67
edits
Changes
Created page with "left|thumb === Introduction & Motivation === When conducting a Cost Benefit Analysis (CBA) the assessment model is often based on assumpt..."
[[File:Copilot 20250707 152121.png|left|thumb]]
=== Introduction & Motivation ===
When conducting a Cost Benefit Analysis (CBA) the assessment model is often based on assumptions or parameters that are uncertain. Knowledge of how uncertainties impact outcomes provide important information that deepens our understanding beyond just the most likely outcome. In the case of only one uncertain parameter, it is a relatively simply process to determine how alternative possibilities affect the outcome, however as the number of uncertain/risky parameters increases the range of possible outcomes that may result from a particular combination increases to a magnified level.
When outcomes are not discrete (one possible outcome among a limited set of possibilities) but continuous (one possible outcome among unlimited possibilities with or without a range) the problem becomes even more complex. As most parameters are of a continuous nature a more robust method than traditional sensitivity analysis is needed.
In situations such as this model inputs may be best defined as probability distributions rather than an individual number. When more than one probability distribution is used in repeated simulations to calculate an objective outcome this is referred to as a Monte-Carlo experiment. This works by repeatedly and randomly selecting outcomes from the distributions of the various inputs and applying them to the model. Each time this is done, a possible outcome of the model is created. The more times this process is repeated the more observations from the total set of possible outcomes are calculated, until eventually enough data is collected to piece together a probability-distribution of the model output.
=== Introduction & Motivation ===
When conducting a Cost Benefit Analysis (CBA) the assessment model is often based on assumptions or parameters that are uncertain. Knowledge of how uncertainties impact outcomes provide important information that deepens our understanding beyond just the most likely outcome. In the case of only one uncertain parameter, it is a relatively simply process to determine how alternative possibilities affect the outcome, however as the number of uncertain/risky parameters increases the range of possible outcomes that may result from a particular combination increases to a magnified level.
When outcomes are not discrete (one possible outcome among a limited set of possibilities) but continuous (one possible outcome among unlimited possibilities with or without a range) the problem becomes even more complex. As most parameters are of a continuous nature a more robust method than traditional sensitivity analysis is needed.
In situations such as this model inputs may be best defined as probability distributions rather than an individual number. When more than one probability distribution is used in repeated simulations to calculate an objective outcome this is referred to as a Monte-Carlo experiment. This works by repeatedly and randomly selecting outcomes from the distributions of the various inputs and applying them to the model. Each time this is done, a possible outcome of the model is created. The more times this process is repeated the more observations from the total set of possible outcomes are calculated, until eventually enough data is collected to piece together a probability-distribution of the model output.