129 resultados para uncertainty aversion


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This research has responded to the need for diagnostic reference tools explicitly linking the influence of environmental uncertainty and performance within the supply chain. Uncertainty is a key factor influencing performance and an important measure of the operating environment. We develop and demonstrate a novel reference methodology based on data envelopment analysis (DEA) for examining the performance of value streams within the supply chain with specific reference to the level of environmental uncertainty they face. In this paper, using real industrial data, 20 product supply value streams within the European automotive industry sector are evaluated. Two are found to be efficient. The peer reference groups for the underperforming value streams are identified and numerical improvement targets are derived. The paper demonstrates how DEA can be used to guide supply chain improvement efforts through role-model identification and target setting, in a way that recognises the multiple dimensions/outcomes of the supply chain process and the influence of its environmental conditions. We have facilitated the contextualisation of environmental uncertainty and its incorporation into a specific diagnostic reference tool.

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The semi-distributed, dynamic INCA-N model was used to simulate the behaviour of dissolved inorganic nitrogen (DIN) in two Finnish research catchments. Parameter sensitivity and model structural uncertainty were analysed using generalized sensitivity analysis. The Mustajoki catchment is a forested upstream catchment, while the Savijoki catchment represents intensively cultivated lowlands. In general, there were more influential parameters in Savijoki than Mustajoki. Model results were sensitive to N-transformation rates, vegetation dynamics, and soil and river hydrology. Values of the sensitive parameters were based on long-term measurements covering both warm and cold years. The highest measured DIN concentrations fell between minimum and maximum values estimated during the uncertainty analysis. The lowest measured concentrations fell outside these bounds, suggesting that some retention processes may be missing from the current model structure. The lowest concentrations occurred mainly during low flow periods; so effects on total loads were small.

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A number of studies have addressed the relationship between intra-personal uncertainty and inter-personal disagreement about the future values of economic variables such as output growth and inflation using the SPF. By making use of the SPF respondents' probability forecasts of declines in output, we are able to construct a quarterly series of output growth uncertainty to supplement the annual series that are often used in such analyses. We also consider the relationship between disagreement and uncertainty for probability forecasts of declines in output.

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We consider the impact of data revisions on the forecast performance of a SETAR regime-switching model of U.S. output growth. The impact of data uncertainty in real-time forecasting will affect a model's forecast performance via the effect on the model parameter estimates as well as via the forecast being conditioned on data measured with error. We find that benchmark revisions do affect the performance of the non-linear model of the growth rate, and that the performance relative to a linear comparator deteriorates in real-time compared to a pseudo out-of-sample forecasting exercise.

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The nature of the climate–carbon cycle feedback depends critically on the response of soil carbon to climate, including changes in moisture. However, soil moisture–carbon feedback responses have not been investigated thoroughly. Uncertainty in the response of soil carbon to soil moisture changes could arise from uncertainty in the relationship between soil moisture and heterotrophic respiration. We used twelve soil moisture–respiration functions (SMRFs) with a soil carbon model (RothC) and data from a coupled climate–carbon cycle general circulation model to investigate the impact of direct heterotrophic respiration dependence on soil moisture on the climate carbon cycle feedback. Global changes in soil moisture acted to oppose temperature‐driven decreases in soil carbon and hence tended to increase soil carbon storage. We found considerable uncertainty in soil carbon changes due to the response of soil respiration to soil moisture. The use of different SMRFs resulted in both large losses and small gains in future global soil carbon stocks, whether considering all climate forcings or only moisture changes. Regionally, the greatest range in soil carbon changes across SMRFs was found where the largest soil carbon changes occurred. Further research is needed to constrain the soil moisture–respiration relationship and thus reduce uncertainty in climate–carbon cycle feedbacks. There may also be considerable uncertainty in the regional responses of soil carbon to soil moisture changes since climate model predictions of regional soil moisture changes are less coherent than temperature changes.

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Radar refractivity retrievals can capture near-surface humidity changes, but noisy phase changes of the ground clutter returns limit the accuracy for both klystron- and magnetron-based systems. Observations with a C-band (5.6 cm) magnetron weather radar indicate that the correction for phase changes introduced by local oscillator frequency changes leads to refractivity errors no larger than 0.25 N units: equivalent to a relative humidity change of only 0.25% at 20°C. Requested stable local oscillator (STALO) frequency changes were accurate to 0.002 ppm based on laboratory measurements. More serious are the random phase change errors introduced when targets are not at the range-gate center and there are changes in the transmitter frequency (ΔfTx) or the refractivity (ΔN). Observations at C band with a 2-μs pulse show an additional 66° of phase change noise for a ΔfTx of 190 kHz (34 ppm); this allows the effect due to ΔN to be predicted. Even at S band with klystron transmitters, significant phase change noise should occur when a large ΔN develops relative to the reference period [e.g., ~55° when ΔN = 60 for the Next Generation Weather Radar (NEXRAD) radars]. At shorter wavelengths (e.g., C and X band) and with magnetron transmitters in particular, refractivity retrievals relative to an earlier reference period are even more difficult, and operational retrievals may be restricted to changes over shorter (e.g., hourly) periods of time. Target location errors can be reduced by using a shorter pulse or identified by a new technique making alternate measurements at two closely spaced frequencies, which could even be achieved with a dual–pulse repetition frequency (PRF) operation of a magnetron transmitter.

