M. Kiwan¹, D.V. Berezkin², M. Raad³, B. Rasheed4

1–3 Bauman Moscow State Technical University (Moscow, Russia)

4 Innopolis University

Statement of a problem. One of the main tasks today is to prevent accidents in complex systems, which requires determining their cause. In this regard, several theories and models of the causality of accidents are being developed. Traditional approaches to accident modeling are not sufficient for the analysis of accidents occurring in complex environments such as socio-technical systems, since an accident is not the result of individual component failure or human error. Therefore, we need more systematic methods for the investigation and modeling of accidents.

Purpose. Conduct a comparative analysis of accident models in complex systems, identify the strengths and weaknesses of each of these models, and study the feasibility of their use in risk management in socio-technical systems.

The paper analyzes the main approaches of accident modeling and their limitations in determining the cause-and-effect relationships and dynamics of modern complex systems. the methodologies to safety and accident models in sociotechnical systems based on systems theory are discussed. The complexity of sociotechnical systems requires new methodologies for modeling the development of emergency management. At the same time, it is necessary to take into account the socio-technical system as a whole and to focus on the simultaneous consideration of the social and technical aspects of the systems. When modeling accidents, it is necessary to take into account the social structures and processes of social interaction, the cultural environment, individual characteristics of a person, such as their abilities and motivation, as well as the engineering design and technical aspects of systems.

Practical importance. Based on analyzing various techniques for modeling accidents, as well as studying the examples used in modeling several previous accidents and review the results of this modeling, it is concluded that it is necessary to improve the modeling techniques. The result was the appearance of hybrid models of risk management in socio-technical systems, which we will consider in detail in our next work.

Kiwan M., Berezkin D.V., Raad M., Rasheed B. Analyzing common approaches of accident models for risk management in sociotechnical systems. Dynamics of complex systems. 2021. T. 15. № 1. Р. 22−37. DOI: https://doi.org/10.18127/j19997493-202101-03 (In Russian)

1. Höhl M., Ladkin P. Analysing the 1993 Warsaw accident with a WB-Graph. Report RVS-Occ-97-09. 1997. (8).
2. Ferry T.S. Modern accident investigation and analysis. California: John Wiley & Sons. 1988. 105 p.
3. Leveson N. Others Medical devices: The therac-25. Appendix of: Safeware: System Safety and Computers. 1995.
4. Hollnagel E. Barriers and accident prevention Ashgate. Hampshire. 2004.
5. Hollnagel E. Cognitive reliability and error analysis method (CREAM). Elsevier. 1998.
6. Hollnagel E., Goteman O. The functional resonance accident model. Proceedings of cognitive system engineering in process plant. 2004. P. 155–161.
7. Hopkins A. An AcciMap of the Esso Australia gas plant explosion. Proceedings of the 18th ESReDA Seminar. Karlstad. Sweden, Ed. by Svedung, I., Cojazzi. G. M. 2000.
8. Henley E.J., Kumamoto H. Reliability engineering and risk assessment. Prentice Hall. 1981.
9. Malhotra M., Trivedi K.S. Power-hierarchy of dependability-model types. IEEE Transactions on Reliability. 1994. № 3 (43). P. 493–502.
10. Dugan J.B., Bavuso S.J., Boyd M.A. Fault trees and sequence dependencies 1990. P. 286–293.
11. Zang X., Wang D., Sun H. et al. A BDD-based algorithm for analysis of multistate systems with multistate components. IEEE Transactions on computers. 2003. № 12 (52). P. 1608–1618.
12. Veeraraghavan M., Trivedi K.S. A combinatorial algorithm for performance and reliability analysis using multistate models. IEEE Transactions on Computers. 1994. № 2 (43). P. 229–234.
13. Hollnagel E. Anticipating failures: what should predictions be about? 2001.
14. Hollnagel E., Woods D.D. Joint cognitive systems: Foundations of cognitive systems engineering. Woods, CRC press. 2005.
15. Leveson N. A new accident model for engineering safer systems. Safety science. 2004. № 4 (42). P. 237–270.
16. Rasmussen J. Risk management in a dynamic society: a modelling problem. Safety science. 1997. № 2–3 (27). P. 183–213.
17. Vicente K.J., Mumaw R.J., Roth E.M. Operator monitoring in a complex dynamic work environment: a qualitative cognitive model based on field observations. Theoretical Issues in Ergonomics Science. 2004. № 5 (5). P. 359–384
18. Rasmussen J., Suedung I. Proactive risk management in a dynamic society. Swedish Rescue Services Agency. 2000.
19. Ladkin P.B. Why-Because Analysis of the Glenbrook, NSW Rail Accident and Comparison with Hopkins’s Accimap. 2005.
20. Johnson C.W., Holloway C.M. The ESA/NASA SOHO mission interruption: Using the STAMP accident analysis technique for a software related ‘mishap’. Software: Practice and Experience. 2003. № 12 (33). P. 1177–1198.
21. Johnson C., Holloway C.M. A survey of logic formalisms to support mishap analysis. Reliability Engineering & System Safety. 2003.  № 3 (80). P. 271–291.
22. Pearl J. Introduction to probabilities, graphs, and causal models. Causality: models, reasoning and inference. 2000. P. 1–40.
23. Ladkin P., Loer K. Why-because analysis: Formal reasoning about incidents. Bielefeld, Germany, Document RVS-Bk-98-01, Technischen Fakultat der Universitat Bielefeld, Germany. 1998.
24. Lewis D. Causation. The journal of philosophy. 1974. № 17 (70). P. 556–567.
25. Woo D.M., Vicente K.J. Sociotechnical systems, risk management, and public health: comparing the North Battleford and Walkerton outbreaks. Reliability Engineering & System Safety. 2003. № 3 (80). P. 253–269.
26. Leveson N.G. System safety engineering: Back to the future. Massachusetts Institute of Technology. 2002.
27. Huang W., Liu Y., Zhang Y. et al. Fault Tree and Fuzzy DS Evidential Reasoning combined approach: An application in railway dangerous goods transportation system accident analysis. Information Sciences. 2020. (520). C. 117–129.
28. Leveson N., Dulac N. Safety and risk-based design in complex systems-of-systems 2005. 2558 с.
29. Proletarsky A.V., Andreev A.M., Berezkin D.V. et al. Approach to Forecasting the Development of Crisis Situations in Complex Information Networks 2019. P. 437–446.
30. Proletarskij A.V., Berezkin D.V., Mozharov G.P. Igrovye i grafovye modeli informacionnyh setej dlya issledovaniya slozhnyh system. Avtomatizaciya. Sovremennye tekhnologii. 2020. № 6 (74). C. 269–281 (In Russian).
31. Kiwan M., Berezkin D.V. Disaster Recognition System for Risk Management in Socio-Technical Systems 2021. №2 (3). P. 952–958.
32. Macchi L. A Resilience Engineering approach for the evaluation of performance variability: development and application of the Functional Resonance Analysis Method for air traffic management safety assessment 2010.
33. Rosa L.V., Haddad A.N., Carvalho P.V.R. Assessing risk in sustainable construction using the Functional Resonance Analysis Method (FRAM). Cognition, Technology & Work. 2015. № 4 (17). P. 559–573.
34. Patriarca R., Gravio G. Di, Costantino F. A Monte Carlo evolution of the Functional Resonance Analysis Method (FRAM) to assess performance variability in complex systems. Safety science. 2017. (91). P. 49–60.