582 research outputs found
Parkinson's Law Quantified: Three Investigations on Bureaucratic Inefficiency
We formulate three famous, descriptive essays of C.N. Parkinson on
bureaucratic inefficiency in a quantifiable and dynamical socio-physical
framework. In the first model we show how the use of recent opinion formation
models for small groups can be used to understand Parkinson's observation that
decision making bodies such as cabinets or boards become highly inefficient
once their size exceeds a critical 'Coefficient of Inefficiency', typically
around 20. A second observation of Parkinson - which is sometimes referred to
as Parkinson's Law - is that the growth of bureaucratic or administrative
bodies usually goes hand in hand with a drastic decrease of its overall
efficiency. In our second model we view a bureaucratic body as a system of a
flow of workers, which enter, become promoted to various internal levels within
the system over time, and leave the system after having served for a certain
time. Promotion usually is associated with an increase of subordinates. Within
the proposed model it becomes possible to work out the phase diagram under
which conditions bureaucratic growth can be confined. In our last model we
assign individual efficiency curves to workers throughout their life in
administration, and compute the optimum time to send them to old age pension,
in order to ensure a maximum of efficiency within the body - in Parkinson's
words we compute the 'Pension Point'.Comment: 15 pages, 5 figure
Opinion Formation in Laggard Societies
We introduce a statistical physics model for opinion dynamics on random
networks where agents adopt the opinion held by the majority of their direct
neighbors only if the fraction of these neighbors exceeds a certain threshold,
p_u. We find a transition from total final consensus to a mixed phase where
opinions coexist amongst the agents. The relevant parameters are the relative
sizes in the initial opinion distribution within the population and the
connectivity of the underlying network. As the order parameter we define the
asymptotic state of opinions. In the phase diagram we find regions of total
consensus and a mixed phase. As the 'laggard parameter' p_u increases the
regions of consensus shrink. In addition we introduce rewiring of the
underlying network during the opinion formation process and discuss the
resulting consequences in the phase diagram.Comment: 5 pages, eps fig
Statistical mechanics of scale-free networks at a critical point: Complexity without irreversibility?
Based on a rigorous extension of classical statistical mechanics to networks,
we study a specific microscopic network Hamiltonian. The form of this
Hamiltonian is derived from the assumption that individual nodes
increase/decrease their utility by linking to nodes with a higher/lower degree
than their own. We interpret utility as an equivalent to energy in physical
systems and discuss the temperature dependence of the emerging networks. We
observe the existence of a critical temperature where total energy
(utility) and network-architecture undergo radical changes. Along this
topological transition we obtain scale-free networks with complex hierarchical
topology. In contrast to models for scale-free networks introduced so far, the
scale-free nature emerges within equilibrium, with a clearly defined
microcanonical ensemble and the principle of detailed balance strictly
fulfilled. This provides clear evidence that 'complex' networks may arise
without irreversibility. The results presented here should find a wide variety
of applications in socio-economic statistical systems.Comment: 4 pages, 5 figure
Unified model for network dynamics exhibiting nonextensive statistics
We introduce a dynamical network model which unifies a number of network
families which are individually known to exhibit -exponential degree
distributions. The present model dynamics incorporates static (non-growing)
self-organizing networks, preferentially growing networks, and (preferentially)
rewiring networks. Further, it exhibits a natural random graph limit. The
proposed model generalizes network dynamics to rewiring and growth modes which
depend on internal topology as well as on a metric imposed by the space they
are embedded in. In all of the networks emerging from the presented model we
find q-exponential degree distributions over a large parameter space. We
comment on the parameter dependence of the corresponding entropic index q for
the degree distributions, and on the behavior of the clustering coefficients
and neighboring connectivity distributions.Comment: 11 pages 8 fig
Schumpeterian economic dynamics as a quantifiable minimum model of evolution
We propose a simple quantitative model of Schumpeterian economic dynamics.
