Monte Carlo Simulation Provides Insights to Manage Risks

Randy Heffernan


May 28, 2021

Two people examining graphs on paper, computer and tablet.

Risk analysis is part of every decision we make. We are constantly faced with uncertainty, ambiguity, and variability, and even though we have unprecedented access to information, we cannot accurately predict the future. Monte Carlo simulation lets you see all possible outcomes of decisions and assess the impact of risk, allowing for better decision-making under uncertainty.

Monte Carlo simulation is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision-making. The technique was first used by scientists working on the atom bomb, and was named for Monte Carlo, the Monaco resort town renowned for its casinos.

In scientific and technical fields, Monte Carlo simulation is used in many processes ranging from computational physics to aerodynamic design and models for weather forecasting. In the field of engineering, uses range from microelectronics to the design of wireless networks, autonomous robotics and artificial intelligence systems. Environmental firms use simulation for cleanup, preservation, and wildlife protection efforts. Monte Carlo simulations play a role in the creation of photo-realistic images of virtual 3D models with applications in video games, architecture, and cinematic special effects. The U.S. Coast Guard uses Monte Carlo simulations to calculate the probable locations of victims during search and rescue operations.

Monte Carlo simulation furnishes decision-makers with a range of possible outcomes and the probabilities they will occur for any choice of action. It shows the extreme possibilities—the outcomes of going for broke and for the most conservative decision—along with all possible consequences for middle-of-the-road decisions.

How Monte Carlo Simulation Works

Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions. Depending upon the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. Monte Carlo simulation produces distributions of possible outcome values.

By using probability distributions, variables can have different probabilities of different outcomes occurring. Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis. 

Common probability distributions include:

  • Normal: Also known as “bell curve,” the user simply defines the mean or expected value and a standard deviation to describe the variation about the mean. Values in the middle near the mean are most likely to occur. It is symmetrical and describes many natural phenomena such as people’s heights. Examples of variables that normal distributions describe include inflation rates and energy prices.
  • Lognormal: Values are positively skewed, not symmetric like a normal distribution. It is used to represent values that do not go below zero but have unlimited positive potential. Examples of variables described by lognormal distributions include real estate property values, stock prices, and oil reserves.
  • Uniform: All values have an equal chance of occurring, and the user simply defines the minimum and maximum. Examples of variables that could be uniformly distributed include manufacturing costs or future sales revenues for a new product.
  • Triangular: The user defines the minimum, most likely, and maximum values. Variables that could be described by a triangular distribution include past sales history per unit of time and inventory levels. 
  • PERT: The user defines the minimum, most likely, and maximum values, just like the triangular distribution. Values around the most likely are more likely to occur. However values between the most likely and extremes are more likely to occur than the triangular; that is, the extremes are not as emphasized. An example of the use of a PERT distribution is to describe the duration of a task in a project management model.
  • Discrete: The user defines specific values that may occur and the likelihood of each. An example might be the results of a lawsuit: 20% chance of positive verdict, 30% change of negative verdict, 40% chance of settlement, and 10% chance of mistrial.

During a Monte Carlo simulation, values are sampled at random from the input probability distributions. Each set of samples is called an iteration, and the resulting outcome from that sample is recorded. Monte Carlo simulation does this hundreds or thousands of times, and the result is a probability distribution of possible outcomes. In this way, Monte Carlo simulation provides a much more comprehensive view of what may happen. It tells you not only what could happen, but how likely it is to happen.

Advantages Compared to Deterministic “Single-Point” Estimates

Monte Carlo simulation provides a number of advantages over deterministic, or “single-point estimate” analysis:

  • Probabilistic Results: Results show not only what could happen, but how likely each outcome is.
  • Graphical Results: Because of the data a Monte Carlo simulation generates, it is easy to create graphs of different outcomes and their chances of occurrence. This is important for communicating findings to other stakeholders.
  • Sensitivity Analysis: With just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most. In Monte Carlo simulation, it is easy to see which inputs had the biggest effect on bottom-line results.
  • Scenario Analysis: In deterministic models, it is very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios. Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. This is invaluable for pursuing further analysis.
  • Correlation of Inputs: In Monte Carlo simulation, it is possible to model interdependent relationships between input variables. It is important for accuracy to represent how, in reality, when some factors goes up, others go up or down accordingly.

Turning risk into opportunity relies on using analytics and data, identifying factors driving risk, and simulating many factors to shape strategy. Monte Carlo simulation can fulfill a key role in increasing the quality of decision-making and helping project teams think clearly, act decisively and feel confident. Predictive analytics help organizations navigate uncertainty, save lives, avoid surprises, make better decisions, and create market advantages that unveil new opportunities. 

Randy Heffernan is chief executive officer of Palisade Company which provides risk and decision analysis software solutions globally for industry and science, including @RISK and the DecisionTools Suite.