Mitigate Risks

Decision Trees

Gain deeper insight into your complex decision by visually mapping out all possible routes with probabilistic decision trees. By modeling the life of the project, you can account for the possible consequences of future decisions and gain realistic and useful decision modeling analytics.
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Gain Probabilistic Insights in Your Decision Analysis

In a complex world, the decisions we face are often more sophisticated than “pick X or Y.” Previous decisions and uncertainty shape the choices yet to come. Each specific decision path we could take leads to unique situations, yet we must make choices now prior to knowing precisely where we will find ourselves in time, down the road. Which decisions will lead to the best returns, or lowest costs?

Decision trees allow us to visually model these chronological layers of decisions and random events to determine the best business policy under uncertainty. They are well-suited in the analysis of multi-stage decisions over time, where values at each stage are uncertain.

What are Decision Trees?

The Monte Carlo method is a computerized mathematical technique that allows people to quantitatively account for risk in forecasting and decision-making. At its core, the Monte Carlo method is a way to use random samples of parameters to explore the behavior of a complex system. A Monte Carlo simulation is used to handle an extensive range of problems in a variety of different fields to understand the impact of risk and uncertainty.

Visual Decision Modeling Across Industries

Decision trees are a model type that accounts for the conditional nature of future decisions, giving realistic and useful decision modeling analytics. The technique is used in construction & engineering, energy & utilities, mining & minerals, logistics & transportation, consulting & legal, healthcare & pharmaceuticals, and many other disciplines. Use cases include:

Prescriptive Decision Suggestions

Decision trees give a decision maker an overall policy suggestion to utilize throughout the life of the project, as well as a probabilistic comparison of the risk profile of different potential sets of decisions. There is also great value in being able to visualize large, complex decisions and the various stages of decision-making, which decision trees do exceptionally well. The visual nature of decision tree diagrams makes them well-suited to discussion, problem-solving, and communication to others.

Use cases are varied, and include:
Resource extraction strategies
Bidding decisions
Build vs. buy strategies
Litigation planning
Treatment planning
Negotiation strategies and more

A Range of Outcomes (one col list)

Monte Carlo simulation furnishes the decision-maker 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

History of Monte Carlo Simulation

The technique was first used by scientists working on the atom bomb; it was named for Monte Carlo, the Monaco resort town renowned for its casinos. Since its introduction in World War II, Monte Carlo simulation has been used to model a variety of physical and conceptual systems.

Random Sampling Versus Best Guess

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.

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’s easy to create graphs of different outcomes and their chances of occurrence. This is important for communicating findings to other stakeholders.
Sensitivity Analysis. Deterministic analysis makes it difficult to see which variables impact the outcome the most. In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results. This allows you to identify and mitigate factors which cause the most risk.
Scenario Analysis: In deterministic models, it’s 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’s possible to model interdependent relationships between input variables. It’s important for accuracy to represent how, in reality, when some factors go up or down, others go up or down accordingly.
An enhancement to Monte Carlo simulation is the use of Latin Hypercube sampling, which samples more accurately from the full range of values within distribution functions and produces results more quickly.

Enterprise Licensing
Better Research, Insights, and Outcomes for All

Whether your organization’s focus is qualitative, quantitative, or mixed methods data analysis, we can help your whole team work better together — collaborating to aggregate, organize, analyze, and present your findings. Lumivero’s enterprise licensing options offer volume pricing for teams and organizations needing nine (9) or more licenses.

Enterprise licenses allow the flexibility to install Lumivero software and solutions on multiple computers (up to the maximum number of licenses that your site has purchased) with a centralized management solution.

Lumivero’s team-based solutions allow you to:

Stay up-to-date with free upgrades to the latest releases
Reduce IT costs with one platform deployed across your organization
Reassign licenses to different users as teams evolve
Centralize license and subscription management in one place
Streamline budget allocation, especially for smaller groups and consultancy firms
Enjoy a Dedicated Customer Success Manager and pro-rated rates for new users

Decision Tree Software from Lumivero

Lumivero’s PrecisionTree software puts powerful decision tree analysis at the fingertips of any Excel user. PrecisionTree makes it easy to create, analyze, and share trees with others, all from the familiar Excel environment. Furthermore, PrecisionTree is designed to work with Palisade’s @RISK and TopRank products, which add Monte Carlo simulation and sensitivity analysis to decision tree models. These combined analyses allow the creation of the most accurate tree models anywhere, while maintaining point-and-click ease-of-use.
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