5.1 Introduction
In their everyday operations, controllers are often placed in situations that are either unfamiliar or filled with uncertainty. Without an under-standing of the situation, controllers cannot take timely and adequate actions. Making sense of critical situations is difficult, especially when controllers are faced with abundant, conflicted, or limited informa-tion. In recent years, the expansion of information technology has increased the amount of information presented to controllers without any assistance on how to make sense of the situation or how to antici-pate future trends of the situation. Air traffic control (ATC) is a com-plex and dynamic environment that requires controllers to attend to multiple events, register fast changing data, diagnose system failures, and resolve conflicts while maintaining resources to handle traffic, and above all to make sense of evolving scenarios. Sensemaking has been viewed as a retrospective activity of individuals and teams bounded by organizational rules and constraints (Weick 1995). Sensemaking has implications for the design of training curricula and decision support systems, especially for major air traffic management (ATM) system-wide interventions (e.g., single European sky ATM research program [SESAR] and next generation air transportation system [NextGen]).
In some respects, sensemaking entails the cognitive functions of recog-nition, modeling, and critiquing that have been discussed in the taskwork/
teamwork for effective and adaptive management (T2EAM) framework.
This chapter provides a more elaborate discussion of individual and team sensemaking functions because of the importance of managing modern ATM systems, which present high levels of uncertainty and complexity.
5.2 Frames and Cognitive Functions of Sensemaking
Sensemaking represents one of the key functions of human per-formance that can be accomplished by individuals, teams, and
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organizations (Klein et al. 2003). Sensemaking is triggered as a response to situational surprises and failures of expectation.
Sensemaking starts when prior understanding is in doubt and fur-ther attempts are made to integrate data into a better understanding of the situation. Sensemaking allows practitioners to understand how current accounts of the problem came about and how to anticipate future evolutions through a process of fitting data into an explanatory framework (Crandall et al. 2006). Klein et al. (2005) argue that sen-semaking is an essential activity that enables practitioners to recon-ceptualize the situation and not just fill in gaps to solve the problem at hand. In the context of ATM, the ability to make sense at early stages of the problem may result in timely and effective interventions.
Sensemaking has been based on the concept of a frame, which is an explanatory structure that defines entities and relates them to other entities (Klein et al. 2007). Typical frames in the context of ATM include the following:
• A radar map: A meaningful pattern that fits in multiple sources of information about aircraft position, flight level, heading, speed, and distances from obstacles or other aircraft
• An operational plan: A typical sequence of actions, including how to vector a wave of aircraft into a landing sequence, how to stack aircraft into holding patterns, how to circumnavigate aircraft around areas of convective weather, and so on
• A script: A typical pattern of division of work between the executive and coordinating controllers in an air traffic sector
• A story: An explanation or a story that a controller devel-ops “why an aircraft executes a rapid descend without prior notice” or “why an aircraft has stopped its taxi out from the runway,” and so on
Apart from such typical frames, controllers may construct their own individual frames to make sense of traffic patterns and evolving situations. For instance, controllers can create their own categories of standard and nonstandard flows, group aircraft into units, or imagine possible points of traffic conversion in order to make sense of traffic patterns (Malakis and Kontogiannis 2013, 2014).
According to the data/frame model (Klein et al. 2006) sensemaking is a recursive process that entails six cognitive functions (see Figure 5.1):
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1. Identifying a frame: This a pattern-matching function where data are fitted into a frame. Expectancies come true and there is a seemingly uninterrupted flow of data that fit into the explanatory frame, with minimal effort.
2. Questioning a frame: Inconsistencies between observations and expectations may trigger a process of questioning that entails:
gauging the quality of data, tracking anomalies, and judging the plausibility of several scenarios.
3. Comparing frames: When more than one frame is plausible, additional data may be sought that allow controllers to choose the most suitable one.
4. Creating a new frame: When the current frame may no longer be applicable, the controller may seek to create a new one to accommodate the data.
5. Preserving a frame: In many cases, the current frame may be valid and small deviations can be explained to preserve a mindset of operations.
Re–frame Compare frames create new frame Question a frame
Track anomalies detect inconsistencies
judge plausibility gauge data quality Elaborate a frame
Add and fill slots seek and infer data
discover new relationships discard data
Data Recognize/
construct a frame
Frame Manage attention
define, connect and filter data
Elaborating cycle
Preserving cycle Reframing cycle
Figure 5.1 Cognitive processes of the data-frame model. (From Klein, G.A. et al., IEEE Intelligent Systems, 21, 5, 88–92, 2006.)
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6. Elaborating a frame: Sometimes more details may be added into the current frame in order to explain a wide range of data, without abandoning the frame. This function allows controllers to fill slots, seek or discard data, and discover new data or new relationships.
The hunch that motivates questioning a frame is based on the avail-able data and may result from direct contradictions to the frame, the accumulation of discrepancies, or the detection of subtle anomalies.
Questioning a frame may lead to elaboration, preservation of a frame, and the comparison of alternative frames or reframing.
Research in ATM has addressed many aspects of team perfor-mance such as team communication (Cardosi 1993; Morrow et al.
1993), information sharing with flight crews (Hansman and Davison 2000), teamwork strategies during emergencies (Malakis et al. 2010a, b), aspects of error detection, and team support (Kontogiannis and Malakis 2009), as well as ways of managing uncertainty in a team environment (Corver and Grote 2016). However, these aspects of team performance have been examined in isolation, hence fail to get integrated into the context of team sensemaking.
Team sensemaking refers to the coordination of practitioners as they seek data, synthesize data, and disseminate their inferences in a team environment. According to Klein et al. (2010), the meaning of data becomes the object of negotiation within a team and often trig-gers a new round of seeking more refined data, hence replacing frames that seemed to be incompatible with data. Team sensemaking is not a stand-alone concept but is related to other team concepts such as team adaptation and shared understanding.
Team sensemaking involves a number of cognitive functions regarding the collection and synthesis of data, the cross-checking of data by other members, the resolution of disagreements, and the dis-semination of information among members. Therefore, team sense-making may include
1. Data synthesis 2. Seeking data
3. Monitoring data quality 4. Resolving disputes
5. Dissemination of information and orders