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 (non-representativeness) 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)

Box — Examples of Sources of Uncertainty (from Moss and Schneider, 2000).