The philosophical conception of mechanistic explanation is grounded on a limited number of canonical examples. These examples provide an overly narrow view of contemporary scientific practice, because they do not reflect the extent to which the heuristic strategies and descriptive practices that contribute to mechanistic explanation have evolved beyond the well-known methods of decomposition, localization, and pictorial representation. Recent examples from evolutionary robotics and network approaches to biology and neuroscience demonstrate the increasingly important role played by computer simulations and mathematical representations in the epistemic practices of mechanism discovery and mechanism description. These examples also indicate that the scope of mechanistic explanation must be re-examined: With new and increasingly powerful methods of discovery and description comes the possibility of describing mechanisms far more complex than traditionally assumed.
Information-theoretic techniques have received much recent attention as tools for the analysis of embodied agents. However, while techniques for quantifying static information structure are well-established, the application of information theory to the analysis of temporal behavior is still in its infancy. Here we formulate a novel information-theoretic approach for analyzing the dynamics of information flow in embodied systems. To demonstrate our approach, we apply it to analyze a previously evolved model of relational categorization. The results of this analysis demonstrate the unique strengths of our approach for exploring the detailed structure of information dynamics, and point towards a natural synergy between temporally-extended information theory and dynamical systems theory.
A graphical chain modelling approach is used to study the determinants of neonatal and post-neonatal mortality in Malaysia. This approach provides an easily interpretable empirical description and illustrates explicitly the conditional independence structure between each pair of variables. The interpretation can be read directly from a mathematical graph. Besides examining the direct association of each determinant on mortality, we also examine the effects of socioeconomic determinants on intermediate determinants to understand the pathways through which the socioeconomic determinants affect the chance of mortality. The data analysed come from the second Malaysian Family Life Survey, fielded between August 1988 and January 1989.