Cramer et al.'s article is an example of the fruitful application of complex dynamic systems theory. We extend their approach with examples from our own work on development and developmental psychopathology and address three issues: (1) the level of aggregation of the network, (2) the required research methodology, and (3) the clinical and educational application of dynamic network thinking.

The real-life complexity of moral motivation can be examined and explained by reintegrating time and development into moral inquiry. This article is one of the possible integrative steps in this direction. A dynamic developmental conception of moral motivation can be a useful bridge toward such integration. A comprehensive view of moral motivation is presented. Moral motivation is reconceptualized as a developmental process of self-organization and self-regulation out of which moral judgment and action emerge through the interplay of dynamically intertwined cognitive (...) and emotional components. Moral identity is proposed to emerge from long-term self-organization of moral motivation. In turn, as a higher-order construct, moral identity has a top-down influence on real-time self-organization. The article includes an account of short-term changes in moral motivation, and an account that connects real-time moral functioning with long-term changes. Moral motivation is qualified as a dynamic developmental process on the basis of self-organization, multicausality, nonlinearity, interconnectedness of time scales and substantial intrapersonal variability through motivational pluralism. (shrink)

The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to (...) arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy. (shrink)