HCL.
Hybrid Causal Logic
Originators Mosleh, Modarres, Groth, Mohaghegh & colleagues (Univ. of Maryland / UCLA B. John Garrick Institute, 2005–)
Paradigm Integrated probabilistic risk modelling
Unit of analysis Accident scenarios with physical, human & organisational contributors
Primary domains Aviation, nuclear, space, offshore, maritime

HCL is a unifying modelling language for probabilistic risk analysis that combines three complementary representations — Event Sequence Diagrams (ESD) for scenario evolution, Fault Trees (FT) for component-level failure logic, and Bayesian Belief Networks (BBN) for human and organisational causation — within a single quantifiable model.

Overview of the framework

Introduced in the mid-2000s and developed for the US FAA's aviation safety research programme (Wang & Mosleh, 2010; Groth et al., 2010), HCL extends the classical ET–FT architecture of PRA by adding BBNs at points where causation cannot be adequately represented by Boolean logic — e.g., human performance, organisational pressure, situational context. ESDs capture the time-ordered branching of an accident scenario; fault trees model the hardware/software basic events that drive ESD branches; BBNs feed softer evidence — human cognitive states, safety-climate indicators, performance-shaping factors — into those basic events. Mohaghegh, Kazemi & Mosleh (2009) formalised the quantification, enabling consistent propagation of uncertainty across the three layers. HCL has since been applied to taking off from the wrong runway, ship collisions, autonomous vehicles and offshore operations (Røed et al., 2009; Zhang et al., 2020).

ESD (scenario) Initiator Pivotal 1 Pivotal 2 Pivotal 3 End-state / Outcome Fault Tree (hardware/software) Top: P2 fails AND Comp A Comp B BBN (human & organisational) Safety culture Training Workload P(human error) BBN output conditions basic-event probability
Figure 1. HCL integrates ESDs (top), fault trees (bottom-left) and Bayesian belief networks (bottom-right); BBN outputs condition basic-event probabilities in the fault tree and, through them, ESD branch probabilities.

When to use it

Typical applications

  • Quantitative safety models where human and organisational factors materially affect risk.
  • Integration of heterogeneous evidence (expert judgement, operational data, audits).
  • Uncertainty quantification and sensitivity analysis across socio-technical layers.

Aviation & cross-domain relevance

  • Core method of the FAA-funded research programme on commercial aviation accident causation (Groth et al., 2010; Wang & Mosleh, 2010).
  • Applied to wrong-runway take-off, airline maintenance risk, ATC human performance.
  • Also used in offshore O&G (Røed et al., 2009), ship collision (Zhang et al., 2020), autonomous vehicles and ML-enabled systems.

Benefits

Analytical strengths

  • Unifies physical, human and organisational contributors in a single quantifiable model.
  • Combines the strengths of ESD/FT (auditable, combinatorial) with BBN (probabilistic inference with soft evidence).
  • Supports Bayesian updating as new operational data becomes available.
  • Enables integrated uncertainty propagation across all three representations.

Practical strengths

  • Backed by the FAA and US academic consortia with documented aviation case studies.
  • Compatible with established PRA toolchains (e.g., SAPHIRE, Riskman) and BBN tools (e.g., GeNIe, HUGIN, BayesiaLab).
  • Offers a rigorous basis for regulator–operator conversations about organisational factors.

Limitations

  • High modelling effort. Eliciting BBN structures and conditional probability tables is resource intensive.
  • Data scarcity for organisational variables can dominate uncertainty.
  • Expertise required across PRA, human factors, and probabilistic graphical models.
  • Interpretability. Large coupled models can become opaque to stakeholders without careful visualisation.
In short HCL is the state-of-the-art when a fully quantitative, integrated socio-technical risk model is needed — particularly in aviation safety, where ESD/FT and BBN strengths must be combined rather than chosen between.

References (APA 7)

Wang, C., & Mosleh, A. (2010). Qualitative–quantitative Bayesian belief networks for reliability and risk assessment. Proceedings of the Annual Reliability and Maintainability Symposium, San Jose, CA, 1–5. https://doi.org/10.1109/RAMS.2010.5448048

Groth, K., Wang, C., & Mosleh, A. (2010). Hybrid causal methodology and software platform for probabilistic risk assessment and safety monitoring of socio-technical systems. Reliability Engineering & System Safety, 95(12), 1276–1285. https://doi.org/10.1016/j.ress.2010.06.005

Mohaghegh, Z., Kazemi, R., & Mosleh, A. (2009). Incorporating organizational factors into probabilistic risk assessment (PRA) of complex socio-technical systems: A hybrid technique formalization. Reliability Engineering & System Safety, 94(5), 1000–1018. https://doi.org/10.1016/j.ress.2008.11.006

Røed, W., Mosleh, A., Vinnem, J. E., & Aven, T. (2009). On the use of the hybrid causal logic method in offshore risk analysis. Reliability Engineering & System Safety, 94(2), 445–455. https://doi.org/10.1016/j.ress.2008.04.003

Mohaghegh, Z., & Mosleh, A. (2009). Incorporating organizational factors into probabilistic risk assessment of complex socio-technical systems: Principles and theoretical foundations. Safety Science, 47(8), 1139–1158. https://doi.org/10.1016/j.ssci.2008.12.008

Further reading

Groth, K. M., & Mosleh, A. (2012). A data-informed PIF hierarchy for model-based human reliability analysis. Reliability Engineering & System Safety, 108, 154–174. https://doi.org/10.1016/j.ress.2012.08.006

Zhang, M., Zhang, D., Goerlandt, F., Yan, X., & Kujala, P. (2020). Use of HFACS and fault tree model for collision risk factors analysis of icebreaker assistance. Safety Science, 130, 104888. https://doi.org/10.1016/j.ssci.2020.104888

Zhou, Q., Wong, Y. D., Xu, H., Van Thai, V., Loh, H. S., & Yuen, K. F. (2020). An enhanced CREAM with stakeholder-graded protocols for tanker shipping safety application. Safety Science, 130, 104840.

Modarres, M., Kaminskiy, M., & Krivtsov, V. (2017). Reliability engineering and risk analysis: A practical guide (3rd ed.). CRC Press.

Federal Aviation Administration, Commercial Aviation Safety Team. (2012). Causal model for commercial aviation safety (Technical report). US DOT/FAA.