19,546 research outputs found
Scaling behavior in economics: II. Modeling of company growth
In the preceding paper we presented empirical results describing the growth
of publicly-traded United States manufacturing firms within the years
1974--1993. Our results suggest that the data can be described by a scaling
approach. Here, we propose models that may lead to some insight into these
phenomena. First, we study a model in which the growth rate of a company is
affected by a tendency to retain an ``optimal'' size. That model leads to an
exponential distribution of the logarithm of the growth rate in agreement with
the empirical results. Then, we study a hierarchical tree-like model of a
company that enables us to relate the two parameters of the model to the
exponent , which describes the dependence of the standard deviation of
the distribution of growth rates on size. We find that , where defines the mean branching ratio of the hierarchical tree and
is the probability that the lower levels follow the policy of higher
levels in the hierarchy. We also study the distribution of growth rates of this
hierarchical model. We find that the distribution is consistent with the
exponential form found empirically.Comment: 19 pages LateX, RevTeX 3, 6 figures, to appear J. Phys. I France
(April 1997
Scaling behavior in economics: I. Empirical results for company growth
We address the question of the growth of firm size. To this end, we analyze
the Compustat data base comprising all publicly-traded United States
manufacturing firms within the years 1974-1993. We find that the distribution
of firm sizes remains stable for the 20 years we study, i.e., the mean value
and standard deviation remain approximately constant. We study the distribution
of sizes of the ``new'' companies in each year and find it to be well
approximated by a log-normal. We find (i) the distribution of the logarithm of
the growth rates, for a fixed growth period of one year, and for companies with
approximately the same size displays an exponential form, and (ii) the
fluctuations in the growth rates -- measured by the width of this distribution
-- scale as a power law with , . We find
that the exponent takes the same value, within the error bars, for
several measures of the size of a company. In particular, we obtain:
for sales, for number of employees,
for assets, for cost of goods sold, and
for property, plant, & equipment.Comment: 16 pages LateX, RevTeX 3, 10 figures, to appear J. Phys. I France
(April 1997
Molecular dynamics simulations of oxide memristors: crystal field effects
We present molecular-dynamic simulations of memory resistors (memristors)
including the crystal field effects on mobile ionic species such as oxygen
vacancies appearing during operation of the device. Vacancy distributions show
different patterns depending on the ratio of a spatial period of the crystal
field to a characteristic radius of the vacancy-vacancy interaction. There are
signatures of the orientational order and of spatial voids in the vacancy
distributions for some crystal field potentials. The crystal field stabilizes
the patterns after they are formed, resulting in a non-volatile switching of
the simulated devices.Comment: 9 pages, 3 figure
Some Combinatorial Properties of Hook Lengths, Contents, and Parts of Partitions
This paper proves a generalization of a conjecture of Guoniu Han, inspired
originally by an identity of Nekrasov and Okounkov. The main result states that
certain sums over partitions p of n, involving symmetric functions of the
squares of the hook lengths of p, are polynomial functions of n. A similar
result is obtained for symmetric functions of the contents and shifted parts of
n.Comment: 20 pages. Correction of some inaccuracies, and a new Theorem 4.
Dynamics of Surface Roughening with Quenched Disorder
We study the dynamical exponent for the directed percolation depinning
(DPD) class of models for surface roughening in the presence of quenched
disorder. We argue that for dimensions is equal to the exponent
characterizing the shortest path between two sites in an
isotropic percolation cluster in dimensions. To test the argument, we
perform simulations and calculate for DPD, and for
percolation, from to .Comment: RevTex manuscript 3 pages + 6 figures (obtained upon request via
email [email protected]
A Landslide Climate Indicator from Machine Learning
In order to create a Landslide Hazard Index, we accessed rain, snow, and a dozen other variables from the National Climate Assessment Land Data Assimilation System. These predictors were converted to probabilities of landslide occurrence with XGBoost, a major machine-learning tool. The model was fitted with thousands of historical landslides from the Pacific Northwest Landslide Inventory (PNLI)
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