By Beth Lay (USA) and Matthieu Branlat (Norway)
Naturalistic Decision Making (NDM) and Resilience Engineering (RE) researchers and practitioners are beginning to explore synergies with a goal of co-creating new thinking, tools, and practices that will help us in these unprecedented times. This post is written from the viewpoint of Resilience Engineering practitioners and researchers, more novice at applying NDM tools.
Photo by Hans-Peter Gauster on Unsplash
Resilience Engineers speak about surprise as an inescapable component in complex, adaptive work. To be resilient is to be prepared for surprise, to “expect the unexpected”. This may seem like an oxymoron but it can be translated into practical strategies and actions. This is an area where Naturalistic Decision Making also provides concepts, tools and insights. We’ll explore this marriage, so to speak, of the two domains through taking a brief look at views, practices, and tools related to decision making under uncertainty.
NDM offers methods to identify critical, domain specific, complex decisions and tools to aid improving sensemaking and decision making. NDM experts use Cognitive Task Analysis (cognitive interviews) to identify common critical decisions and mine cues noticed by experts. They work with organization leaders and experts to define best courses of action and decisions that align with the organization’s risk tolerance. NDM practitioners craft scenarios that include ambiguity and decision points branching to play out in different ways. Novices learn to recognize cues that indicate increasing uncertainty, heightening risk, and signs trouble might be coming. Their decisions and handling of the situation is then compared with what an expert would notice and do. NDM researchers would also try to discover why decision makers were confused or mistaken.
Resilience Engineers claim that we need to monitor ambiguity as a signal to move into a different mode of managing work and that waiting until you are certain is too late. Resilience Engineering specify the need to practice decision making under uncertainty with scenarios where the path forward not clear (the more ambiguity, the better) with a goal of preparing to handle uncertainty and surprise – in general, not necessarily related to specific decisions. Resilience Engineers may craft scenarios based on situations where ambiguity was handled well and query which actions and interactions led to good outcomes then teach and train others to replicate these successful actions and interactions.
From the points above, we see that the management of uncertainty, in particular the notion of ambiguity, is a major topic for both NDM and RE. Ambiguous situations, a frequent characteristic of challenging real-world situations (i.e., “naturalistic” or related to “work-as-done”), provide insight into how individuals, teams or organizations manage uncertainty. For both fields, ambiguity can be a desired characteristic of a scenario used for knowledge elicitation or training. In the context of NDM, the macrocognitive model [1] provides a basis to explain, at an individual level, how the understanding of a situation evolves in time, as cues steer the observer one way or another. For RE, there may be less focus on the role or nature of expertise per se and more focus on how organizations, team dynamics and practices enable its expression.
Here’s a brief comparison of RE and NDM specific questions about managing uncertainty:
- RE: actively probe the “Un’s” [2] to understand unknown, unclear, uncertain, unseen, uncontrollable, and unstable situations through questions such as “How could we be surprised?” “What’s making me uneasy?” “What can I see, and more importantly what can I NOT see?” “What do I know and NOT know?” “What’s different?” “What else could this be?” “What do I need to pay close attention to?” “Who else can help?” The aim is to broaden and deepen the perspective that not all is knowable, however, there are questions and actions that can help move things from the unknown to known.
- NDM (example from nuclear power plant crisis management team) to increase team situation awareness / reduce common ground breakdowns through questions such as: “What is the immediate goal of your team?” “What are you doing to support that goal?” “What is our biggest worry?” “What is the current threat’s location, size, and intention?” “What do you think this situation will look like in 20 minutes? Why?” The aim is to improve sensemaking.
From this brief example, you may notice that both NDM and RE practices to improve decision making under uncertainty are similar, in some cases the questions are almost a mirror-image, while others are complementary.
Interestingly, RE and NDM share a “positivist” view about the investigation of work situations. In both fields, investigating why things go well in spite of challenges such as ambiguity is a central method to reveal the nature and the demands of work as well as expert behaviour. What seems to differentiate the two fields is the system of interest, more focused on individual(s) in NDM, more focused on the environment in which they perform in RE.
- Klein, G., Moon, B., & Hoffman, R. R. (2006). Making Sense of Sensemaking 2: A Macrocognitive Model. Intelligent Systems, IEEE, 21(5), 88–92.
- Laurin Mooney https://www.behighlyreliable.com/read-more