Developing an ABM-driven Decision Support System in the Emergency Services

Stephen Dobson, Mark Burkitt, Dermot Breslin, Daniela Romano


The research presented here outlines an application of Agent-Based Modelling (ABM) used to support strategic decision-making in the emergency services. Here the resulting ABM-driven Decision Support System (DSS) (called ‘Premonition’) is designed to help practitioners engage with a complex and dynamic environment of co-evolving fire risk behaviours through time. Social change is presented here as a process by which behaviours co-evolve within connected networks of agents. ABM is identified as a beneficial approach to simulate changing household behaviours and the influence of social networks, environmental factors, and also fire service interventions within the Sheffield City region, UK. This project seeks to develop a DSS which supports the stages of ‘intelligence’, ‘design’, and ‘choice’ as the decision-maker moves from identifying problem areas, establishing possible strategies for intervention, and predicting possible outcomes of alternative courses of action.


  1. Andreica, M. 2009. 'Overview of Modelling the Risk Assessment Process in Applied Project Management' Metalurgia International, 14, pp. 139-144.
  2. Anthony, R.N. 1965. Planning and Control Systems: A framework for analysis Harvard University Graduate School of Business Administration
  3. Apolloni, B. 1998. 'Learning fuzzy decision trees' Neural Networks, vol. 11, pp 885-895.
  4. Bayraktar, M.E. and Hastak, H. 2009. 'Bayesian Belief Network Model for Decision Making in Highway Maintenance: Case Studies' Journal of Construction Engineering and Management 135(12), pp.1357-1369
  5. Breslin, D. 2011. Interpreting Futures through the MultiLevel Co-Evolution of Organizational Practices. Futures, 43(9), pp.1020-1028.
  6. Breslin, D., Romano, D. and Percival, J. 2015. 'Conceptualizing and Modelling Multi-Level Organisational Co-Evolution.78 In Secchi, D. and Neumann, M. (Eds.) Agent-Based Modeling in Management and Organizations, Springer, pp. 137-157
  7. Chung, T. H., Abraham, D. M., and Gokhale, S. B. 2004. 'Decision support system for microtunneling applications.78 Journal of Construction Engineering Management, 130(6), pp. 835-843.
  8. Covaliu, Z., and Soyer, R., 1996. 'Bayesian project management'. In Proc. Conf. ASA section on Bayesian Statistical Science, pp. 208-213.
  9. de Meaux, J. and Koornneef, M. 2008. 'The cause and consequences of natural variation: the genome era takes off!78 Current Opinion in Plant Biology, 11, pp. 99-102.
  10. Dobson, S., Breslin, D., Suckley, L., Barton, R. and Rodriguez, L. 2013. Small Firm Growth and Innovation: an Evolutionary Approach. International Journal of Entrepreneurship & Innovation, 14(2), 69-80.
  11. Dodin, B.M., and Elmaghraby, S.E., 1985. 'Approximating the criticality indices of the activities in PERT networks'. Management Science 31, pp. 207-223.
  12. Gilbert, G. N. 2008. Agent-based models Sage Publications.
  13. Gorry, G.A. and Scott Morton, M.S. 1971. 'A Framework for Management Information Systems' Sloan Management Review 13(1), pp50-70
  14. Hart, D. 2008 'Risk management in paediatric hospitals: Results of the multicentre project “Reducing Risks - Increasing Safety"78. Monatsschrift Kinderheilkunde, 156, pp. 1104-1113.
  15. Horstmann, R. 2006 'Risk management in the operation room results of a pilot project of interdisciplinary "incident reporting", Zentralblatt Fur Chirurgie,. 131, pp. 332-340.
  16. Jagielska, A. and Jacob, 1993. 'A Neural network model for sales forecasting'. New Zealand International Conference on Artificial Neural Networks and Expert Systems Dunedin, New Zealand, pp. 544-549
  17. Jensen, F., Lauritzen, S. and Olesen, K. 1990. 'Bayesian updating in causal probabilistic networks by local computations' Computational Statistics Quarterly 4, pp.269-282.
  18. Keen, P. 1987. 'Decision Support Systems: The next decade' Decision Support Systems 3(3), pp253-265
  19. Keenan, P. 1998. 'Spatial Decision Support Systems for vehicle routing', Decision Support Systems 22(1) pp. 65-71.
  20. Lauría, E. J., and Duchessi, P. J. 2006. 'A Bayesian belief network for IT implementation decision support'. Decision Support Systems, 42(3), pp. 1573-1588.
  21. Lee, E., Park, Y. and Shin, J.G. 2008 'Large engineering project risk management using a Bayesian belief network' Expert Systems with Applications 36, pp. 5880-5887
  22. MacCrimmon, K.R., and Ryavec, C.A., 1964. 'An analytic study of the PERT assumptions'. Operations Research 14, pp.16-37.
  23. Mitroff, I.I., and Linstone, H.A. 1993 The Unbounded Mind: Breaking the chains of traditional business thinking Oxford University Press, New York
  24. Richmond, P., Walker, D., Coakley, S., & Romano, D. 2010. 'High performance cellular level agent-based simulation with FLAME for the GPU'.Briefings in bioinformatics, 11(3), pp.334-347.
  25. Rittel, H.W., and Webber, M.M. 1973. 'Dilemmas in a general theory of planning'. Policy sciences, 4(2), pp. 155-169.
  26. Romano, D. M., Lomax, L., & Richmond, P. 2009. 'NARCSim an agent-based illegal drug market simulation'. In Games Innovations Conference, 2009. ICE-GIC 2009. International IEEE Consumer Electronics Society's (pp. 101-108). IEEE.
  27. Shim, J., Warkentin, M., Courtney, J., Power, D., Sharda, R. and Carlsson, C. 2002 'Past, present, and future of decision support technology', Decision Support Systems 33 pp. 111-126.
  28. Simon, H.A. (1960) The New Science of Management Harper Brothers, New York
  29. Smith, R., Wright, M., and Solanki, A. 2008. Analysis of fire and rescue service performance and outcomes with reference to population socio-demographics. Department for Communities and Local Government, Fire Research Series 9/2008
  30. Taylor, M.J., Higgins, E., Lisboa, P.J., and Kwasnica, V. 2012. 'An exploration of causal factors in unintentional dwelling fires'. Risk Management, 14, pp. 109 - 125
  31. Virto, M.A., Martin, J., and Insua, D.R., 2002. 'An approximate solutions of complex influence diagrams through MCMC methods'. In: Gamez, S. (Eds.), Proc. First European Workshop on Probabilistic Graphical Models, pp. 169-175
  32. Xiaocong, H., & Ling, K. 2010. 'A risk management decision support system for project management based on Bayesian network' in Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference, pp. 308-312.

Paper Citation

in Harvard Style

Dobson S., Burkitt M., Breslin D. and Romano D. (2016). Developing an ABM-driven Decision Support System in the Emergency Services . In Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-187-8, pages 151-157. DOI: 10.5220/0005773501510157

in Bibtex Style

author={Stephen Dobson and Mark Burkitt and Dermot Breslin and Daniela Romano},
title={Developing an ABM-driven Decision Support System in the Emergency Services},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Developing an ABM-driven Decision Support System in the Emergency Services
SN - 978-989-758-187-8
AU - Dobson S.
AU - Burkitt M.
AU - Breslin D.
AU - Romano D.
PY - 2016
SP - 151
EP - 157
DO - 10.5220/0005773501510157