Problems with data
 Missing components or errors in the data
 “Noise” in the data associated with biased or incomplete
observations
 Random sampling error and biases (nonrepresentativeness) in a
sample
Problems with models
 Known processes but unknown functional relationships or errors in
the structure of the model
 Known structure but unknown or erroneous values of some important
parameters
 Known historical data and model structure, but reasons to believe
parameters or model structure will change over time
 Uncertainty regarding the predictability (e.g., chaotic or stochastic
behavior) of the system or effect
 Uncertainties introduced by approximation techniques used to solve
a set of equations that characterize the model.
Other sources of uncertainty
 Ambiguously defined concepts and terminology
 Inappropriate spatial/temporal units
 Inappropriateness of/lack of confidence in underlying assumptions
 Uncertainty due to projections of human behavior (e.g., future
consumption patterns, or technological change), which is distinct
from uncertainty due to “natural” sources (e.g., climate
sensitivity, chaos)
