Analytica (software)
Encyclopedia
Analytica is a visual software package developed by Lumina Decision Systems for creating, analyzing and communicating quantitative decision models. Analytica includes hierarchical influence diagram
s for visual creation and view of models, intelligent arrays for working with multidimensional data, and Monte Carlo simulation for analyzing risk and uncertainty. The design of Analytica, especially its influence diagrams and treatment of uncertainty, is based on ideas from the field of decision analysis
. Analytica includes a computer language, which is notable in being declarative (non-procedural) for referential transparency, supporting array abstraction, and providing automatic dependency maintenance for efficient sequencing of computation.
Analytica is widely used for policy analysis
, business modeling
, and risk analysis
, with application areas that include
energy
,
health
and pharmaceuticals
,
environmental risk and emissions policy analysis,
wildlife management
,
ecology
climate change,
technology and defense
,
strategic financial planning
,
R&D planning and portfolio management
,
financial services
,
aerospace
,
and manufacturing
.
s. 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 submodel.
Hierarchical influence diagrams in Analytica serve as a key organizational tool. People find it natural to conceptualize the structure of models both spatially and hierarchically. 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 far more information about a model's structure and organization at a glance than is possible with less visual paradigms, such as spreadsheet
s 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. The ability of a target audience to understand these aspects is critical to the modeling enterprise. 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 formulae. 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 investing in 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.
For example, while creating a model, the model builder might assume a particular variable, for example discount 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.
The ability to readily adjust dimensionality without having to perform major reorganization, recoding, or general surgery on a model is a hallmark of the Analytica modeling environment. Adjustments to dimensionality are common and natural late in the modeling process, when one is often exploring computation tradeoffs between the level of detail, computation time, available data, and overall size or dimensionality of parametric spaces. Also such adjustments are common after models have been fully constructed as a way of exploring What-If scenarios and overall relationships between variables.
The dimensions of a multidimensional array in Analytica are defined by Index objects. 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 broken down across multiple dimensions, Analytica repeats the computation, iterating over each dimension, but recognizing when the same dimension occurs in both values and treating it as the same dimension during the iteration. Unlike many programming languages, there is no inherent ordering to the dimensions in a multidimensional array—they are identified by name (or more precisely, by the index used to define the dimension). This ability to intelligently correspond and iterate over extra dimensions is termed intelligent array abstraction (a term trademarked by Lumina Decision Systems). A consequence of this facility is that duplicated formulas and explicit FOR loops, both common sources of modeling errors, are rare in Analytica models. Expressions are simplified, further increasing the accessibility, interpretability and transparency of models.
Quantities in Analytica can be specified using a distribution function
. When evaluated, distributions are sampled using either Latin hypercube or Monte Carlo sampling, and the samples are propagated through the computations to the results. The sampled result distribution can then be viewed directly, or in various statistical views such as mean
, fractile bands, PDF
, CDF
, or standard statistics, each providing a different way of visualizing the range of uncertainty in the output result.
According to Lumina Decision Systems, Analytica also supports collaborative Decision Analysis and Probability Management through the use of the DIST standard
.
Analytica has several features as a computer language designed to make it easy to use for quantitative modeling: It is a visual programming language, where users view programs (or "models") as influence diagrams, which they create and edit visually by adding and linking nodes. It is a declarative language, meaning that a model declares a definition for each variable without specifying an execution sequence as required by conventional imperative languages. Analytica determines a correct and efficient execution sequence using the dependency graph. It is a referentially transparent functional language, in that execution of functions and variables have no side effects i.e. changing other variables. As described above under "intelligent arrays, Analytica is an array programming
language, where operations and functions generalize to work on multidimensional arrays.
operating systems. Three editions (Professional, Enterprise, and Optimizer editions) each with increasing functionality and cost, are purchased by users interested in building models. A free Analytica-Player edition lets users view models, change inputs, and compute results, to enable free sharing of models for review, and a more capable but non-free Power Player enables users to save inputs and utilize database connections. The Analytica Web Player lets users access and run via a web browser. 64-bit editions are available for Enterprise, Optimizer and Power Player, which allow very memory-intensive models to be evaluated that would otherwise be too large to do in a 32-bit process.
The most recent release of Analytica is version 4.3, released 5 May 2011.
by Max Henrion as a PhD student and later professor at Carnegie Mellon University
between 1979 and 1990. Henrion founded Lumina Decision Systems in 1991 with Brian Arnold. Lumina continued to develop the software and apply it to environmental and public policy analysis applications. Lumina first released Analytica as a product in 1996.
