Cognitive Challenges to Resilience Dynamics in Managing Large-Scale Event Response

Abstract

In early 2014, a number of large-scale adverse weather events in the United States (and elsewhere) have renewed discussions of event response and resilience. Unlike events caused by human error or malicious intent, adverse natural events can be seen as uncontrolled and unpreventable; the measure of response success is the mitigation of adverse and disastrous effects. However, significant cognitive limitations interfere with human decision makers’ ability to effectively estimate likelihood, magnitude, and effective response of large-scale events in the face of multiple forms of uncertainty and system dependencies. Some authors highlight stability and maintenance of original design capability as the fundamental goal of robust and resilient response. Long-standing traditions from systems dynamics, population ecology, and process control suggest that improved understanding of system response ranges and dynamic equilibria provide a better description of effective event response. Challenges due to cognitive framing and deep uncertainty influence critical interplays of event characteristics, decision maker expertise, and resource availability. The concept of resilience boundary framing is introduced as a desirable, but difficult to achieve, goal for decision makers to respond gracefully to large-scale natural events. Examples from January and February 2014 are used to highlight these issues.

A framework for conceptualizing and assessing the resilience of essential services produced by socio-technical systems

“…Building resilience has arisen as a response to deal with uncertainty and external risk, limited control, deep disruption, and an unpredictable future (DuPlessis VanBreda 2001, Sheffi 2005, Bhamra et al 2011, Caldwell 2014. Resilience refers to the innate ability of complex adaptive systems to absorb disturbances or surprise and to adapt to dynamic change without losing their identity or function (Folke et al 2002, Walker et al 2004, Berkes 2007.…”

“…The ability to persist within a stability zone (Folke et al 2010) through anticipation strategies, being prepared, and applying prevention (Comfort et al 2001) An intangible emergent capacity for adaptation and transformation (Folke et al 2010) across multiple equilibria (North 1993, Caldwell 2014) How to build it Can be established by following best practice, through managing foreseeable risks (Garred 2013), and by how infrastructure is designed, built, and maintained (NIAC 2010) Is nurtured through the capacity for abductive thinking and sense making (Grøtan 2013) and evolutionary self-organization (Allan and Bryant 2014, Scolobig et al 2015, De Coning 2016 How to sustain it Employs single-loop learning and aims to strengthen negative feedback loops (Antonacopoulou and Chiva, unpublished manuscript † ): to return conditions toward a predetermined target, to remove deviations, and to keep operations within deterministic boundaries (Weick and Sutcliffe 2007) Employs double-loop learning and aims to strengthen positive feedback loops (Antonacopoulou and Chiva, unpublished manuscript † ): to self-reinforce, amplify, enhance, and stimulate behaviors that enhance resilience, which includes modifying the rules that drive behavior (Holman 2010) † https://warwick.ac.uk/fac/soc/wbs/conf/olkc/archive/oklc6/papers/antonacopoulou__chiva.pdf and surprise and to endure novelty and instability, including multiple shocks and cascading failure (Folke et al 2010, Walker andSalt 2012). General resilience emerges when predetermined plans are inadequate to deal with the situation at hand, and new capabilities are dynamically developed to respond (Lee et al 2013).…”

Supporting Advances in Human-Systems Coordination through Simulation of Diverse, Distributed Expertise

“…Addressing complex problems in industry, government, and academic sectors often requires the coordination of expertise of multiple human, and more recently, non-human, entities [1,2]. Critical aspects of team level event response and problem solving include the support of distributed expertise through "information alignment" [3], which facilitates efficient and effective coordination.…”

Measuring Resilience

“…These are human–machine or sociotechnical work systems that require cognition to adapt to complexity (Klein et al, 2003). Many cases can be cited in which macrocognitive work systems have had to be adaptive and resilient (e.g., Caldwell, 2014). When the tsunami wave triggered the meltdown at the Daiichi nuclear power plant in Fukushima, Japan on March 11, 2011, power was lost to the control computers.…”