646,524 research outputs found

    Chaos in credit–constrained emerging economies with Leontief technology

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    This work provides a framework to analyze the role of financial development as a source of endogenous instability in emerging economies subject to moral hazard problems. We study a piecewise linear dynamic model describing a small open economy with a tradable good produced by internationally mobile capital and a country specific production factor, using Leontief technology. We demonstrate that emerging markets could be endogenously unstable when large capital in–flows increase risk and exacerbate asymmetric information problems, according to empirical evidence. Using bifurcation and stability analysis we describe the properties of the system attractors, we assess the plausibility for complex dynamics and we find out that border collision bifurcations can emerge.border collision bifurcations,,complex dynamics,,emerging economies,,CEECs,,Endogenous instability,,moral hazard,,piecewise linear map.

    Non-monotonic long memory dynamics in black-market premia

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    The dynamic response of Black market premia to domestic shocks is an important issue in the design and implementation of stabilization and reform programs. We use a vector autoregressive fractionally integrated model to provide new evidence on the dynamics of the official and Black market exchange rates. We show that the official and Black market exchange rates in Hungary are cointegrated with a negative fractional order ofintegration in the cointegrating residuals. The new empirical finding means that the cointegrating residuals are positively autocorrelated in the short run due to autoregressive dynamics, but are negatively autocorrelated in the long run. The rich and complex dynamics of the premia suggests the existence of what we call long memory non-monotonicity.Foreign exchange rates ; Vector autoregression

    System dynamics advances strategic economic transition planning in a developing nation

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    The increasingly complex environment of today's world, characterized by technological innovation and global communication, generates myriads of possible and actual interactions while limited physical and intellectual resources severely impinge on decision makers, be it in the public or private domains. At the core of the decision-making process is the need for quality information that allows the decision maker to better assess the impact of decisions in terms of outcomes, nonlinear feedback processes and time delays on the performance of the complex system invoked. This volume is a timely review on the principles underlying complex decision making, the handling of uncertainties in dynamic envrionments and of the various modeling approaches used. The book consists of five parts, each composed of several chapters: I: Complex Decision Making: Concepts, Theories and Empirical Evidence II: Tools and Techniques for Decision Making in Complex Environments and Systems III: System Dynamics and Agent-Based Modeling IV: Methodological Issues V: Future Direction

    The Elusive Empirical Shadow of Growth Convergence

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    Two groups of applied econometricians have figured prominently in empirical studies of growth convergence. In terms of a popular caricature, one group believes it has found a black hat of convergence (evidence for growth convergence) in the dark room of economic growth, even though the hat may not exist (the task may be futile). A second group believes it has found a black coat of divergence (evidence against growth convergence) even though this object also may not exist (empirical reality, including the nature of growth divergence, is ever more complex than the models used to characterize it). The present paper seeks to light a candle to see whether there is a hat, a coat or another object of identifiable clothing in the room of regional and multi-country economic growth. After our examination, we find that the candle power of applied econometrics is too low to clearly distinguish a black hat in the huge dark room of economic growth. However, in our theory model, we find an important new role for heterogeneity over time and across economies in the transitional dynamics of economic growth; and, in our empirical work, these transitional dynamics reveal an elusive shadow of the conditional convergence hat in both US regional and inter-country OECD growth patterns.Convergence Parameter, Conditional Convergence, Economic Growth, Growth Convergence, Heterogeneity, Neoclassical Economics, Transition measures

    Competing contagion processes: Complex contagion triggered by simple contagion

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    Empirical evidence reveals that contagion processes often occur with competition of simple and complex contagion, meaning that while some agents follow simple contagion, others follow complex contagion. Simple contagion refers to spreading processes induced by a single exposure to a contagious entity while complex contagion demands multiple exposures for transmission. Inspired by this observation, we propose a model of contagion dynamics with a transmission probability that initiates a process of complex contagion. With this probability nodes subject to simple contagion get adopted and trigger a process of complex contagion. We obtain a phase diagram in the parameter space of the transmission probability and the fraction of nodes subject to complex contagion. Our contagion model exhibits a rich variety of phase transitions such as continuous, discontinuous, and hybrid phase transitions, criticality, tricriticality, and double transitions. In particular, we find a double phase transition showing a continuous transition and a following discontinuous transition in the density of adopted nodes with respect to the transmission probability. We show that the double transition occurs with an intermediate phase in which nodes following simple contagion become adopted but nodes with complex contagion remain susceptible.Comment: 9 pages, 4 figure

