Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
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Understanding change in complex socio-technical systems
Information-Knowledge-Systems Management - Complex Socio-Technical Systems --Understanding and Influencing Causality of Change
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Causality is grounded in every scientific field. Computational modelling is no exception, except that it is our focus in this article. But what if we have made a mistake? Is causality a constraint on our understanding of complex systems? Is it an obstacle in our ability to build theories to control change in complex systems? Or do we merely need to refine the concept as we evolve from one level of complexity to another? We start the journey of this article by glancing over a few key pieces of work from Philosophy and Metaphysics. We then centre the discussion on the pivotal element of this paper, causality of change in complex systems of systems and demonstrate that a counterfactual analysis of causality breaks down. We steer the discussion more towards "change" and the separation between physical and perceptual elements. Three applications are presented as examples of the type of complexity we face in computational modelling of complex systems of systems. These three applications --covering story generation in linguistics, network centric operations in defence and interdependency security problems --demonstrate how causal dependencies can be modelled, identified and extracted from a computational environment that mimics real-world complex systems of systems. We conclude the paper with a proposed model to control change in complex systems; a model we call the E4 model.