Stochastic dynamics Research Papers - Academia.edu (original) (raw)

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- Decision Making, Operations Management, Mathematical Programming, Container Terminal
- by Donna Lee
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- Bioeconomics, Invasive Species, Technology Adoption, Benefit Cost Analysis
- by Roy Cerqueti and +1
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- Applied Mathematics, Monte Carlo Simulation, Profitability, Stochastic dynamics
- by Marcello Vasta
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- Civil Engineering, Stochastic dynamics, Computational Method
- by Yasin Polat
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- Mechanical Engineering, Condensed Matter Physics, Magnetic field, Ising Model
- by Andrew Torda
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- Molecular Simulation, The, Path Integral Dynamics, Stochastic dynamics
- by Roland Malhamé and +1
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- Cognitive Science, Applied Mathematics, Optimal Control, Statistical Physics

- by Pierre Malgrange
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- General Equilibrium, Economic analysis, DSGE Model, Stochastic dynamics
- by Christopher Otrok and +1
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- Economics, General Equilibrium, Monetary Policy, Developing Country
- by Lora Billings
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- Applied Mathematics, Stochastic Process, Nonlinear dynamics, Pattern Formation
- by Fred Benth and +1
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- Mechanical Engineering, Energy Economics, Risk Management, Applied Economics
- by Raul Toral
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- Stochastic Process, Phase transition, Stochastic dynamics, Indexation

This paper is a contribution to the forthcoming Edward Elgar Handbook of the History of Economic Analysis volume edited by Gilbert Faccarello and Heinz Kurz. Its aim is to introduce the reader to the main episodes that have marked the course of modern macroeconomics: its emergence after the publication of Keynes’s General Theory, the heydays of Keynesian macroeconomics based on the IS-LM model, disequilibrium and non-Walrasian equilibrium modelling, the invention of the natural rate of unemployment notion, the new classical attack against Keynesian macroeconomics, the first wave of new Keynesian models, real business cycle modelling and, finally, the second wage of new Keynesian models, i.e. DSGE models. A main thrust of the paper is the contrast we draw between Keynesian macroeconomics and stochastic dynamic general equilibrium macroeconomics. We hope that our paper will be useful for teachers of macroeconomics wishing to complement their technical material with a historical addendum.

- by michel de vroey
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- Economics, General Equilibrium, Economic analysis, DSGE Model
- by Hans Amman
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- Business Administration, Mathematics, Economics, Research Methodology
- by David Wingate
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- Kalman Filter, Latent variable, Stochastic dynamics, Discrete Time Systems

This paper investigates the stochastic behaviour of hydrodynamic journal bearings by solving the Reynolds equation with random parameters numerically based on finite difference method. The steady state and dynamic characteristics are quantified considering random variabilities in eccentricity and surface roughness that can closely simulate the uncertain service conditions and inevitable manufacturing imperfections. Based on efficient radial basis function, a Monte Carlo simulation (MCS) algorithm is developed in conjunction with the governing equations for quantifying stochastic characteristics of the crucial performance parameters concerning hydrodynamic bearings. Relative sensitivity to stochasticity for different performance parameters is analysed. Physically insightful new results are presented in a probabilistic framework, wherein it is observed that the stochasticity has pronounced influence on the performance of bearing.

- by TM TM
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- Stochastic dynamics, Journal Bearing

In this short note we investigate the natur of the phase transitions in a spin market model as a function of the interaction strength between local and global effects. We find that the stochastic dynamics of this stylized market model exhibit a periodicity whose dependence on the coupling constant in the Ising-like Hamiltonian is robust to changes in the temperature and the size of the market.

- by Ted Theodosopoulos
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- Phase transition, Stochastic dynamics, Renewal Process, Coupling Constant

Elementary processes in astrophysical phenomena traditionally attract researchers attention. At first this can be attributed to a group of chemi-ionization processes in Rydberg atom collisions with ground state parent atoms. This processes might be studied as a prototype of the elementary process of the radiation energy transformation into electrical one. The studies of nonlinear mechanics have shown that so called regime of dynamic chaos should be considered as typical, rather than exceptional situation in Rydberg atoms collision. From comparison of theory with experimental results it follows that a such kind of stochastic dynamic processes, occurred during the single collision, may be observed.

