Complex Dynamical Systems

The study of health behavior change, including nutrition and physical activity behaviors, has been rooted in a cognitive-rational paradigm. Change is conceptualized as a linear, deterministic process where individuals weigh pros and cons, and at the point at which the benefits outweigh the cost change occurs. Consistent with this paradigm, the associated statistical models have almost exclusively assumed a linear relationship between psychosocial predictors and behavior. Such a perspective however, fails to account for non-linear, quantum influences on human thought and action. Consider why after years of false starts and failed attempts, a person succeeds at increasing their physical activity, eating healthier or losing weight. Or, why after years of success a person relapses. This paper discusses a competing view of health behavior change that was presented at the 2006 annual ISBNPA meeting in Boston. Rather than viewing behavior change from a linear perspective it can be viewed a...

Genes and proteins form complex dynamical systems or gene regulatory networks (GRN) that can reach several steady states (attractors). These may be associated with distinct cell types. In plants, the ABC combinatorial model establishes the necessary gene combinations for floral organ cell specification. We have developed dynamic gene regulatory network (GRN) models to understand how the combinatorial selection of gene activity is established during floral organ primordia specification as a result of the concerted action of ABC and non-ABC genes. Our analyses have shown that the floral organ specification GRN reaches six attractors with gene configurations observed in primordial cell types during early stages of flower development and four that correspond to regions of the inflorescence meristem. This suggests that it is the overall GRN dynamics rather than precise signals that underlie the ABC model. Furthermore, our analyses suggest that the steady states of the GRN are robust to random alterations of the logical functions that define the gene interactions. Here we have updated the GRN model and have systematically altered the outputs of all the logical functions and addressed in which cases the original attractors are recovered. We then reduced the original three-state GRN to a two-state (Boolean) GRN and performed the same systematic perturbation analysis. Interestingly, the Boolean GRN reaches the same number and type of attractors as reached by the three-state GRN, and it responds to perturbations in a qualitatively identical manner as the original GRN. These results suggest that a Boolean model is sufficient to capture the dynamical features of the floral network and provide additional support for the robustness of the floral GRN. These findings further support that the GRN model provides a dynamical explanation for the ABC model and that the floral GRN robustness could be behind the widespread conservation of the floral plan among eudicotyledoneous plants. Other aspects of evolution of flower organ arrangement and ABC gene expression patterns are discussed in the context of the approach proposed here.

The psychological self is often treated as a mental structure which enables a person to act individually and consciously, In our view, the self can only be kept non-paradoxical if it is seen as a process with homeostatic attributes. We therefore discuss a self concept derived from the theory of complex dynamical systems. The dynamical perspective makes us lay an emphasis on the recursion implicated by the operator 'self' marking the application of cognition to cognition. We argue that from this recursive dynamics the self arises as a processual gestalt via self-organization. We can then link our concept of self to psychological observations which show that the self is maintained by a rhythmical series of calibrating events (self-reflections and social interactions). Implications for psychopathology (schizophrenia theory) and interpersonal relationships (transference. love) are outlined.

This paper is a preliminary to constructing a pragmatic theory of "ba" ("field, place, space, situation", etc.) (Nishida 1953; Jodo 2003; Shimizu 1995). Using the conceptual framework developed by William F. Hanks (2016a, 2016b), who synthesizes Nishida's philosophy and practice theories, I illustrate the framework with five interactions in Yucatec Maya and Japanese. In particular it is shown that the improvisational model (Shimizu 1995) offers a useful analytic lens in the analysis. It is argued that the pragmatics of "ba" is an inherently multi-disciplinary project, in which the analyst proposes falsifiable hypotheses in the paradigm of complexity science (Boyd and Richerson 2006; Kauffman 1995; Larsen-Freeman and Cameron 2008, among others).

Young Children's Knowledge About the Moon: A Complex Dynamic System Grady J. Venville & Robert D. Louisell & Jennifer A. Wilhelm © Springer Science+Business Media BV 2011 ... The researchers cite the research of Siegal et al. (2004) in Australia and Schoultz et al. ...

H. Papadopoulos, AS Andreou, and M. Bramer (Eds.): AIAI 2010, IFIP AICT 339, pp. 45–52, 2010. © IFIP International Federation for Information Processing 2010 ... Α Generic Tool for Building Fuzzy Cognitive Map ... Maria Papaioannou1, Costas Neocleous2, Anastasis ...

—Complex dynamical networks can often be analysed as the interconnection of subsystems: this allows to considerably simplify the model and better understand the global behaviour. Some biological networks can be conveniently analysed as aggregates of monotone subsystems. Yet, monotonicity is a strong requirement: it relies on the knowledge of the state representation and imposes a severe restriction on the Jacobian (which must be a Metzler matrix). Systems with a Monotonic Step Response (MSR), which include input-output monotone systems as a special case, are a broader class and still have interesting features. The property of having a monotonically increasing step response (or equivalently, in the linear case, a positive impulse response) can be evinced from experimental data, without an explicit model of the system. We consider networks that can be decomposed as aggregates of MSR subsystems and we provide a structural (parameter-free) classification of oscillatory and multistationary behaviours. The classification is based on the exclusive or concurrent presence of negative and positive cycles in the system aggregate graph, whose nodes are the MSR subsystems. The result is analogous to our earlier classification for aggregates of monotone subsystems. Models of biomolecular networks are discussed to demonstrate the applicability of our classification, which helps build synthetic biomolecular circuits that, by design, are well suited to exhibit the desired dynamics.