Analytica (software)

Analytica is a visual software developed by Lumina Decision Systems for creating, analyzing and communicating quantitative decision models. It combines hierarchical influence diagrams for visual creation and view of models, intelligent arrays for working with multidimensional data, Monte Carlo simulation for analyzing risk and uncertainty, and optimization, including linear and nonlinear programming. Its design is based on ideas from the field of decision analysis. As a computer language, it combines a declarative (non-procedural) structure for referential transparency, array abstraction, and automatic dependency maintenance for efficient sequencing of computation.

Analytica models are organized as influence diagrams. Variables (and other objects) appear as nodes of various shapes on a diagram, connected by arrows that provide a visual representation of dependencies. Analytica influence diagrams may be hierarchical, in which a single module node on a diagram represents an entire sub-model.

Hierarchical influence diagrams in Analytica serve as an organizational tool. Because the visual layout of an influence diagram matches these natural human abilities both spatially and in the level of abstraction, people are able to take in more information about a model's structure and organization at a glance than is possible with less visual paradigms, such as Spreadsheets and Mathematical expressions. Managing the structure and organization of a large model can be a significant part of the modeling process, but is substantially aided by the visualization of influence diagrams.

Influence diagrams also serve as a tool for communication. Once a quantitative model has been created and its final results computed, it is often the case that an understanding of how the results are obtained, and how various assumptions impact the results, is far more important than the specific numbers computed. Analytica gives users the ability to help target audiences understand these aspects within their models. The visual representation of an influence diagram quickly communicates an understanding at a level of abstraction that is normally more appropriate than detailed representations such as mathematical expressions or cell formulas. When more detail is desired, users can drill down to increasing levels of detail, speeded by the visual depiction of the model's structure.

The existence of an easily understandable and transparent model supports communication and debate within an organization, and this effect is one of the primary benefits of quantitative model building. When all interested parties are able to understand a common model structure, debates and discussions will often focus more directly on specific assumptions, can cut down on "cross-talk", and therefore lead to more productive interactions within the organization. The influence diagram serves as a graphical representation that can help to make models accessible to people at different levels.

Analytica uses index objects to track the dimensions of multidimensional arrays. An index object has a name and a list of elements. When two multidimensional values are combined, for example in an expression such as

where Revenue and Expenses are each multidimensional, Analytica repeats the profit calculation over each dimension, but recognizes when same dimension occurs in both values and treats it as the same dimension during the calculation, in a process called intelligent array abstraction. Unlike most programming languages, there is no inherent ordering to the dimensions in a multidimensional array. This avoids duplicated formulas and explicit FOR loops, both common sources of modeling errors. The simplified expressions made possible by intelligent array abstraction allow the model to be more accessible, interpretable, and transparent.

Another consequence of intelligent array abstraction is that new dimensions can be introduced or removed from an existing model, without requiring changes to the model structure or changes to variable definitions. For example, while creating a model, the model builder might assume a particular variable, for example Discounted rate, contains a single number. Later, after constructing a model, a user might replace the single number with a table of numbers, perhaps Discount rate broken down by Country and by Economic scenario. These new divisions may reflect the fact that the effective discount rate is not the same for international divisions of a company, and that different rates are applicable to different hypothetical scenarios. Analytica automatically propagates these new dimensions to any results that depend upon Discount rate, so for example, the result for Net present value will become multidimensional and contain these new dimensions. In essence, Analytica repeats the same calculation using the discount rate for each possible combination of Country and Economic scenario.