Agent-Based Modelling

Computer-Based models can be used to simulate changing behaviours of individuals connected together through a network of interactions. These interactions are defined through rules or heuristics. Agent-based models thus allow the researcher to simulate changing patterns of behaviour within a social systems over a period of time. When used to model real organizations or communities, data can be gathered to validate the accuracy of the predictions.

Recommended Readings

Breslin, D. (2014) Calm in the Storm: Simulating the Management of Organizational Evolution. Futures, 52, 62-77.

This paper presents an multi-level agent-based model of an organization, with interactions between employees, groups of employees and managers defined through a co-evolutionary logic, based on the mechanisms of variation-selection-retention. Through simulations, the model is used to first show how incremental and punctuated change patterns are produced from the same multi-level model. This findings undermines widespread views in the literature that radical and incremental change patterns are fundamentally different (see Gould versus Dawkins). Second, simulations are then used to determine the optimal levels of management control needed for different organizational configurations. The paper concludes that management control is vital even in highly decentralised configurations, in order to bring order to emerging chaotic patterns of change.

Breslin, D., Burkitt, M., Dobson, S. and Romano, D. (2016). Modelling Connectivity and Co-evolution: A Study of Domestic Fire Risk Behaviours. 16th EURAM Conference, June, Paris, France

In the Premonition project, which was funded by the South Yorkshire Fire & Rescue Authority (SYFRA), UK, an agent-based model is used to simulate changing domestic fire risk behaviours in the Sheffield city region. The changing behaviours of individuals is again modelled through a co-evolutionary logic of variation-selection-retention through their interactions within family and friends in the community. SYFRA use the model to predict changing patterns of fire risk within the centre, and then in a preventative fashion, target these vulnerable individuals through home safety visits.

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