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Rationale: Opioid antagonism reduces the consumption of palatable foods in humans but the neural substrates implicated in these effects are less well understood. Objectives: The aim of the present study was to examine the effects of the opioid antagonist, naltrexone, on neural response to rewarding and aversive sight and taste stimuli. Methods: We used functional magnetic resonance imaging (fMRI) to examine the neural responses to the sight and taste of pleasant (chocolate) and aversive (mouldy strawberry) stimuli in 20 healthy volunteers who received a single oral dose of naltrexone (50 mg) and placebo in a double-blind, repeated-measures cross-over, design. Results: Relative to placebo, naltrexone decreased reward activation to chocolate in the dorsal anterior cingulate cortex and caudate, and increased aversive-related activation to unpleasant strawberry in the amygdala and anterior insula. Conclusions: These findings suggest that modulation of key brain areas involved in reward processing, cognitive control and habit formation such as the dorsal anterior cingulate cortex (dACC) and caudate might underlie reduction in food intake with opioid antagonism. Furthermore we show for the first time that naltrexone can increase activations related to aversive food stimuli. These results support further investigation of opioid treatments in obesity.

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We compare and contrast the accuracy and uncertainty in forecasts of rents with those for a variety of macroeconomic series. The results show that in general forecasters tend to be marginally more accurate in the case of macro-economic series than with rents. In common across all of the series, forecasts tend to be smoothed with forecasters under-estimating performance during economic booms, and vice-versa in recessions We find that property forecasts are affected by economic uncertainty, as measured by disagreement across the macro-forecasters. Increased uncertainty leads to increased dispersion in the rental forecasts and a reduction in forecast accuracy.

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Future changes in the stratospheric circulation could have an important impact on Northern winter tropospheric climate change, given that sea level pressure (SLP) responds not only to tropospheric circulation variations but also to vertically coherent variations in troposphere-stratosphere circulation. Here we assess Northern winter stratospheric change and its potential to influence surface climate change in the Coupled Model Intercomparison Project – phase 5 (CMIP5) multi-model ensemble. In the stratosphere at high latitudes, an easterly change in zonally averaged zonal wind is found for the majority of the CMIP5 models, under the Representative Concentration Pathway 8.5 scenario. Comparable results are also found in the 1% CO2 increase per year projections, indicating that the stratospheric easterly change is common feature in future climate projections. This stratospheric wind change, however, shows a significant spread among the models. By using linear regression, we quantify the impact of tropical upper troposphere warming, polar amplification and the stratospheric wind change on SLP. We find that the inter-model spread in stratospheric wind change contributes substantially to the inter-model spread in Arctic SLP change. The role of the stratosphere in determining part of the spread in SLP change is supported by the fact that the SLP change lags the stratospheric zonally averaged wind change. Taken together, these findings provide further support for the importance of simulating the coupling between the stratosphere and the troposphere, to narrow the uncertainty in the future projection of tropospheric circulation changes.

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We present and experimentally test a theoretical model of majority threshold determination as a function of voters’ risk preferences. The experimental results confirm the theoretical prediction of a positive correlation between the voter's risk aversion and the corresponding preferred majority threshold. Furthermore, the experimental results show that a voter's preferred majority threshold negatively relates to the voter's confidence about how others will vote. Moreover, in a treatment in which individuals receive a private signal about others’ voting behaviour, the confidence-related motivation of behaviour loses ground to the signal's strength.

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The evidence for anthropogenic climate change continues to strengthen, and concerns about severe weather events are increasing. As a result, scientific interest is rapidly shifting from detection and attribution of global climate change to prediction of its impacts at the regional scale. However, nearly everything we have any confidence in when it comes to climate change is related to global patterns of surface temperature, which are primarily controlled by thermodynamics. In contrast, we have much less confidence in atmospheric circulation aspects of climate change, which are primarily controlled by dynamics and exert a strong control on regional climate. Model projections of circulation-related fields, including precipitation, show a wide range of possible outcomes, even on centennial timescales. Sources of uncertainty include low-frequency chaotic variability and the sensitivity to model error of the circulation response to climate forcing. As the circulation response to external forcing appears to project strongly onto existing patterns of variability, knowledge of errors in the dynamics of variability may provide some constraints on model projections. Nevertheless, higher scientific confidence in circulation-related aspects of climate change will be difficult to obtain. For effective decision-making, it is necessary to move to a more explicitly probabilistic, risk-based approach.