Influence diagram
An influence diagram is a compact graphical and mathematical representation of a decision situation...
s for visual creation and view of models, intelligent arrays for working with multidimensional data, and Monte Carlo simulation for analyzing risk and uncertainty. The design of Analytica, especially its influence diagrams and treatment of uncertainty, is based on ideas from the field of decision analysis
Decision analysis
Decision analysis is the discipline comprising the philosophy, theory, methodology, and professional practice necessary to address important decisions in a formal manner...
. Analytica includes a computer language, which is notable in being declarative (non-procedural) for referential transparency, supporting array abstraction, and providing automatic dependency maintenance for efficient sequencing of computation.
Analytica is widely used for policy analysis
Policy analysis
Policy analysis is "determining which of various alternative policies will most achieve a given set of goals in light of the relations between the policies and the goals". However, policy analysis can be divided into two major fields. Analysis of policy is analytical and descriptive—i.e., it...
, business modeling
Business Analysis
Business analysis is the discipline of identifying business needs and determining solutions to business problems. Solutions often include a systems development component, but may also consist of process improvement, organizational change or strategic planning and policy development...
, and risk analysis
Risk analysis (Business)
Risk analysis is a technique to identify and assess factors that may jeopardize the success of a project or achieving a goal.This technique also helps to define preventive measures to reduce the probability of these factors from occurring and identify countermeasures to successfully deal with these...
, with application areas that include
energy
,
health
Health care
Health care is the diagnosis, treatment, and prevention of disease, illness, injury, and other physical and mental impairments in humans. Health care is delivered by practitioners in medicine, chiropractic, dentistry, nursing, pharmacy, allied health, and other care providers...
and pharmaceuticals
,
environmental risk and emissions policy analysis,
wildlife management
Wildlife management
Wildlife management attempts to balance the needs of wildlife with the needs of people using the best available science. Wildlife management can include game keeping, wildlife conservation and pest control...
,
ecology
climate change,
technology and defense
,
strategic financial planning
,
R&D planning and portfolio management
,
financial services
Financial services
Financial services refer to services provided by the finance industry. The finance industry encompasses a broad range of organizations that deal with the management of money. Among these organizations are credit unions, banks, credit card companies, insurance companies, consumer finance companies,...
,
aerospace
Aerospace
Aerospace comprises the atmosphere of Earth and surrounding space. Typically the term is used to refer to the industry that researches, designs, manufactures, operates, and maintains vehicles moving through air and space...
,
and manufacturing
.
Hierarchical influence diagrams
Analytica models are organized as influence diagramInfluence diagram
An influence diagram is a compact graphical and mathematical representation of a decision situation...
s. 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 submodel.
Hierarchical influence diagrams in Analytica serve as a key organizational tool. People find it natural to conceptualize the structure of models both spatially and hierarchically. 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 far more information about a model's structure and organization at a glance than is possible with less visual paradigms, such as spreadsheet
Spreadsheet
A spreadsheet is a computer application that simulates a paper accounting worksheet. It displays multiple cells usually in a two-dimensional matrix or grid consisting of rows and columns. Each cell contains alphanumeric text, numeric values or formulas...
s 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. The ability of a target audience to understand these aspects is critical to the modeling enterprise. 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 formulae. 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 investing in 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.
Intelligent multidimensional arrays
The Analytica engine represents and operates upon multidimensional arrays. For an Analytica modeler, these multiple dimensions provide yet an additional source of abstraction, as well as a source of flexibility. New dimensions can often be introduced or removed easily 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 discount rate
Discount rate
The discount rate can mean*an interest rate a central bank charges depository institutions that borrow reserves from it, for example for the use of the Federal Reserve's discount window....
, 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
Discount rate
The discount rate can mean*an interest rate a central bank charges depository institutions that borrow reserves from it, for example for the use of the Federal Reserve's discount window....
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
Net present value
In finance, the net present value or net present worth of a time series of cash flows, both incoming and outgoing, is defined as the sum of the present values of the individual cash flows of the same entity...
will become multidimensional and contain these new dimensions. In essence, Analytica repeats the same calculation using the discount rate
Discount rate
The discount rate can mean*an interest rate a central bank charges depository institutions that borrow reserves from it, for example for the use of the Federal Reserve's discount window....
for each possible combination of Country and Economic_scenario.
The ability to readily adjust dimensionality without having to perform major reorganization, recoding, or general surgery on a model is a hallmark of the Analytica modeling environment. Adjustments to dimensionality are common and natural late in the modeling process, when one is often exploring computation tradeoffs between the level of detail, computation time, available data, and overall size or dimensionality of parametric spaces. Also such adjustments are common after models have been fully constructed as a way of exploring What-If scenarios and overall relationships between variables.
