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Validation and Verification of Computational Models with Multiple Cognitive Agents

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Abstract

Wider issues of the validation of computational models - ascertaining that they are sound and consistent relative to some logical formalism and/or substantive theory - have not been a subject of the management science literature. In this paper, we demonstrate that computational models can be sound and consistent relative both to a fragment of strongly grounded autoepistemic logic (FOSGAL) and to theories of cognition without losing the expressiveness found in the informally oriented literature on organizational learning and business strategy. Validation is achieved by implementing models and their theoretical components in a programming language which corresponds to a known formal logic. The language used in this paper is SDML. The correspondence of SDML to autoepistemic logic is explained and justified. Issues associated with the verification of models - how well they correspond to observation - are also considered and extended. Benefits of explicit validation and verification of computational models are demonstrated by the implementation in SDML of a computational model of the critical-incident management organization of one of the largest public utilities in Europe. On the basis of the reported simulation results with the model, several research issues are identified both for the development of validation practices in the management sciences and for the analysis of crisis management.

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  • Scott Moss & Bruce Edmonds & Steve Wallis, 1997. "Validation and Verification of Computational Models with Multiple Cognitive Agents," Discussion Papers 97-25, Manchester Metropolitan University, Centre for Policy Modelling.
  • Handle: RePEc:wuk:mcpmdp:9725
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    References listed on IDEAS

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    5. Bruce Edmonds & Scott Moss & Steve Wallis, 1996. "Logic, Reasoning and A Programming Language for Simulating Economic and Business Processes with Artificially Intelligent Agents," Discussion Papers 009, Manchester Metropolitan University, Centre for Policy Modelling.
    6. Wallis, Kenneth F, 1993. "Comparing Macroeconometric Models: A Review Article," Economica, London School of Economics and Political Science, vol. 60(238), pages 225-237, May.
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    1. Juan Manuel Larrosa, 2016. "Agentes computacionales y análisis económico," Revista de Economía Institucional, Universidad Externado de Colombia - Facultad de Economía, vol. 18(34), pages 87-113, January-J.
    2. José Manuel Galán & Luis R. Izquierdo & Segismundo S. Izquierdo & José Ignacio Santos & Ricardo del Olmo & Adolfo López-Paredes & Bruce Edmonds, 2009. "Errors and Artefacts in Agent-Based Modelling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-1.
    3. Oswaldo Terán & Johanna Alvarez & Magdiel Ablan & Manuel Jaimes, 2007. "Characterising Land Holding Size Distributions in a Forest Reserve," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(3), pages 1-6.

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