When something goes wrong in an operation, the first question asked is usually “who did it?”; it is quick and has an answer, but it often fails to provide the explanation that prevents the problem from happening again. This episode examines systems thinking through five verified peer-reviewed sources. Sterman (2006) states that policies often fail or even worsen the problem they are meant to solve; while evidence-based learning is expected to prevent this policy resistance, learning in complex systems is often weak and slow. Complexity keeps us from seeing the delayed and distant effects of interventions and produces unintended side effects; moreover, learning can fail even when strong evidence is available, because common mental models lead to flawed but self-confirming inferences. It should be noted that the context of this study is public health policy and that the link to aviation is an adaptation. Sterman (2002) states that understanding complex systems requires knowledge of feedback, stocks and flows, time delays and nonlinearity; that research shows these concepts to be highly counterintuitive and poorly understood; that formal modeling and simulation are necessary but not sufficient; and that the most important and hardest thing to learn is that all models are wrong and that we must be humble about the limits of our knowledge. Repenning and Sterman (2002), studying a firm's attempt to improve two core business processes, show that the critical determinant of success is the interaction between managers' attributions about the cause of poor organizational performance and the physical structure of the workplace, particularly the delays between investing in improvement and seeing its rewards; the co-evolution of these beliefs and this structure can produce an organization characterized by conflict, mistrust and control structures that block any useful change. The abstract of this study does not name the firm, its industry or its country. Dekker, Cilliers and Hofmeyr (2011) state that complexity theory suggests we see performance as an emergent property resulting from complex interactions and relationships, that this may conflict with what stakeholders consider legitimate and normal in investigations, and that blaming components is still common when systems fail; they also show that the Newtonian analysis seen in many safety analyses makes particular assumptions about cause and effect, the foreseeability of harm, time-reversibility and the possibility of producing the true story of an event. Leveson (2004), in turn, states that the most effective models go beyond assigning blame and help us learn as much as possible about all the factors involved, including those related to social and organizational structures. Finally, Hulme and colleagues (2019) searched four databases, reviewed peer-reviewed articles published between 1990 and 2018 and included a total of seventy-three studies; the most common application contexts include aviation, maritime, rail, public health and mining, and more contributing factors were found at the lower ends of the sociotechnical systems examined, namely at the levels of equipment and technology, people and staff, and operating processes. This review does not claim that these methods are superior to other methods. At the end of the episode, you will clearly see a four-zone systems thinking review built on two axes: the layer at which an explanation stops and how quickly feedback arrives.
📌 Playlist for the full series https://www.youtube.com/playlist?list=PL6s4Uhec9cFBZNpSWXeuaybOuyCsHS71K
Sources Used
Sterman, J. D. (2002). All models are wrong: reflections on becoming a systems scientist. System Dynamics Review, 18(4), 501-531.
Sterman, J. D. (2006). Learning from evidence in a complex world. American Journal of Public Health, 96(3), 505-514.
Repenning, N. P., & Sterman, J. D. (2002). Capability traps and self-confirming attribution errors in the dynamics of process improvement. Administrative Science Quarterly, 47(2), 265-295.
Leveson, N. (2004). A new accident model for engineering safer systems. Safety Science, 42(4), 237-270.
Dekker, S., Cilliers, P., & Hofmeyr, J.-H. (2011). The complexity of failure: Implications of complexity theory for safety investigations. Safety Science, 49(6), 939-945.
Hulme, A., Stanton, N. A., Walker, G. H., Waterson, P., & Salmon, P. M. (2019). What do applications of systems thinking accident analysis methods tell us about accident causation? A systematic review of applications between 1990 and 2018. Safety Science, 117, 164-183.
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