## 2 Complexity and Teamwork
### Complexity and Complex Systems  
The complexity theory has found application in many knowledge domains where it usually dispensed valuable methodological and theoretical insight [8; 18; 19]. Herein, based on the available literature, we discuss the main complexity properties related to networks and teamwork. Complex systems are inherently incomplete ensembles of entities interacting without distinctive borders, in a non-linear nature, without an accurate (or even, perfect) representation of the system, and with direct and indirect cycles of feedback [12]. These arguments claim that an unavoidable limitation of the representation should be acknowledged [20]. Literature recognises the challenges of identifying future behaviours of a complex system. Such behaviours typically arise from six different complexity science concepts: irreversibility of time, path dependence, sensitivity to initial conditions, emergence and systemness, attractor states, and complex causation [21]. We use such six concepts as a baseline for our discussion and we deeply explain them subsequently in the paper. Taking into account the concept of time irreversibility, complexity science offers the scientific foundations on which to build the recognition of the plurality of the future and the uncertainty related to this. It recognises that there is no simple relationship between a past trajectory through time and the present situations running to a single identifiable future. Partially related to this concept is path dependence, which is a consequence of nonlinearity and signifies that rules and past interactions impact the future. In other words, the uncertainties that we face can be limited since they are influenced by previous events and we can have early, at least weak, signals on future events by examining the present. The concept of sensitivity to initial conditions is linked to the idea of weak signals that are early warnings referred to a certain trend. One of the most familiar related concepts is the \"butterfly effect\", which is a powerful and simple image of how small initial differences in the starting states can provoke very different outcomes. The emergence is perhaps one of the most widely known properties [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. This phenomenon is related to a rise in a variety of macroscopic states and systemic properties that are unexpected a priori only by the entities in the system. It is a function of hierarchical levels of reality that both affects and is affected by the others; moreover, it indicates the occurrence of new and unexpected events. Thus, it is important to consider the behaviour at the micro and macro level. Complex systems are exposed to the attractor states. This means that all paths of development that fall within the basin of the attractor have the same future outcome, implying a reiteration of past patterns of development leading to the same outcome. Examples are local cycles of recurrence in stable systems that return into their periods after a perturbation. A complex causation of events involves more than a single event that influences a cause-effect another event through concomitance. It is related to the concept of self-organised critical systems [12] that are conceived to behave out of equilibrium towards a critical point as an attractor. The entities in the system manage to independently find the critical point by means of non-linearity deriving from interactions between causal factors that lead to an unexpected future. These six properties highlight the fact that such systems cannot be described considering an analytical paradigm; thus, it is not possible to make predictions. Indeed, in complex systems, the interactions are determined by a list of rules regarding how the dynamics of the system update its states and future interactions, which then lead to new constraints on the dynamic at the next step [12].