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Chapter 4 Machine and Network Elements of Team Decision Making
Pages 37-48

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From page 37...
... The chapter ends with a brief discussion of metrics that can help assess humanmachine collaboration for decision making. MIXED HUMAN-COMPUTER TEAMS While there are extensive studies of human teamwork in varied contexts, 1 further studies of the characteristics of successful decision-aiding automation in the context of hybrid humanautomation teams are warranted.2 Recent work on the foundations of team cognition helps to fill the need for further empirical studies of team performance that can elicit key attributes for the design of decisionaiding automation.
From page 38...
... Common ground refers to the process of communicating, testing, updating, tailoring, and repairing mutual understandings and permits people to use abbreviated forms of communication, such as head-nods (or an automation analogy) and still be reasonably confident that potentially ambiguous messages and signals will be understood.
From page 39...
... tasks among team members. Other relevant work includes efforts to provide people with new tools and platforms that enable them to solve problems jointly and to tap into larger crowds of people and their intellect.
From page 40...
... Although such systems can be extremely effective, they lack deep understanding of the domain to which they are applied; they can make inferences, but inferences have a non-zero chance of being wrong. People bring a variety of contextual information to bear on interpretation of data, deriving meaning that extends far beyond the raw data.
From page 41...
... The main purpose of sentence-level processing in dialogue systems is often to extract dialogue acts, which convey the action that the persons performed in its action of speaking. 9 These dialogue acts provide higher level "building blocks." Recent work also attempts to identify the emotional attitude of the human interlocutor (Forbes-Riley and Litman, 2011)
From page 42...
... The raw data can be in structured format -- for example, spatial and temporal information in the form of GPS data for vehicles, or location information of mobile phone users. It can be in semistructured or unstructured text format, such as machine logs or tweets and blogs on social media.
From page 43...
... The physical world itself is fast becoming a type of information system: Networked sensors are being embedded in devices ranging from mobile phones, smart energy meters, and cars to personal health monitoring devices and industrial machines that can sense, create, and communicate data about the state of the physical world. As most sensor data are monitoring some aspects of the physical world, "cyber-physical-aware" analytics algorithms that can leverage physical constraints (e.g., temporal, spatial)
From page 44...
... This need is further exacerbated and complicated by the fact that the machines translate data from the real world through sensors and computers that often must process or delay the raw data. Such issues are the primary reason that human decision makers are needed, particularly in networked environments, to resolve uncertainties that result.
From page 45...
... Recent work has explored the development of metric classes for human interaction with automated systems. Much of the following discussion follows Cummings, Pina, and Donmez (2008)
From page 46...
...  Autonomous Platform Behavior Efficiency – For example, usability, adequacy, autonomy, learnability, errors, user satisfaction, automation speed, accuracy and reliability, neglect time.  Human Behavior Efficiency – Operators perform multiple tasks such as monitoring autonomous platform health and status, identifying critical exogenous events, and communicating with others as needed.
From page 47...
... . In addition to measuring team coordination for the human-human metric subclass, assessing team cognition, which refers to the thoughts and knowledge of the team, can be valuable in evaluating team performance and identifying effective training and design interventions (Fiore and Schooler, 2004)
From page 48...
... 48 Complex Operational Decision Making in Networked Systems of Humans and Machines which is usually not consciously measured while such decisions are debated. What metrics can be used to capture the large-scale impacts of important decisions?


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