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Credibility assessment of simulation results

Author:

Osman BalciAuthors Info & Claims

WSC '86: Proceedings of the 18th conference on Winter simulation

Pages 38 - 44

https://doi.org/10.1145/318242.318258

Published: 01 December 1986 Publication History

Abstract

The purpose of this paper is to provide some guidelines for assessing the credibility of simulation results. The life cycle of a simulation study is characterized in terms of 10 phases, 10 processes, and 13 credibility assessment stages (CASs). The credibility of simulation results is assessed by integrating ten CASs: formulated problem verification, feasibility assessment, system and objectives definition verification, model qualification, communicative model verification, programmed model verification, experiment design verification, data validation, model validation, and quality assurance of experimental model. Indicators are identified for evaluating credibility in most of the CASs. The guidelines provided herein are essential for the success of a simulation study.

References

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Annino, J.S. and Russell, E.C. (1979). The ten most frequent causes of simulation analysis failure- and how to avoid them!. Simulation 32, 6 (June), 137-140.

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Balci, O. (1986a). Requirements for model development environments. Computers eJ Operations Research 13, 1 (Jan.-Feb.), 53-67.

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Balci, O. and Sargent, R.G. (1981). A methodology for costrisk analysis in the statistical validation of simulutiort models. Communications of the A CM 24, 4 (Apr.), 190-197.

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Balci, O. and Sargent, R.G. (1984). A bibliography on the credibility assessment and validation of simulation and mathematical models. SimuIetter 15, 3 (July), 15-27.

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Cited By

Rhodes D(2022)Investigating Model Credibility Within a Model Curation ContextRecent Trends and Advances in Model Based Systems Engineering10.1007/978-3-030-82083-1_7(67-77)Online publication date: 25-Mar-2022
https://doi.org/10.1007/978-3-030-82083-1_7
Braakman SPathmanathan PMoore H(2022)Evaluation framework for systems modelsCPT: Pharmacometrics & Systems Pharmacology10.1002/psp4.1275511:3(264-289)Online publication date: 10-Jan-2022
https://doi.org/10.1002/psp4.12755
Kozhevnikova TManzhula I(2021)Simulation Modeling Using Neural Networks to Control Complex Technical SystemsTechnological Advancements in Construction10.1007/978-3-030-83917-8_14(149-158)Online publication date: 7-Sep-2021
https://doi.org/10.1007/978-3-030-83917-8_14
Show More Cited By

Index Terms

Credibility assessment of simulation results
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
    2. Simulation evaluation
2. General and reference
  1. Document types
    1. Surveys and overviews

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Information

Published In

cover image ACM Conferences

WSC '86: Proceedings of the 18th conference on Winter simulation

December 1986

890 pages

ISBN:0911801111

DOI:10.1145/318242

Editors:
James R. Wilson
Purdue Univ., West Lafayette, IN
,
James O. Henriksen
Wolverine Software Corp.
,
Stephen D. Roberts
Regenstrief Institute

Copyright © 1986 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 1986

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Cited By

Rhodes D(2022)Investigating Model Credibility Within a Model Curation ContextRecent Trends and Advances in Model Based Systems Engineering10.1007/978-3-030-82083-1_7(67-77)Online publication date: 25-Mar-2022
https://doi.org/10.1007/978-3-030-82083-1_7
Braakman SPathmanathan PMoore H(2022)Evaluation framework for systems modelsCPT: Pharmacometrics & Systems Pharmacology10.1002/psp4.1275511:3(264-289)Online publication date: 10-Jan-2022
https://doi.org/10.1002/psp4.12755
Kozhevnikova TManzhula I(2021)Simulation Modeling Using Neural Networks to Control Complex Technical SystemsTechnological Advancements in Construction10.1007/978-3-030-83917-8_14(149-158)Online publication date: 7-Sep-2021
https://doi.org/10.1007/978-3-030-83917-8_14
Leathrum JCollins ACotter TLynch CGore R(2020)Education in analytics needed for the modeling & simulation processProceedings of the Winter Simulation Conference10.5555/3466184.3466555(3236-3247)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466555
Lynch CDiallo SKavak HPadilla J(2020)A content analysis-based approach to explore simulation verification and identify its current challengesPLOS ONE10.1371/journal.pone.023292915:5(e0232929)Online publication date: 13-May-2020
https://doi.org/10.1371/journal.pone.0232929
Leathrum JCollins ACotter TLynch CGore R(2020)Education in Analytics Needed for the Modeling & Simulation Process2020 Winter Simulation Conference (WSC)10.1109/WSC48552.2020.9384122(3236-3247)Online publication date: 14-Dec-2020
https://doi.org/10.1109/WSC48552.2020.9384122
Yilmaz LLiu B(2020)Model credibility revisited: Concepts and considerations for appropriate trustJournal of Simulation10.1080/17477778.2020.182158716:3(312-325)Online publication date: 17-Sep-2020
https://doi.org/10.1080/17477778.2020.1821587
Moustaid EMeijer S(2017)A hybrid approach for building models and simulations for smart citiesProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242308(1-12)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242308
Sargent RBalci O(2017)History of verification and validation of simulation modelsProceedings of the 2017 Winter Simulation Conference10.5555/3242181.3242199(1-16)Online publication date: 3-Dec-2017
https://dl.acm.org/doi/10.5555/3242181.3242199
Moustaid EMeijer S(2017)A hybrid approach for building models and simulations for smart cities: Expert knowledge and low dimensionality2017 Winter Simulation Conference (WSC)10.1109/WSC.2017.8247896(1551-1562)Online publication date: Dec-2017
https://doi.org/10.1109/WSC.2017.8247896
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