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Validation and verification of social processes within agent-based computational organization models

Author

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  • Levent Yilmaz

    (Auburn University)

Abstract

The use of simulation modeling in computational analysis of organizations is becoming a prominent approach in social science research. However, relying on simulations to gain intuition about social phenomena has significant implications. While simulations may give rise to interesting macro-level phenomena, and sometimes even mimic empirical data, the underlying micro and macro level processes may be far from realistic. Yet, this realism may be important to infer results that are relevant to existing theories of social systems and to policy making. Therefore, it is important to assess not only predictive capability but also explanation accuracy of formal models in terms of the degree of realism reflected by the embedded processes. This paper presents a process-centric perspective for the validation and verification (V&V) of agent-based computational organization models. Following an overview of the role of V&V within the life cycle of a simulation study, emergent issues in agent-based organization model V&V are outlined. The notion of social contract that facilitates capturing micro level processes among agents is introduced to enable reasoning about the integrity and consistency of agent-based organization designs. Social contracts are shown to enable modular compositional verification of interaction dynamics among peer agents. Two types of consistency are introduced: horizontal and vertical consistency. It is argued that such local consistency analysis is necessary, but insufficient to validate emergent macro processes within multi-agent organizations. As such, new formal validation metrics are introduced to substantiate the operational validity of emergent macro-level behavior.

Suggested Citation

  • Levent Yilmaz, 2006. "Validation and verification of social processes within agent-based computational organization models," Computational and Mathematical Organization Theory, Springer, vol. 12(4), pages 283-312, December.
  • Handle: RePEc:spr:comaot:v:12:y:2006:i:4:d:10.1007_s10588-006-8873-y
    DOI: 10.1007/s10588-006-8873-y
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    References listed on IDEAS

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    Cited by:

    1. Liang Chen & Guy G. Gable & Haibo Hu, 2013. "Communication and organizational social networks: a simulation model," Computational and Mathematical Organization Theory, Springer, vol. 19(4), pages 460-479, December.
    2. Jiang, Guoyin & Tadikamalla, Pandu R. & Shang, Jennifer & Zhao, Ling, 2016. "Impacts of knowledge on online brand success: an agent-based model for online market share enhancement," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1093-1103.
    3. M Günther & C Stummer & L M Wakolbinger & M Wildpaner, 2011. "An agent-based simulation approach for the new product diffusion of a novel biomass fuel," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 12-20, January.
    4. Yu Zhang & Jason Leezer, 2010. "Simulating human-like decisions in a memory-based agent model," Computational and Mathematical Organization Theory, Springer, vol. 16(4), pages 373-399, December.
    5. Dehua Gao & Aliakbar Akbaritabar, 2022. "Using agent-based modeling in routine dynamics research: a quantitative and content analysis of literature," Review of Managerial Science, Springer, vol. 16(2), pages 521-550, February.
    6. Lea Sonderegger-Wakolbinger & Christian Stummer, 2015. "An agent-based simulation of customer multi-channel choice behavior," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(2), pages 459-477, June.
    7. Kathleen M Carley & Geoffrey P Morgan & Michael J Lanham, 2018. "Deterring the development and use of nuclear weapons: a multi-level modeling approach," The Journal of Defense Modeling and Simulation, , vol. 15(4), pages 483-493, October.
    8. Azar, Elie & Nikolopoulou, Christina & Papadopoulos, Sokratis, 2016. "Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling," Applied Energy, Elsevier, vol. 183(C), pages 926-937.
    9. Bing Bai & Byungjoon Yoo & Xiuquan Deng & Iljoo Kim & Dehua Gao, 2016. "Linking routines to the evolution of IT capability on agent-based modeling and simulation: a dynamic perspective," Computational and Mathematical Organization Theory, Springer, vol. 22(2), pages 184-211, June.
    10. Jiang, Guoyin & Shang, Jennifer & Liu, Wenping & Feng, Xiaodong & Lei, Junli, 2020. "Modeling the dynamics of online review life cycle: Role of social and economic moderations," European Journal of Operational Research, Elsevier, vol. 285(1), pages 360-379.
    11. Zhu, Hou & Hu, Bin, 2018. "Impact of information on public opinion reversal—An agent based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 578-587.

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