Given that a negotiation outcome is determined to a large extent by the successive offers exchanged by negotiating agents, it is useful to analyze dynamic patterns of the bidding, what Raiffa calls the "negotiation dance". Patterns in such exchanges may provide additional insight into the strategies used by the agents. The current practice of evaluating a negotiation strategy, however, is to primarily focus on fairness and quality aspects of the agreement. There is a lack of tools and methods that facilitate a precise analysis of the negotiation dynamics. To fill this gap, this paper introduces a method for analysis based on a classification of negotiation steps.The method provides the basic tools to perform a detailed and quantified analysis of a negotiation between two agents in terms of dynamic properties of the negotiation trace. The method can be applied to well-designed tournaments, but can also be used to analyze single 1-on-1 negotiation.Example findings of applying the method to analyze the ABMP and Trade-Off strategies show that sensitivity to the preferences of the opponent is independent, respectively dependent, on a correct model of that opponent. Furthermore, the results illustrate that having domain knowledge is not always enough to avoid making unintentional steps.

Cognitive work analysis (CWA) as an analytical approach for examining complex sociotechnical systems has shown success in modelling the work of single operators. The CWA approach incorporates social and team interactions, but a more explicit analysis of team aspects can reveal more information for systems design. In this paper, Team CWA is explored to understand teamwork within a birthing unit at a hospital. Team CWA models are derived from theories and models of teamworkand leverage the existing CWA approaches to analyse team interactions. Team CWA is explained and contrasted with prior approaches to CWA. Team CWA does not replace CWA, but supplements traditional CWA to more easily reveal team information. As a result, Team CWA may be a useful approach to enhance CWA in complex environments where effective teamwork is required.Practitioner Summary: This paper looks at ways of analysing cognitive work in healthcare teams. Team Cognitive Work Analysis, when used to supplement traditional Cognitive Work Analysis, revealed more team information than traditional Cognitive Work Analysis. Team Cognitive Work Analysis should be considered when studying teams

In 2011, IBM's Watson competed on the game show Jeopardy! winning against the two best players of all time, Brad Rutter and Ken Jennings (Ferrucci et al. 2010). Since this demonstration, IBM has expanded its research program in artificial intelligence (AI), including the areas of natural language processing and machine learning (Kelly and Hamm 2013). Ultimately, IBM sees the opportunity to develop cognitive computing -a unified and universal platform for computational intelligence (Modha et al. 2011). But how might cognitive computing work in real environmentsand in concert with people?In 2013, our group within IBM Research started to explore how to embed cognitive computing in physical environments. We built a Cognitive Environments Laboratory (CEL) (see figure 1) as a living lab to explore how people and cognitive computing come together.Our effort focuses not only on the physical and computational substrate, but also on the users' experience. We envision a fluid and natural interaction that extends through time across multiple environments (office, meeting room, living room, car, mobile). In this view, cognitive computing systems are always on and available to engage with people in the environment. The system appears to follow individual users, or groups of users, as they change environments, seamlessly connecting the users to available input and output devices and extending their reach beyond their own cognitive and sensory abilities.We call this symbiotic cognitive computing: computation that takes place when people and intelligent agents come together in a physical space to interact with one another. The intelligent agents use a computational substrate of "cogs" for visual object recognition, natural language parsing, probabilistic decision support, and other functions. the book The Society of Mind where Marvin Minsky likened agents to "cogs of great machines" (Minsky 1988). These cogs are available to intelligent agents through programmatic interfaces and to human participants through user interfaces.Our long-term goal is to produce a physical and computational environment that measurably improves the performance of groups on key tasks requiring large amounts of data and significant mental effort, such as information discovery, situational assessment, product design, and strategic decision making. To date, we have focused specifically on building a cognitive environment, a physical space embedded with cognitive computing systems, to support business meetings for strategic decision making. Other applications include corporate meetings exploring potential mergers and acquisitions, executive meetings on whether to purchase oil fields, and utility company meetings to address electrical grid outages. These meetings often bring together a group of participants with varied roles, skills, expertise, and points of view. They involve making decisions with a large number of high-impact choices that need to be evaluated on multiple dimensions taking into account large amounts of structured and unstructured data.While...

Today's complex sociotechnical systems involve more than just single operators and often include rich team interactions. In this article, the authors explore cognitive work analysis (CWA), a method typically used for deriving design requirements to support single operators, and rework this methodology into a version that is intended to show requirements required to support successful team collaboration. Team-suitable approaches are presented for the first two steps of CWA, work domain analysis and control task analysis. These approaches are collected from past attempts to model teams with CWA and two new teamwork visualizations, collaboration tables and decision wheels. The applicability of the extended models is demonstrated with a health care example.

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