The dimensions of a multidimensional array in Analytica are defined by Index objects. An index object has a name and a list of elements. When two multidimensional values are combined, for example in an expression such as
Revenue - Expenses
where Revenue and Expenses are each broken down across multiple dimensions, Analytica repeats the computation, iterating over each dimension, but recognizing when the same dimension occurs in both values and treating it as the same dimension during the iteration. Unlike many programming languages, there is no inherent ordering to the dimensions in a multidimensional array—they are identified by name (or more precisely, by the index used to define the dimension). This ability to intelligently correspond and iterate over extra dimensions is termed intelligent array abstraction (a term trademarked by Lumina Decision Systems). A consequence of this facility is that duplicated formulas and explicit FOR loops, both common sources of modeling errors, are rare in Analytica models. Expressions are simplified, further increasing the accessibility, interpretability and transparency of models.
Uncertainty analysis
In any modeling project, many input quantities can only be estimated, and thus have an inherent degree of uncertainty. Models that explicitly specify the range of uncertainty in their inputs often provide much more realistic and informative projections.Quantities in Analytica can be specified using a distribution function
Probability distribution
In probability theory, a probability mass, probability density, or probability distribution is a function that describes the probability of a random variable taking certain values....
. When evaluated, distributions are sampled using either Latin hypercube or Monte Carlo sampling, and the samples are propagated through the computations to the results. The sampled result distribution can then be viewed directly, or in various statistical views such as mean
Mean
In statistics, mean has two related meanings:* the arithmetic mean .* the expected value of a random variable, which is also called the population mean....
, fractile bands, PDF
Probability density function
In probability theory, a probability density function , or density of a continuous random variable is a function that describes the relative likelihood for this random variable to occur at a given point. The probability for the random variable to fall within a particular region is given by the...
, CDF
Cumulative distribution function
In probability theory and statistics, the cumulative distribution function , or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found at a value less than or equal to x. Intuitively, it is the "area so far"...
, or standard statistics, each providing a different way of visualizing the range of uncertainty in the output result.
According to Lumina Decision Systems, Analytica also supports collaborative Decision Analysis and Probability Management through the use of the DIST standard
.
Analytica as a computer language
Analytica includes a general language of operators and functions for expressing mathematical relationships among variables. Users can define functions and libraries to extend the language.Analytica has several features as a computer language designed to make it easy to use for quantitative modeling: It is a visual programming language, where users view programs (or "models") as influence diagrams, which they create and edit visually by adding and linking nodes. It is a declarative language, meaning that a model declares a definition for each variable without specifying an execution sequence as required by conventional imperative languages. Analytica determines a correct and efficient execution sequence using the dependency graph. It is a referentially transparent functional language, in that execution of functions and variables have no side effects i.e. changing other variables. As described above under "intelligent arrays, Analytica is an array programming
Array programming
In computer science, array programming languages generalize operations on scalars to apply transparently to vectors, matrices, and higher dimensional arrays....
language, where operations and functions generalize to work on multidimensional arrays.
Editions
The Analytica software runs on Microsoft WindowsMicrosoft Windows
Microsoft Windows is a series of operating systems produced by Microsoft.Microsoft introduced an operating environment named Windows on November 20, 1985 as an add-on to MS-DOS in response to the growing interest in graphical user interfaces . Microsoft Windows came to dominate the world's personal...
operating systems. Three editions (Professional, Enterprise, and Optimizer editions) each with increasing functionality and cost, are purchased by users interested in building models. A free Analytica-Player edition lets users view models, change inputs, and compute results, to enable free sharing of models for review, and a more capable but non-free Power Player enables users to save inputs and utilize database connections. The Analytica Web Player lets users access and run via a web browser. 64-bit editions are available for Enterprise, Optimizer and Power Player, which allow very memory-intensive models to be evaluated that would otherwise be too large to do in a 32-bit process.
The most recent release of Analytica is version 4.3, released 5 May 2011.
History
Analytica's predecessor, called Demos, grew from the research on tools for policy analysisPolicy analysis
Policy analysis is "determining which of various alternative policies will most achieve a given set of goals in light of the relations between the policies and the goals". However, policy analysis can be divided into two major fields. Analysis of policy is analytical and descriptive—i.e., it...
by Max Henrion as a PhD student and later professor at Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University is a private research university in Pittsburgh, Pennsylvania, United States....
between 1979 and 1990. Henrion founded Lumina Decision Systems in 1991 with Brian Arnold. Lumina continued to develop the software and apply it to environmental and public policy analysis applications. Lumina first released Analytica as a product in 1996.