Guidelines for successful simulation studies

Balcı, Osman

doi:10.1109/wsc.1990.129482

Cited by 84 publications

(96 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A knowledge related to the system to be simulated as a whole. It concerns aspects of the system that are not directly related to the individual entities and it helps to build the socalled environment model 3 . A knowledge relating to specific aspects of single entities that will be suited for building the agent models.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Common and Domain-specific Metamodel Elements for Problem Description in Simulation Problems

Ribino¹,

Seidita²,

Lodato³

et al. 2014

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-It is well known that the multi-agent system paradigm is well suited for modelling and developing simulations of complex systems belonging to several application domains. Simulation study aims at developing simulation models useful for representing, studying and analyzing entities and their behavior in a system according to specific purposes. With our work we are trying to understand what are the right elements to be considered and included in the description of a simulation problem. In order to root our resulting metamodel in the state of the art of multi-agent simulations we started from the study of twelve papers dealing with four different application domains: Crowd Dynamics, Traffic and Transportation, Electricity Power Engineering and Supply Chain and Logistic. From this study we obtained a metamodel that may be used by an analyst as a guideline and concept repository for facing a new system design. The metamodel is the result of a well defined approach that is described together with the obtained results consisting in one core metamodel containing elements that are common to all the four application domains and some domain extension contents. These latter contain the elements that are specific of each of the studied domains and are not present in the others.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In [3] and [4] the life cycle of a simulation study is illustrated, it prescribes ten phases organized in a quite iterative way. Briefly, the first step is communicating the problem and formulating it, then defining the system objectives, creating the conceptual model and finally designing experiments.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Common and Domain-specific Metamodel Elements for Problem Description in Simulation Problems

Ribino¹,

Seidita²,

Lodato³

et al. 2014

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…Figure 4). There are many possible approaches, including statistical and subjective validation techniques (Balci, 1990). Kraft describes a five-step validation process that combines both subjective and statistical comparisons of tracing data (Kraft, 2010, chapter 8).…”

Section: Simulation-based Timing Analysismentioning

confidence: 99%

Software Reverse Engineering in the Domain of Complex Embedded Systems

Kienle¹,

Kraft²,

Müller³

2012

Reverse Engineering - Recent Advances and Applications

View full text Add to dashboard Cite

“…Additionally, whatever potential benefits that using the model might have conferred, such as reduced training costs or improved decision analyses, are delayed or lost. The likelihood of a Type I error is termed model builder's risk [Balci, 1981] [Balci, 1990].…”

Section: Final Technical Report Serc-2012-tr-029 Task Order 0019 Rementioning

confidence: 99%

“…A Type II error can result in disastrous consequences, such as an aircraft crash because of an improperly trained pilot or a bridge collapsing because of faulty analyses of structural loads and stresses. The likelihood of a Type II error is termed model user's risk [Balci, 1981] [Balci, 1990].…”

Section: Final Technical Report Serc-2012-tr-029 Task Order 0019 Rementioning

confidence: 99%

Defining the Meaning of a Major Modeling and Simulation Change as Applied to Accreditation

Petty

Alldredge

Beach³

et al. 2012

View full text Add to dashboard Cite

Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. These existing methods vary widely in several respects including level of detail, specificity with respect to modeling and simulation, degree of quantitativeness, ease of use, and applicability to the sponsoring agency. All include some form of the notion of risk, which is conventionally defined as the product of the likelihood of an incorrect decision and the consequences of such a decision. A new method, the Quantitative-to-Qualitative Risk-based (QQR) method, was developed to make a quantitative recommendation regarding the re-validation of a modified model. The QQR method was developed with these goals in mind: to be quantitative, repeatable, and transparent to consider both model modifications and model use risk; to focus on model types and simulation applications of interest to the sponsoring agency; and to be simple and accessible so as to encourage its use in practical applications. The QQR method estimates the probability that not re-validating a modified model will lead to unacceptable consequences, given the modifications made to it. That estimated probability is meant to be interpreted, in the context of a re-validation decision, as a quantitative recommendation to re-validate the model. The method is based on a conditional probability formula that separates the various parts of the estimated probability into distinct terms and factors, and it provides procedures for estimating each term and factor. A central feature of the QQR method is a missions-means decomposition that allows both the method?s user to precisely and effectively identify the nature and extent of the modifications that were made to the model and the method to consider those modifications in its estimate. The QQR method was validated using a set of re-validation scenarios, each describing a model the modifications made to it, and the decision to be based on the model. The QQR method?s revalidation recommendations for the scenarios were compared to those of a set of human experts NO WARRANTY THIS STEVENS INSTITUTE OF TECHNOLOGY AND SYSTEMS ENGINEERING RESEARCH CENTER MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. STEVENS INSTITUTE OF TECHNOLOGY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDIN...

show abstract

Guidelines for successful simulation studies

Cited by 84 publications

References 19 publications

Common and Domain-specific Metamodel Elements for Problem Description in Simulation Problems

Common and Domain-specific Metamodel Elements for Problem Description in Simulation Problems

Software Reverse Engineering in the Domain of Complex Embedded Systems

Defining the Meaning of a Major Modeling and Simulation Change as Applied to Accreditation

Contact Info

Product

Resources

About