This paper illustrates how fluctuations in aggregate economic activity can result from many small, independent shocks to individual sectors. The e#ects of the small independent shocks fail to cancel in the aggregate due to the presence of two non standard assumptions: local interaction between productive units (linked by supply relationships), and non-convex technology. We also argue that neither feature on its own would su#ce. In the case of a simple model, we explicitly calculate the distribution of aggregate activity in the limit of an infinite number of independent disturbed sectors. 1 Introduction Explaining the observed instability of economic aggregates is a long-standing puzzle for economic theory. A number of possible reasons for variation in the pace of production are easily given, such as stochastic variation in the timing of households' desired consumption of produced goods, or stochastic variation in the costs of production. But it is hard to see why there should...

Synchrony among multicortical EEGs 2 Freeman, Gaál & Jörnsten Information transfer and integration among functionally distinct areas of cerebral cortex of oscillatory activity requires some degree of phase synchrony of the trains of action potentials that carry the information prior to the integration. However, propagation delays are obligatory. Delays vary with the lengths and conduction velocities of the axons carrying the information, causing phase dispersion. In order to determine how synchrony is achieved despite dispersion, we recorded EEG signals from multiple electrode arrays on five cortical areas in cats and rabbits, that had been trained to discriminate visual or auditory conditioned stimuli. Analysis by time-lagged correlation, multiple correlation and PCA, showed that maximal correlation was at zero lag and averaged.7, indicating that 50 % of the power in the gamma range among the five areas was at zero lag irrespective of phase or frequency. There were no stimulus-related episodes of transiently increased phase locking among the areas, nor EEG "bursts " of transiently increased amplitude above the sustained level of synchrony. Three operations were identified to account for the sustained correlation. Cortices broadcast their outputs over divergent-convergent axonal

To explain how stimuli cause consciousness, we have to explain causality. We can't trace linear causal chains from receptors after the first cortical synapse, so we use circular causality to explain neural pattern formation by self-organizing dynamics. But an aspect of intentional action is causality, which we extrapolate to material objects in the world. Thus causality is a property of mind, not matter.

prediction is made for clinical testing that wave packets are precursor to states of awareness. They are not by themselves accessible to experience, as may be the macroscopic states initiated by global state transitions. Keywords: EEG; meaning; mesoscopic neurodynamics; phase cone; state transition; wave packet. 1. Introduction Brain systems operate on many levels of organization, each with its own scales of time and space. Dynamics applies to every level from the atomic to the molecular, and from macromolecular organelles to the neurons that incorporate them. In turn neurons form populations, these form the subassemblies in brains, and so on to embodied brains interacting intentionally with material, interpersonal, and social environments. Each level is macroscopic to that below it and microscopic to that above it. Among the most di#cult tasks scientists face are those of conceiving and describing the exchanges between levels, seeing that the measures of time 3 and distance ar

This paper studies static complexity in manufacturing systems. We enumerate factors influencing static complexity, and define a static complexity measure in terms of the processing requirements of parts to be produced and machine capabilities. The measure suggested for static complexity in manufacturing systems needs only the information available from production orders and process plans. The variation in static complexity is studied with respect to part similarity, system size, and product design changes. Finally, we present relationships between the static complexity measure and system performance. 1 Introduction Manufacturing systems are often described as being complex [Pritsker, 1990, Lin, 1993]. The dynamic nature of the manufacturing environment greatly increases the number of decisions that need to be made and system integration makes it difficult to predict the effect of a decision on future system performance. In fact, Upton [Upton, 1988] observes that many integrate...

Despite its current popularity, “emergence” is a concept with a venerable history and an elusive, ambiguous standing in contemporary evolutionary theory. This paper briefly recounts the history of the term and details some of its current usages. Not only are there radically varying interpretations about what emergence means but “reductionist ” and “holistic ” theorists have very different views about the issue of causation. However, these two seemingly polar positions are not irreconcilable. Reductionism, or detailed analysis of the parts and their interactions, is essential for answering the “how ” question in evolution--how does a complex living system work? But holism is equally necessary for answering the “why ” question-- why did a particular arrangement of parts evolve? In order to answer the “why ” question, a broader, multi-leveled paradigm is required. The reductionist approach to explaining emergent complexity has entailed a search for underlying “laws of emergence.” Another alternative is the “Synergism Hypothesis, ” which focuses on the “economics ” – the functional effects produced by emergent wholes and their selective consequences. This theory, in a nutshell, proposes that the synergistic (co-operative) effects produced by various combinations of parts have played a major causal role in the evolution of biological complexity. It will also be argued that emergent phenomena represent, in effect, a subset of a much larger universe of combined effects in the natural world; there are many different kinds of synergy, but not all synergies represent emergent phenomena.

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l networks, and that recording wave activity is equivalent to observing an engine with a stethoscope or a computer with a D'Arsonval galvanometer. However, one can learn a lot about a system by listening and watching, if one knows what to seek and find. Numerous recent studies of the behavioral correlates of so-called "unit activity" of single neurons in sensory and motor systems have shown that the carrier of behaviorally significant information is not the pulse train of the single neuron, but instead the organized activity of arrays of neurons (see review in Note 3.7 in Freeman 1995). How many neurons are needed to make an array? Does the number exceed the number that can be accessed by current methods of recording pulse trains (on the order of 100)? Where do they form, what fractions of neurons in local neighborhoods suffice, and how are their outputs selectively read by their targets of transmission? In my view these questions have no answers, because the objects of their inquiry

This paper describes the incremental development of a model for interactive music – music instantiated in real-time on the basis of local performance and environmental information. Music is understood as a dynamical complex of interacting situated embodied behaviours. These behaviours may be physical or virtual, composed or emergent, or of a time scale such that they figure as constraints or constructs. All interact in the same space by a process of mutual modelling, redescription, and emergent restructuring. The model is implemented as a complex adaptive system in the Swarm simulation environment. 1.1 A View of Music This paper presents a model for interactive music composition and performance – music instantiated during performance on the basis of stored programs and materials, and performance and environmental information. The model is predicated on a view of musical activity as a distributed, situated embodied phenomenon. Each event on the musical surface is the trace of a unique node in a tapestry of

ABSTRACT. It is argued that in order to solve complex problems we need a new approach, which is neither reductionistic nor holistic, but based on the entanglement of distinction and connection, of disorder and order, thus defining a science of complexity. A model of complex evolution is proposed, based on distributed variation through recombination and mutation, and selective retention of internally stable systems. Internal stability is then analysed through a generalized mathematical closure property. Examples of closure in self-organizing and cognitive systems are discussed. 1.