Using Simulation Systems for Decision Support

Using Simulation Systems for Decision Support

Andreas Tolk (Old Dominion University, USA)
DOI: 10.4018/978-1-60566-774-4.ch014
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Abstract

This chapter describes the use of simulation systems for decision support in support of real operations, which is the most challenging application domain in the discipline of modeling and simulation. To this end, the systems must be integrated as services into the operational infrastructure. To support discovery, selection, and composition of services, they need to be annotated regarding technical, syntactic, semantic, pragmatic, dynamic, and conceptual categories. The systems themselves must be complete and validated. The data must be obtainable, preferably via common protocols shared with the operational infrastructure. Agents and automated forces must produce situation adequate behavior. If these requirements for simulation systems and their annotations are fulfilled, decision support simulation can contribute significantly to the situational awareness up to cognitive levels of the decision maker.
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Introduction

Modeling and simulation (M&S) systems are applied in various domains, such as

  • supporting the analysis of alternatives,

  • supporting the procurement of new systems by simulating them long before first prototypes are available,

  • supporting the testing and evaluation of new equipment by providing the necessary stimuli for the system being tested,

  • training of new personnel working with the system,

and many more.

The topic of this chapter is one of the most challenging applications for simulation systems, namely the use of simulation systems for decision support in general, and particularly in direct support of operational processes. In other words, the decision maker is directly supported by M&S applications, helping with

  • “what-if” analysis for alternatives,

  • plausibility evaluation for assumptions of other party activities,

  • consistency checks of plans for future operations,

  • simulation of expected behavior based on the plan and trigger the real world observations for continuous comparison (are we still on track),

  • manage uncertainty by simulating several runs faster than real time and display variances and connected risks,

  • trend simulation to identify potentially interesting developments in the future based on current operational developments,

and additional applications that support the meaningful interpretation of current data.

While current decision support systems are focused on data mining and data presentation, which is the display of snap-shot information and historical developments are captured in most cases in the form of static trend analyses and display curves (creating a common operating picture), simulation systems display the behavior of the observed system (creating a common executable model). This model can be used by the decision maker to manipulate the observed system “on the fly” and use it not only for analysis, but also to communicate the results very effectively to and with partners, customers, and supporters of his efforts. As stated by van Dam (1999) during his lecture at Stanford: “If a picture is worth a 1000 words, a moving picture is worth a 1000 static ones, and a truly interactive, user-controlled dynamic picture is worth 1000 ones that you watch passively.” That makes simulation very interesting for managers and decision makers, encouraging the use of decision support simulation systems. Another aspect is that of complex systems: non-linearity and multiple connections. In order to understand and evaluate such system, traditional tools of operational research and mathematics have to be increasingly supported by the means of modeling and simulation. The same is true for decisions in complex environments, such as the battlefield of a military decision maker or the stock market for an international investment broker.

To this end, the simulation system must be integrated into operational systems as a decision support service. In order to be successful, not only the technical challenges of integration, discrete and other simulation technologies, into operational IT systems must be solved. It is also required that the simulation system fulfills additional operational and conceptual requirements as well. Simulation systems are more than software. Simulation systems are executable models, and models are purposeful abstractions of reality. In order to understand if a simulation system can be used for decision support, the concepts and assumptions derived to represent real world objects and effects in a simplified form must be understood. The conceptualization of the model’s artifacts is as important as the implementation details of the simulation. As stated in Tolk (2006): interoperability of systems requires composability of models!

Key Terms in this Chapter

Integratability: contends with the physical/technical realms of connections between systems, which include hardware and firmware, protocols, networks, etc. If two systems can exchange physical data with each other in a way that the target system receives and decoded the submitted data from the sending system the two systems are integrated.

Model-based Data Engineering: is the process of applying documented and repeatable engineering methods for data administration – i.e. managing the information exchange needs including source, format, context of validity, fidelity, and credibility –, data management – i.e. planning, organizing and managing of data, including defining and standardizing the meaning of data and of their relations -, data alignment – i.e. ensuring that data to be exchanged exist in all participating systems, focusing a data provider /data consumer relations -, and data transformation – i.e. the technical process of mapping different representations of the same data elements to each other – supported by a common reference model.

Decision Support Systems: are information systems supporting operational (business and organizational) decision-making activities of a human decision maker. The DSS shall help decision makers to compile useful information from raw data and documents that are distributed in a potentially heterogeneous IT infrastructure, personal or educational knowledge that can be static or procedural, and business models and strategies to identify and solve problems and make decisions.

Decision Support Simulation Systems: are simulation systems supporting operational (business and organizational) decision-making activities of a human decision maker by means of modeling and simulation. They use decision support system means to obtain, display and evaluate operationally relevant data in agile contexts by executing models using operational data exploiting the full potential of M&S and producing numerical insight into the behavior of complex systems.

Validation and Verification: are processes to determine the simulation credibility. Validation is the process of determining the degree to which a model or simulation is an accurate representation of the real world from the perspective of the intended uses. Validation determines the behavioral and representational accuracy. Verification is the process of determining that a model or simulation implementation accurately represents the developer’s conceptual description and specifications. Verification determines the accuracy of transformation processes.

Conceptual Modeling: is the process of defining a non-software specific formal specification of a conceptualization building the basis for the implementation of a simulation system (or another model-based implementation) describing the objectives, inputs, outputs, content, assumptions, and simplifications of the model. The conceptual model conceptual model is a bridge between the real world observations and the high-level implementation artifacts.

Composability: contends with the alignment of issues on the modeling level. The underlying models are purposeful abstractions of reality used for the conceptualization being implemented by the resulting systems. If two systems are interoperable and share assumptions and constraints in a way that the axioms of the receiving system are not violated by the sending system, the systems are composable.

Interoperability: contends with the software and implementation details of interoperations; this includes exchange of data elements via interfaces, the use of middleware, and mapping to common information exchange models. If two systems are integrated and the receiving system can not only decode but understand the data in a way that is meaningful to the receiving system, the systems are interoperable.

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