Decision Analysis is a comprehensive framework for structuring uncertainties, defining dependencies and explicitly considering alternatives

## Decision Analytics vs. Monte Carlo Simulation

Critical business decisions deserve the most rigorous approach to analyzing potential risks and enumerating possible alternatives. While Monte Carlo simulation provides a quick method for assessing risk and helping the user make a go/no go decision, it does not provide a system for structuring a decision problem and mapping out alternatives.

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### The Limitations of Monte Carlo Simulation

Monte Carlo simulation is limited in that it requires a pre-specified decision rule of thumb be applied regardless of the situation. While this rule of thumb may make sense in a given scenario, there is no guarantee that it is appropriate across a range of scenarios. Monte Carlo takes a less rigorous approach than Decision Analysis which allows for a more comprehensive framework for structuring uncertainties, defining dependencies and explicitly considering alternatives in order to arrive at the most well-conceived decision.

Decision Analysis Monte Carlo
DECISIONS

Decisions are modelled explicitly (e.g., we can build, abandon, or wait). Most decisions are modelled as rules (e.g., =IF(A42>B42,C42,0).)
BOTTOM LINE: It's about the decisions.
UNCERTAINTIES

Most uncertainties are modeled with discrete outcomes. Probabilities and values are assessed. Most uncertainties are modeled with common statistical distributions. Distribution parameters are assessed.
BOTTOM LINE: Decision makers have more intuition for specific outcomes than for things like variance and skewness.
DEPENDENCIES

Dependence between uncertainties is usually handled explicitly ("if we know natural gas prices are high the likelihood changes to..."). Dependence between uncertainties is usually handled with a correlation coefficient. (In practice, it is often ignored.)
BOTTOM LINE: Even technical people have little intuition for correlation coefficients.
VALUE OF INFORMATION

The importance of an uncertainty (& the value of learning more about it) can be calculated and shown in a tree. Value of information can't be calculated directly (no explicit modelling of decisions).
BOTTOM LINE: Value of information has relevance to real world actions and can be used to determine when to move forward.
SOLUTION METHOD

Results come from a Policy Tree™ every scenario can be inspected and the Policy Tree™ provides a "strategic road map" for making decisions and managing value going forward. Results come from a number of equally likely random draws of the input distributions.
BOTTOM LINE: Portions of the tree are meaningful and can build understanding. With Monte Carlo, each draw is independent, results are only stable for a very large number of draws.
RESULTS

In addition to the Policy Tree™ the other main result is a Risk Profile showing the overall range of outcomes. Numerous sensitivity analyses are available. Pretty much just an output distribution.
BOTTOM LINE: An outcome probability distribution is usually not enough to give a decision maker confidence that a thorough analysis has been performed and a decision can be made.