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Both practitioners and researchers in the field of project management have referred to problems caused by complexity or problems of particular significance to complex projects. In different scientific disciplines investigations into the behavior of complex dynamical systems are revealing insights that, taken together, amount to a challenge to the prevalent Cartesian/Newtonian/Enlightenment paradigm from which the practice of project management has emerged. Concepts such as nonlinearity, emergence, self-organization, and radical unpredictability have major implications for the uncodified paradigm that underpins project management practice and research. Taken together, they amount to a complementary way of thinking and talking about projects and their management that might shed new light on intractable problems that appear to plague certain areas of project management practice. One strand within complexity studies that holds particular promise is complex responsive processes of relating, a means of talking about how human beings interact and learn and how their interactions evolve over time and across space. A new program of research, of which this paper forms part, will apply this conceptual framework to the lived experience of project teams, including executive sponsors, project managers and project team members.

This paper reports on 115 pediatric patients who were treated for peritonsillar abscess at The Columbus Children's Hospital. Its purpose is to document the threefold increase of peritonsillar abscess between 1959 and 1978, relating this to the simultaneous decrease in the number of tonsillectomies by about one third. The paper further shows that 55 of 115 patients underwent successful treatment by immediate tonsillectomy with lower morbidity and shorter hospital stay when compared with 60 patients treated medically. The timing of the operation will also be discussed. We believe that immediate tonsillectomy is the treatment of choice.

This paper explores the compressibility of complex systems by considering the simplification of Boolean networks. A method, which is similar to that reported by Bastolla and Parisi,4,5 is considered that is based on the removal of frozen nodes, or stable variables, and network "leaves," i.e. those nodes with outdegree = 0. The method uses a random sampling approach to identify the minimum set of frozen nodes. This set contains the nodes that are frozen in all attractor schemes. Although the method can over-estimate the size of the minimum set of frozen nodes, it is found that the chances of finding this minimum set are considerably greater than finding the full attractor set using the same sampling rate. Given that the number of attractors not found for a particular Boolean network increases with the network size, for any given sampling rate, such a method provides an opportunity to either fully enumerate the attractor set for a particular network, or improve the accuracy of the random sampling approach. Indeed, the paper also shows that when it comes to the counting of attractors in an ensemble of Boolean networks, enhancing the random sample method with the simplification method presented results in a significant improvement in accuracy.

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1In the complexity and simulation communities there is growing support for the use of bottom-up computer-based simulation in the analysis of complex systems. The presumption is that because these models are more complex than their linear predecessors they must be more suited to the modeling of systems that appear, superficially at least, to be (compositionally and dynamically) complex. Indeed the apparent ability of such models to allow the emergence of collective phenomena from quite simple underlying rules is very compelling. But does this 'evidence' alone 'prove' that nonlinear bottom-up models are superior to simpler linear models when considering complex systems behavior? Philosophical explorations concerning the efficacy of models, whether they be formal scientific models or our personal worldviews, has been a popular pastime for many philosophers, particularly philosophers of science. This paper offers yet another critique of modeling that uses the results and observations of nonlinear mathematics and bottom-up simulation themselves to develop a modeling paradigm that is significantly broader than the traditional model-focused paradigm. In this broader view of modeling we are encouraged to concern ourselves more with the modeling process rather than the (computer) model itself and embrace a nonlinear modeling culture. This emerging view of modeling also counteracts the growing preoccupation with nonlinear models over linear models.