Connectivity, the control parameter in a nonlinear dynamics model of team performance is mathematically linked to the ratio of positivity to negativity (P/N) in team interaction. By knowing the P/N ratio it is possible to run the nonlinear dynamics model that will portray what types of dynamics are possible for a team. These dynamics are of three types: point attractor, limit cycle, and complexor (complex order, or “chaotic ” in the mathematical sense). Low performance teams end up in point attractor dynamics, medium perfomance teams in limit cycle dynamics, and high performance teams in complexor dynamics. Keywords: positivity; connectivity; team performance; nonlinear dynamics Positive organizational scholars have made an explicit call for the use of nonlinear models stating that their field “is especially interested in the nonlinear positive dynamics... that are frequently associated with positive organizational phenomena ” (Cameron, Dutton, & Quinn, 2003, pp. 4-5). This article answers this call by showing how a nonlinear dynamics model, the meta learning (ML) model, developed and validated against empirical time series data of business

This paper begins to answer the call to broaden current theories of individual decision-making by including in them the effects of human mood. Grounding our arguments in psychological literature on the effects of mood on information processing, motivation, and decision heuristics, we develop hypotheses about how mood can significantly affect individuals' use of structured decision protocols. In support of our hypotheses, results from an experimental study of complex decision-making suggest that, in situations where a structured decision protocol is the usual method of decision-making, individuals in moderately negative moods are significantly more likely than those in moderately positive moods to: (1) carefully execute all the steps of a structured decision protocol, (2) execute the steps of a structured decision protocol in the correct order, and (3) rely on the outcome of the structured decision protocol as the primary basis for the decision. We discuss these findings in terms of the...

The question for many designers of intelligent systems is no longer whether to incorporate emotion and motivation, but how to do so. This question has strong consequences for the autonomy and flexibility of a system and its ability to respond to novel, unpredictable or changing environments – in other words, the degree of intelligence it will display. Autonomous agents that learn through situated embodiment in a rich and changing environment may require a very different approach to emotion and motivation than systems in which procedures are written to cover all contingencies and choices are made among known alternatives, albeit using a sophisticated decision process. Here, we dispute Sloman’s suggestion that emotion is an emergent control state and thus not a component of a system architecture. We also propose ways in which an emotion mechanism can enhance intelligence in autonomous agents. Dennett (2001) states: “…recent empirical and theoretical work in cognitive science strongly suggests that emotions are so valuable in the real-time control of our rationality that an embodied robot would be well advised to be equipped with artificial emotions…”. Recent examples of emotional robots include Breazeal