Toward overcoming accidental complexity in organisational decision-making

Tony Clark, Vinay Kulkarni, Souvik Barat, Balbir Barn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper takes a practitioner's perspective on the problem of organisational decision-making. Industry practice follows a refinement based iterative method for organizational decision-making. However, existing enterprise modelling tools are not complete with respect to the needs of organizational decision-making. As a result, today, a decision maker is forced to use a chain of non-interoperable tools supporting paradigmatically diverse modelling languages with the onus of their co-ordinated use lying entirely on the decision maker. This paper argues the case for a model-based approach to overcome this accidental complexity. A bridge meta-model, specifying relationships across models created by individual tools, ensures integration and a method, describing what should be done when and how, and ensures better tool integration. Validation of the proposed solution using a case study is presented with current limitations and possible means of overcoming them outlined.
Original languageEnglish
Title of host publication2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)
PublisherIEEE
Pages368-377
ISBN (Electronic)978-1-4673-6908-4
DOIs
Publication statusPublished - 30 Nov 2015
Event2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS) - Ottawa, ON, Canada
Duration: 30 Sep 20152 Oct 2015

Conference

Conference2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)
Period30/09/152/10/15

Bibliographical note

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    Clark, T., Kulkarni, V., Barat, S., & Barn, B. (2015). Toward overcoming accidental complexity in organisational decision-making. In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS) (pp. 368-377). IEEE. https://doi.org/10.1109/MODELS.2015.7338268