The portion of risk that cannot be measured or quantified. FX SignificanceWhen...
All event s, both positive and negative whose probabilities are neither 0% nor 100%. Uncertainty is a distinct characteristic of the project environment. [D02229] RMW Lack of knowledge of future event s. See also Project Risk. [D02094] PMK87 The possibility that event s may occur which will impact the project either favorably or unfavorably. Uncertainty gives rise to both opportunity and risk. See also ( project) risk. & [D02095] FWH RMH A condition, event, outcome, or circumstance for which the extent, value, or consequence is not predictable. [D03704] DSMC source of risk derived from a lack of sufficient knowledge about the underlying probabilities of adverse event s and/or their consequences. [D05138] RAMP
The lack of precise knowledge based on the amount and quality of evidence or data available.
(uncertainty analysis) During risk assessments, the need to make assumptions or best judgments in the absence of precise scientific data creates uncertainties. Uncertainties attempt to define the usefulness of the conclusion in making a decision. These uncertainties are sometimes expressed qualitatively and sometimes quantitatively. For example, a risk may be determined to be 8 in 100,000, plus or minus 1; meaning that it is between 7 and 9 in 100,000. return to: [] [ Click "BackButton" for previous location
An expression of the lack of knowledge, usually given as a range or group of plausible alternatives.
Failure to know anything that may be relevant for an economic decision, such as future variables, details of a technology, or sales. In models, uncertainty usually appears as a random variable and corresponding probability density function. But in practice, most international models, especially of trade, assume certainty.
A situation in which a decision maker has neither certainty nor reasonable probability estimates available.
Uncertainty: reference to the lack of knowledge (ignorance) about the state of the environment or other decision variables. Uncertainty about the future vs. uncertainty about other places, events, structures and other phenomena Risk: reference to the consequences of uncertainty, such as regret often expressed in terms of probabilities of such consequences uncertainty then reduced to uncertainty about actual occurrence and, possibly, about the reliability of probabilities. it shall be suggested that "complexity" (at a point in time) and "instability" (over time) are two different root causes of uncertainty and (following Lawrence & Lorsch) that these two attributes of decision environments lead to poor quality of information, leads & lags in the economy resulting in delays in in formation feedback, and a lack of causal understanding and thereby contribute to the individual's uncertainty and can be identified as more immediate "causes" for uncertainty. [Otherwise: see Goodall
Uncertainty is a prominent feature of the benefits and costs of climate change. Decision makers need to compare risk of premature or unnecessary actions with risk of failing to take actions that subsequently prove to be warranted. This is complicated by potential irreversibilities in climate impacts and long term investments.
Lack of knowledge regarding the true value of a quantity, such as a specific characteristic (e.g. mean, variance) of a distribution for variability, or regarding the appropriate and adequate inference options to use to structure a model or scenario. These are also referred to as model uncertainty and scenario uncertainty. Lack of knowledge uncertainty can be reduced by obtaining more information through research and data collection, such as through research on mechanisms, larger sample sizes or more representative samples.
MS = The condition in which reasonable knowledge regarding risks, benefits, or the future is not available. UI = D035501
Usually refers to risk or volatility.
Uncertainty is defined as A potential deficiency in any phase or activity of the modeling process that is due to the lack of knowledge." (AIAA G-077-1998) Further discussion of uncertainty can be found at the page entitled Uncertainty and Error in CFD Simulations.
also known as risk or alea; any contract in which the availability of goods promised cannot be guaranteed is invalidated through this element of risk.
Estimates of future performance always entail uncertainty, the inverse of confidence. It is useful to quantify the uncertainty to perform Sensitivity Analysis, demonstrating the possible impacts of different levels of performance. See Sensitivity Analysis.
Uncertainty occurs because of a lack of knowledge. It is not the same as variability. For example, a risk assessor may be very certain that different people drink different amounts of water but may be uncertain about how much variability there is in water intakes within the population. Uncertainty can often be reduced by collecting more and better data, whereas variability is an inherent property of the population being evaluated. Variability can be better characterized with more data but it cannot be reduced or eliminated. Efforts to clearly distinguish between variability and uncertainty are important for both risk assessment and risk characterization.
If outcomes will occur with a probability that cannot even be estimated, the decisionmaker faces uncertainty. Contrast risk. This meaning to uncertainty is attributed to Frank Knight, and is sometimes referred to as Knightian uncertainty. The decisionmaker can apply game theory even in such a circumstance, e.g. the choice of a dominant strategy. Kreps (1988), p 31, writes that three standard ways of modeling choices made under conditions of uncertainty are with von Neumann-Morgenstern expected utility over objective uncertainty, the Savage axioms for modeling subjective uncertainty, and the Anscombe-Aumann theory which is a middle course between them. A recent ad for a new book edited by Haim Levy ( Stochastic Dominance: Investment Decision Making under Uncertainty) considers three ways of modeling investment choices under uncertainty: by tradeoffs between mean and variance, by choices made by stochastic dominance, and non-expected utility approaches using prospect theory. Source: econterms