The Decision Analysis Advantage

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:

DPL Decision Node in Decision Tree


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.

DPL Uncertainty Node in Decision Tree


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.

DPL Conditioning Arc


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.

DPL Output - Value of Information and Control


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.

DPL Output - Policy Tree


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 are only stable for a very large number of draws.

DPL Output - Risk Profiles


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.

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