Limited research has examined the decision-making process of officers during potentially volatile interactions with the public. To provide an initial understanding of this process in Canada, we conducted interviews using the Critical Decision Method with 23 Winnipeg Police officers with a variety of experiences (e.g. junior members, tactical officers). Using grounded theory methodology, we developed a theory that explains an officer’s decision-making process during potential use-of-force incidents. Our theory, the process of preserving life, has two main components: (1) the decision-making process involved in preserving life; and (2) the overarching factors that influence officer decision-making. We found that officers have expertise in responding to potential use-of-force incidents and that tactical officers have some unique elements of enhanced decision-making. Based on our findings, we discuss implications for officer training and future research.
This case study explored the influence of opponents’ spatial density and teammates’ communications on decision-making in a competitive gaming setting (i.e., League of Legends) using a mixed method. In-game behavior recordings complemented by verbal data from 15 post-game video-cued recall interviews were collected from five expert players during three French national Esport championship games. Using the Recognition-Primed Decision model, 243 decisions were classified into three types of recognition processes, and 70 decisions were observed to be influenced by teammate communication. Two distinct spatiotemporal game patterns emerged from data analysis. Decision processes seem to be affected by opponents’ spatial density and exhibit similarities to those observed in traditional team sports’ contexts, regardless of the level of time pressure involved. During higher spatial density phases, influenced decisions predominantly rely on sole reactions to explicit communication. Conversely, communications are mostly used as an auxiliary source of information in low spatial density phases. A quantitative increase in the cognitive package verbalization during confrontation phases introduces a potential paradox. Unlike traditional models suggesting a reduction in information quantity processed under pressure, high environmental monitoring and dense internal processing was observed, suggesting a complex micro-cognitive adaptation to manage increased proximal stimuli.
Single-Pilot Operations (SPO) in commercial aviation raise critical safety challenges in complex, time-pressured situations where human expertise is paramount. This study investigates how AI-based Human-Autonomy Teaming (HAT) can support pilot decision-making under high workload, compromised situational awareness, and time pressure. In a high-fidelity Airbus A320 simulator, 22 airline pilots performed a fuel- and time-constrained approach scenario designed to challenge HAT’s operational effectiveness under four conditions: no assistance, on-demand AI, proactive AI, and proactive explainable AI. Workload (NASA-TLX) remained high across conditions, yet decision-making differed markedly. Without assistance, only 20% chose the safest landing option; with on-demand AI, 40%; and with proactive AI (both versions), 100%. Explainable AI yielded the highest scenario comprehension (75%), matching two-crew baseline performance. Proactive systems increased decision and SA related interactions, fostering a collaborative dynamic akin to traditional crew resource management. Thus, the results demonstrate that, in the context of SPO, proactive and explainable AI was associated with crew-level decision performance within the experimental scenario without adding cognitive load. Beyond the aeronautical domain, our results are of interest for the design of cognitive support systems for high-risk domains where teams composed of humans and AI must adapt in real time.
As technology enables increasingly large, complex, and distributed team structures, new methods are needed to assess coordination in action teams. Recurrence Quantification Analysis (RQA) offers a powerful tool for capturing team coordination dynamics, yet its application to these modern teams remains methodologically underdeveloped. This paper identified and examined four characteristics of large action teams that contribute to analytical challenges when using RQA. We presented a systematic framework of Pre-Analytical Treatments, which are data preprocessing methods that can make RQA more computationally feasible while preserving meaningful team-level patterns. We then empirically evaluated three specific Pre-Analytical Treatments that either preserved temporal structure (Time Series) or controlled for periods of silence (Sequential, Dead-air Randomized). Using communication data from a 14-person expert team, we applied each treatment and examined their effects on RQA metrics and correlations with team trust, cohesion, and workload measures. Results demonstrated that these preprocessing decisions significantly affect RQA output relationships with team-level outcomes. Rather than advocating for a single approach, this study provides researchers with an empirically-grounded framework of methodological trade-offs to consider when applying RQA to large teams, offering both theoretical clarity about the impact of preprocessing decisions and practical guidance for selecting appropriate treatments based on research objectives.
Some of the toughest problems corporations, organizations, and societies face are wicked problems. Conventional or “tame” problems have right answers and best choices that can be determined using analytical methods. In contrast, wicked problems ( Rittel & Webber, 1973 ) are impossible to solve definitively but can only be managed because they are big, messy, laden with unintended consequences, and filled with conflict between stakeholders who have multiple competing interests. Analytical methods designed for conventional problems won’t work effectively against problems like illegal drugs, societal inequality, poverty, debt, war, or homelessness. We conducted an investigation of 32 actual wicked or near-wicked problems to identify practices that seemed effective and those that failed to work or even got in the way of progress. We found that progress depended on revising goals, aligning the goals of multiple stakeholders, and reframing the way the situation is understood. From this investigation, we formulated a set of recommendations. These include judging whether a problem is wicked, and if so shifting mindset to a flexible perspective, and making use of relevant tools, along with mistakes to avoid. We also suggest a potentially positive perspective on wicked problems—that in some situations they can support change and adaptation rather than rigidity and conformity.
