The intuitive difference between a system that choreographs the motion of its parts in the service of goals of its own formulation and a system composed of a collection of parts doing their own thing without coordination has been shaken by now familiar examples of self-organization. There is a broad and growing presumption in parts of philosophy and across the sciences that the appearance of centralized information-processing and control in the service of system-wide goals is mere appearance, i.e., an explanatory (...) heuristic we have evolved to predict behavior, but one that will eventually get swept away in the advancing tide of self-organization. I argue that there is a distinction of central importance here, and that no adequate science of complex systems can dispense with it. (shrink)

This article considers the implications of complex systems models for the study of economics and the evaluation of public policies. I argue that complexity can enhance current approaches to formal economic analysis, but does so in ways that complement current approaches. I further argue that while complexity can influence how public policy analysis is conducted, it does not delimit the use of consequentialist approaches to policy comparison to the degree initially suggested by Hayek and most recently defended by Gaus.

In this article an epistemological framework is proposed in order to integrate the emergentist thought with systemic studies on biological autonomy, which are focused on the role of organization. Particular attention will be paid to the role of the observer’s activity, especially: (a) the different operations he performs in order to identify the pertinent elements at each descriptive level, and (b) the relationships between the different models he builds from them. According to the approach sustained here, organization will be considered (...) as the result of a specific operation of identification of the relational properties of the functional components of a system, which do not necessarily coincide with the intrinsic properties of its structural constituents. Also, an epistemological notion of emergence—that of “complex emergence”—will be introduced, which can be defined as the insufficiency, even in principle, of a single descriptive modality to provide a complete description of certain classes of systems. This integrative framework will allow us to deal with two issues in biological and emergentist studies: (1) distinguishing the autonomy proper of living systems from some physical processes like those of structural stability and pattern generation, and (2) reconsidering the notion of downward causation not as a direct or indirect influence of the whole on its parts, but instead as an epistemological problem of interaction between descriptive domains in which the concept of organization proposed and the observational operations related to it play a crucial role. (shrink)

Cellular automata (henceforth: CA) are discrete, abstract computational systems that have proved useful both as general models of complexity and as more specific representations of non-linear dynamics in a variety of scientific fields. Firstly, CA are (typically) spatially and temporally discrete: they are composed of a finite or denumerable set of homogeneous, simple units, the atoms or cells. At each time unit, the cells instantiate one of a finite set of states. They evolve in parallel at discrete time steps, following (...) state update functions or dynamical transition rules: the update of a cell state obtains by taking into account the states of cells in its local neighborhood (there are, therefore, no actions at a distance). Secondly, CA are abstract, as they can be specified in purely mathematical terms and implemented in physical structures. Thirdly, CA are computational systems: they can compute functions and solve algorithmic problems. Despite functioning in a different way from traditional, Turing machine-like devices, CA with suitable rules can emulate a universal Turing machine, and therefore compute, given Turing's Thesis, anything computable.... (shrink)