- by Nenad Sakan
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- Stochastic dynamics, Atomic Absorption
- by Qishao Lu
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- Mechanical Engineering, Time Series, Timing Analysis, Nonlinear dynamics
- by christian maes
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- Mathematical Physics, Stochastic Process, Thermodynamics, Stochastic analysis
- by Paolo Pra
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- Applied Mathematics, Robust Control (Mathematics), Stochastic Games, Stochastic Control
- by Kostas Spyrou
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- Engineering, Monte Carlo, Numerical Simulation, Mathematical Sciences
- by David Galas
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- Genetics, Stochastic Process, Molecular Biology, System Biology
- by Daniel Gulli
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- Decision Making, Operations Management, Mathematical Programming, Container Terminal
- by Tim Maudlin
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- Philosophy, Quantum Mechanics, Probability, Bohmian Mechanics

The aim of the present research is to use a model economy built for Brazil, based on an optimizing dynamic general equilibrium model, in order to perform numerical simulations to derive the ability of the artificial economy to explain the impact of monetary policy interventions on Brazilian short run economic performance in terms of the inflation rate, output gap, interest

- by Marcelo Muinhos
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- Stochastic Process, Sensitivity Analysis, Monetary Policy, Numerical Simulation
- by Dimitri Volchenkov
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- Power Law, Stochastic dynamics, Multiplicative noise
- by Laurent Cordier and +1
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- Mechanical Engineering, Aerospace Engineering, Statistical Inference, Control Design
- by Subrata Chakraborty
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- Engineering, Modal Analysis, Finite element modelling, Physical sciences
- by Pedro G. Lind
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- Engineering, Stochastic Process, Complex System, Mathematical Sciences
- by Ihor Lubashevsky
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- Data Collection, Traffic Flow, Stochastic dynamics
- by Ira Schwartz
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- Population Dynamics, Stochastic processes, Biological Sciences, Humans
- by Francesco Pellicano
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- Engineering, Modeling, Fluid structure interaction, Potential Theory

After-sales service is a major profit generator for more and more OEMs in industries with durable products. Successful engagement in after-sales service improves customer loyalty and allows for competitive differentiation through superior service like an extended service period after end of production during which customers are guaranteed to be provided with service parts. In order to fulfill the service guarantee

- by Rainer Kleber
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- Customer Loyalty, Business and Management, Inventory Management, Profitability
- by David Dingli
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- Stem Cells, Molecular Evolution, Stem Cell, Stochastic processes

We present the latest version of the Groningen Molecular Simulation program package, GROMOS05. It has been developed for the dynamical modelling of (bio)molecules using the methods of molecular dynamics, stochastic dynamics, and energy minimization. An overview of GROMOS05 is given, highlighting features not present in the last major release, GROMOS96. The organization of the program package is outlined and the included analysis package GROMOS++ is described. Finally, some applications illustrating the various available functionalities are presented.

- by Riccardo Baron
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- Algorithms, Computational Chemistry, Software, Software Design

Natural systems compute intrinsically and produce information. The organization of a stochastic dynamical system is reflected in the time series of observations made of the system and can be quantified by the excess entropy or predictive information—the mutual information between past and future. This information can be used to build models of vary- ing complexity that capture the causal structure

- by James Crutchfield
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- Time Series, Causal Inference, Mutual Information, Stochastic dynamics

We present results about financial market observables, specifically returns and traded volumes. They are obtained within the current nonextensive statistical mechanical framework based on the entropy Sq=kfrac1−sumi=1Wpiq1−q,(qinRe)left(S1equivSBG=−ksumi=1Wpi,lnpiright)S_{q}=k\frac{1-\sum_{i=1}^{W} p_{i} ^{q}}{1-q}\, (q\in \Re) \left(S_{1} \equiv S_{BG}=-k\sum_{i=1}^{W}p_{i}\,\ln p_{i}\right)Sq=kfrac1−sumi=1Wpiq1−q,(qinRe)left(S1equivSBG=−ksumi=1Wpi,lnpiright) . More precisely, we present stochastic dynamical mechanisms which mimic probability density functions empirically observed. These mechanisms provide possible interpretations for the emergence of the entropic indices q in the time evolution of the corresponding observables. In addition to this, through multi-fractal analysis of return time series, we verify that the dual relation qstat+qsens=2 is numerically satisfied, qstat and qsens being associated to the probability density function and to the sensitivity to initial conditions respectively. This type of simple relation, whose understanding remains ellusive, has been empirically verified in various other systems.

- by Jeferson De Souza
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- Condensed Matter Physics, Thermodynamics, Time Series, Statistical Physics
- by Ricardo Lopes
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- Multivariate Statistics, Statistical Analysis, Eutrophication, Biological Sciences