    Slow dynamics of the contact process on complex networks

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    The Contact Process has been studied on complex networks exhibiting different kinds of quenched disorder. Numerical evidence is found for Griffiths phases and other rare region effects, in Erd˝os RĂ©nyi networks, leading rather generically to anomalously slow (algebraic, logarithmic,...) relaxation. More surprisingly, it turns out that Griffiths phases can also emerge in the absence of quenched disorder, as a consequence of sole topological heterogeneity in networks with finite topological dimension. In case of scalefree networks, exhibiting infinite topological dimension, slow dynamics can be observed on tree-like structures and a superimposed weight pattern. In the infinite size limit the correlated subspaces of vertices seem to cause a smeared phase transition. These results have a broad spectrum of implications for propagation phenomena and other dynamical process on networks and are relevant for the analysis of both models and empirical data

    Empirical Confirmation of Creative Destruction from World Trade Data

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    We show that world trade network datasets contain empirical evidence that the dynamics of innovation in the world economy indeed follows the concept of creative destruction, as proposed by J.A. Schumpeter more than half a century ago. National economies can be viewed as complex, evolving systems, driven by a stream of appearance and disappearance of goods and services. Products appear in bursts of creative cascades. We find that products systematically tend to co-appear, and that product appearances lead to massive disappearance events of existing products in the following years. The opposite–disappearances followed by periods of appearances–is not observed. This is an empirical validation of the dominance of cascading competitive replacement events on the scale of national economies, i.e., creative destruction. We find a tendency that more complex products drive out less complex ones, i.e., progress has a direction. Finally we show that the growth trajectory of a country’s product output diversity can be understood by a recently proposed evolutionary model of Schumpeterian economic dynamics

    Non-linear models: applications in economics

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    The study concentrated on demonstrating how non-linear modelling can be useful to investigate the behavioural of dynamic economic systems. Using some adequate non-linear models could be a good way to find more refined solutions to actually unsolved problems or ambiguities in economics. Beginning with a short presentation of the simplest non-linear models, then we are demonstrating how the dynamics of complex systems, as the economic system is, could be explained on the base of some more advanced non-linear models and using specific techniques of simulation. We are considering the non-linear models only as an alternative to the stochastic linear models in economics. The conventional explanations of the behaviour of economic system contradict many times the empirical evidence. We are trying to demonstrate that small modifications in the standard linear form of some economic models make more complex and consequently more realistic the behaviour of system simulated on the base of the new non-linear models. Finally, few applications of non-linear models to the study of inflation-unemployment relationship, potentially useful for further empirical studies, are presented.non-linear model; continuous time map; strange attractor; fractal dimension; natural unemployment

    Voltage imaging of waking mouse cortex reveals emergence of critical neuronal dynamics.

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    Complex cognitive processes require neuronal activity to be coordinated across multiple scales, ranging from local microcircuits to cortex-wide networks. However, multiscale cortical dynamics are not well understood because few experimental approaches have provided sufficient support for hypotheses involving multiscale interactions. To address these limitations, we used, in experiments involving mice, genetically encoded voltage indicator imaging, which measures cortex-wide electrical activity at high spatiotemporal resolution. Here we show that, as mice recovered from anesthesia, scale-invariant spatiotemporal patterns of neuronal activity gradually emerge. We show for the first time that this scale-invariant activity spans four orders of magnitude in awake mice. In contrast, we found that the cortical dynamics of anesthetized mice were not scale invariant. Our results bridge empirical evidence from disparate scales and support theoretical predictions that the awake cortex operates in a dynamical regime known as criticality. The criticality hypothesis predicts that small-scale cortical dynamics are governed by the same principles as those governing larger-scale dynamics. Importantly, these scale-invariant principles also optimize certain aspects of information processing. Our results suggest that during the emergence from anesthesia, criticality arises as information processing demands increase. We expect that, as measurement tools advance toward larger scales and greater resolution, the multiscale framework offered by criticality will continue to provide quantitative predictions and insight on how neurons, microcircuits, and large-scale networks are dynamically coordinated in the brain
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