# 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.

#### 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.

*Below are some key reasons why a Decision Analytic Approach holds the advantage over Monte Carlo:*

__DECISIONS__

**Decision Analysis**: Decisions are modelled explicitly (e.g., we can build, abandon, or wait).

**Monte Carlo**: Most decisions are modelled as rules (e.g., =IF(A42>B42,C42,0)).

**BOTTOM LINE: It's about the decisions.**

__UNCERTAINTIES__

**Decision Analysis**: Most uncertainties are modeled with discrete outcomes. Probabilities and values are assessed.

**Monte Carlo**: 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.**

__DEPENDANCIES__

**Decision Analysis**: Dependence between uncertainties is usually handled explicitly ("if we know natural gas prices are high the likelihood changes to...").

**Monte Carlo**: 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__

**Decision Analysis**: The importance of an uncertainty (& the value of learning more about it) can be calculated and shown in a tree.

**Monte Carlo**: 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__

**Decision Analysis**: Results come from a decision policy tree, whose paths are specific scenarios that can be inspected.

**Monte Carlo**: 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 result are only stable for a very large number of draws.**

__RESULTS__

**Decision Analysis**: A key result is a Policy Treeā¢ both part of the solution method and a "strategic road map" for making decisions and managing value going forward. The other main result is a Risk Profile showing the overall range of outcomes.

**Monte Carlo**: 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.**