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We apply a new parameterisation of the Greenland ice sheet (GrIS) feedback between surface mass balance (SMB: the sum of surface accumulation and surface ablation) and surface elevation in the MAR regional climate model (Edwards et al., 2014) to projections of future climate change using five ice sheet models (ISMs). The MAR (Modèle Atmosphérique Régional: Fettweis, 2007) climate projections are for 2000–2199, forced by the ECHAM5 and HadCM3 global climate models (GCMs) under the SRES A1B emissions scenario. The additional sea level contribution due to the SMB– elevation feedback averaged over five ISM projections for ECHAM5 and three for HadCM3 is 4.3% (best estimate; 95% credibility interval 1.8–6.9 %) at 2100, and 9.6% (best estimate; 95% credibility interval 3.6–16.0 %) at 2200. In all results the elevation feedback is significantly positive, amplifying the GrIS sea level contribution relative to the MAR projections in which the ice sheet topography is fixed: the lower bounds of our 95% credibility intervals (CIs) for sea level contributions are larger than the “no feedback” case for all ISMs and GCMs. Our method is novel in sea level projections because we propagate three types of modelling uncertainty – GCM and ISM structural uncertainties, and elevation feedback parameterisation uncertainty – along the causal chain, from SRES scenario to sea level, within a coherent experimental design and statistical framework. The relative contributions to uncertainty depend on the timescale of interest. At 2100, the GCM uncertainty is largest, but by 2200 both the ISM and parameterisation uncertainties are larger. We also perform a perturbed parameter ensemble with one ISM to estimate the shape of the projected sea level probability distribution; our results indicate that the probability density is slightly skewed towards higher sea level contributions.

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In the Eastern Cape Province of South Africa, chronic economic uncertainty has seen social relations stretched to breaking point. Informants speak of a 'war between men and women'. While grinding poverty, death in the shape of the 'axe' (HIV/AIDS) and suspicion stalk the land, and the project of building the umzi (homestead) falters, hope for the future and with it, trust between people, leaches away. One response to such uncertainty is a turn to ritual. Through a nearly relentless schedule of ritual activity which invokes the ancestors and the Christian deity in various forms, Xhosa people attempt to dam up trust, secure ongoing investment in the rural homestead and sustain ties of reciprocity both among rural people and between them and their urban kin. It is also through the staging of these rituals that women, acting together and in support of each other, are increasingly assertive – often in the face of a violent, rearguard opposition from men - in their efforts to exercise agency over the differentiated, fragmented and fragile social and economic relationships within their homesteads and across their villages.

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Disturbances in the regulation of reward and aversion in the brain may underlie disorders such as obesity and eating disorders. We previously showed that the cannabis receptor subtype (CB1) inverse agonist rimonabant, an antiobesity drug withdrawn due to depressogenic side effects, diminished neural reward responses yet increased aversive responses (Horder et al., 2010). Unlike rimonabant, tetrahydrocannabivarin is a neutral CB1 receptor antagonist (Pertwee, 2005) and may therefore produce different modulations of the neural reward system. We hypothesized that tetrahydrocannabivarin would, unlike rimonabant, leave intact neural reward responses but augment aversive responses. Methods: We used a within-subject, double-blind design. Twenty healthy volunteers received a single dose of tetrahydrocannabivarin (10mg) and placebo in randomized order on 2 separate occasions. We measured the neural response to rewarding (sight and/or flavor of chocolate) and aversive stimuli (picture of moldy strawberries and/or a less pleasant strawberry taste) using functional magnetic resonance imaging. Volunteers rated pleasantness, intensity, and wanting for each stimulus. Results: There were no significant differences between groups in subjective ratings. However, tetrahydrocannabivarin increased responses to chocolate stimuli in the midbrain, anterior cingulate cortex, caudate, and putamen. Tetrahydrocannabivarin also increased responses to aversive stimuli in the amygdala, insula, mid orbitofrontal cortex, caudate, and putamen. Conclusions: Our findings are the first to show that treatment with the CB1 neutral antagonist tetrahydrocannabivarin increases neural responding to rewarding and aversive stimuli. This effect profile suggests therapeutic activity in obesity, perhaps with a lowered risk of depressive side effects. Keywords: reward, THCv, obesity, fMRI, cannabinoid

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In the last decade, the growth of local, site-specific weather forecasts delivered by mobile phone or website represents arguably the fastest change in forecast consumption since the beginning of Television weather forecasts 60 years ago. In this study, a street-interception survey of 274 members of the public a clear first preference for narrow weather forecasts above traditional broad weather forecasts is shown for the first time, with a clear bias towards this preference for users under 40. The impact of this change on the understanding of forecast probability and intensity information is explored. While the correct interpretation of the statement ‘There is a 30% chance of rain tomorrow’ is still low in the cohort, in common with previous studies, a clear impact of age and educational attainment on understanding is shown, with those under 40 and educated to degree level or above more likely to correctly interpret it. The interpretation of rainfall intensity descriptors (‘Light’, ‘Moderate’, ‘Heavy’) by the cohort is shown to be significantly different to official and expert assessment of the same descriptors and to have large variance amongst the cohort. However, despite these key uncertainties, members of the cohort generally seem to make appropriate decisions about rainfall forecasts. There is some evidence that the decisions made are different depending on the communication format used, and the cohort expressed a clear preference for tabular over graphical weather forecast presentation.