the needs to balance global food security, optimum production, technological innovation, the preservation of environmental functions and the protection of biodiversity. The role of ecologists in finding this balance is pivotal. 2In support of this role, ecologists now have very substantial experience of agricultural systems. We can understand, recognize and sometimes predict, at least qualitatively, the effects of pesticide applications, fertilizer use, drainage, crop choices and habitat modifications on farmland organisms, agro-ecosystems or other ecosystems influenced by agricultural land. 3In instances of greater uncertainty, for example under changing climates, where environmental stresses on ecosystems are interactive, and where ecosystem management or restoration must adapt to new technologies, the investigative skills and experience of ecologists are even more crucial in problem solving. 4There are, nevertheless, contrasting examples of good and bad practice in knowledge-transfer between ecologists and the communities who need our knowledge. The UK farm-scale evaluations of genetically modified crops, for example, involved ecologists at all stages from design and data collection to advocating policy. By contrast, many European agri-environment projects appear to have been developed and evaluated with only modest ecological advice. We advocate fuller involvement of ecologists in the development and evaluation of the European Union Common Agricultural Policy. 5This special profile of seven agriculturally related papers illustrates effectively the array of approaches used by applied ecologists in addressing agricultural questions: modelling, meta-analysis, surveys, transect studies, classical experiments, seedbank assays and process studies based on modern ecological methods. With over 20% of recent papers in the Journal of Applied Ecology reflecting agricultural issues, agro-ecology continues to represent one of the pre-eminent areas of applied ecology that is unlikely to diminish in importance. [source] Interfirm Innovation under Uncertainty: Empirical Evidence for Strategic Knowledge Partitioning,THE JOURNAL OF PRODUCT INNOVATION MANAGEMENT, Issue 5 2008Jaegul Lee This paper analyzes how uncertainty and life-cycle effects condition the knowledge boundary between assemblers and suppliers in interfirm product development. Patents associated with automotive emission control technologies for both assemblers and suppliers are categorized as architectural or component innovations, and technology-forcing regulations imposed by the government on the auto industry from 1970 to 1998 are used to define periods of high and low uncertainty. Results confirm that suppliers dominate component innovation whereas assemblers lead on architectural innovation. More importantly, when facing uncertainty firms adjust their knowledge boundary by increasing the knowledge overlap with their supply-chain collaborators. Suppliers clearly expand their knowledge base relatively more into architectural knowledge during such periods. But assemblers' greater emphasis on component innovation in periods of greater uncertainty is only true as a relative deviation from an overall trend toward increasing component innovation over time. This trend results from an observed life-cycle effect, whereby architectural innovation dominates before the emergence of a dominant design, with component innovation taking the lead afterward. Thus, for assemblers life-cycle effects may dominate over task uncertainty in determining relative effort in component versus architectural innovation. This work extends research on strategic interfirm knowledge partitioning as well as on the information-processing view of product development. First, it provides a large-scale empirical justification for the claim that firms' knowledge boundaries need to extend beyond their task boundaries. Further, it implies that overlaps in knowledge domains between an assembler and suppliers are particularly important for projects involving new technologies. Second, it offers a dynamic view of knowledge partitioning, showing how architectural knowledge prevails in the early phase of the product life cycle whereas component knowledge dominates the later stages. Yet the importance of life-cycle effects versus task uncertainty in conditioning knowledge boundaries is different for assemblers and suppliers, with the former dominating for assemblers and the latter more influential for suppliers. Finally, it supports the idea that architectural and component knowledge are critical elements in the alignment of cognitive frameworks between assemblers and suppliers and thus are key for information-exchange effectiveness and resolution of task uncertainties in interfirm innovation. [source] Semiparametric Regression Modeling with Mixtures of Berkson and Classical Error, with Application to Fallout from the Nevada Test SiteBIOMETRICS, Issue 1 2002Bani Mallick Summary. We construct Bayesian methods for semiparametric modeling of a monotonic regression function when the predictors are measured with classical error, Berkson error, or a mixture of the two. Such methods require a distribution for the unobserved (latent) predictor, a distribution we also model semi-parametrically. Such combinations of semiparametric methods for the dose-response as well as the latent variable distribution have not been considered in the measurement error literature for any form of measurement error. In addition, our methods represent a new approach to those problems where the measurement error combines Berkson and classical components. While the methods are general, we develop them around a specific application, namely, the study of thyroid disease in relation to radiation fallout from the Nevada test site. We use this data to illustrate our methods, which suggest a point estimate (posterior mean) of relative risk at high doses nearly double that of previous analyses but that also suggest much greater uncertainty in the relative risk. [source] | ||||||||||