New goods and services are endogenously produced through combinations of
existing goods. As soon as new goods enter the market they may compete against
already existing goods, in other words new products can have destructive
effects on existing goods. As a result of this competition mechanism existing
goods may be driven out from the market - often causing cascades of secondary
defects (Schumpeterian gales of destruction). The model leads to a generic
dynamics characterized by phases of relative economic stability followed by
phases of massive restructuring of markets - which could be interpreted as
Schumpeterian business `cycles'. Model timeseries of product diversity and
productivity reproduce several stylized facts of economics timeseries on long
timescales such as GDP or business failures, including non-Gaussian fat tailed
distributions, volatility clustering etc. The model is phrased in an open,
non-equilibrium setup which can be understood as a self organized critical
system. Its diversity dynamics can be understood by the time-varying topology
of the active production networks.Comment: 21 pages, 11 figure
Unanimity Rule on networks
We introduce a model for innovation-, evolution- and opinion dynamics whose
spreading is dictated by unanimity rules, i.e. a node will change its (binary)
state only if all of its neighbours have the same corresponding state. It is
shown that a transition takes place depending on the initial condition of the
problem. In particular, a critical number of initially activated nodes is
needed so that the whole system gets activated in the long-time limit. The
influence of the degree distribution of the nodes is naturally taken into
account. For simple network topologies we solve the model analytically, the
cases of random, small-world and scale-free are studied in detail.Comment: 7 pages 4 fig
To bail-out or to bail-in? Answers from an agent-based model
Since the beginning of the 2008 financial crisis almost half a trillion euros have been spent to financially assist EU member states in taxpayer-funded bail-outs. These crisis resolutions are often accompanied by austerity programs causing political and social friction on both domestic and international levels. The question of how to resolve failing financial institutions, and how this depends on economic preconditions, is therefore a pressing and controversial issue of vast political importance. In this work we employ an agent-based model to study the economic and financial ramifications of three highly relevant crisis resolution mechanisms. To establish the validity of the model we show that it reproduces a series of key stylized facts of the financial and real economy. The distressed institution can either be closed via a purchase & assumption transaction, it can be bailed-out using taxpayer money, or it may be bailed-in in a debt-to-equity conversion. We find that for an economy characterized by low unemployment and high productivity the optimal crisis resolution with respect to financial stability and economic productivity is to close the distressed institution. For economies in recession with high unemployment the bail-in tool provides the most efficient crisis resolution mechanism. Under no circumstances do taxpayer-funded bail-out schemes outperform bail-ins with private sector involvement
To what extent homophily and influencer networks explain song popularity
Forecasting the popularity of new songs has become a standard practice in the
music industry and provides a comparative advantage for those that do it well.
Considerable efforts were put into machine learning prediction models for that
purpose. It is known that in these models, relevant predictive parameters
include intrinsic lyrical and acoustic characteristics, extrinsic factors
(e.g., publisher influence and support), and the previous popularity of the
artists. Much less attention was given to the social components of the
spreading of song popularity. Recently, evidence for musical homophily - the
tendency that people who are socially linked also share musical tastes - was
reported. Here we determine how musical homophily can be used to predict song
popularity. The study is based on an extensive dataset from the last.fm online
music platform from which we can extract social links between listeners and
their listening patterns. To quantify the importance of networks in the
spreading of songs that eventually determines their popularity, we use musical
homophily to design a predictive influence parameter and show that its
inclusion in state-of-the-art machine learning models enhances predictions of
song popularity. The influence parameter improves the prediction precision
(TP/(TP+FN)) by about 50% from 0.14 to 0.21, indicating that the social
component in the spreading of music plays at least as significant a role as the
artist's popularity or the impact of the genre.Comment: 7 pages, 3 figure
Scale-freeness for networks as a degenerate ground state: A Hamiltonian formulation
The origin of scale-free degree distributions in the context of networks is
addressed through an analogous non-network model in which the node degree
corresponds to the number of balls in a box and the rewiring of links to balls
moving between the boxes. A statistical mechanical formulation is presented and
the corresponding Hamiltonian is derived. The energy, the entropy, as well as
the degree distribution and its fluctuations are investigated at various
temperatures. The scale-free distribution is shown to correspond to the
degenerate ground state, which has small fluctuations in the degree
distribution and yet a large entropy. We suggest an implication of our results
from the viewpoint of the stability in evolution of networks.Comment: 7 pages, 3 figures. To appear in Europhysics lette
Statistical detection of systematic election irregularities
Democratic societies are built around the principle of free and fair
elections, that each citizen's vote should count equal. National elections can
be regarded as large-scale social experiments, where people are grouped into
usually large numbers of electoral districts and vote according to their
preferences. The large number of samples implies certain statistical
consequences for the polling results which can be used to identify election
irregularities. Using a suitable data collapse, we find that vote distributions
of elections with alleged fraud show a kurtosis of hundred times more than
normal elections on certain levels of data aggregation. As an example we show
that reported irregularities in recent Russian elections are indeed well
explained by systematic ballot stuffing and develop a parametric model
quantifying to which extent fraudulent mechanisms are present. We show that if
specific statistical properties are present in an election, the results do not
represent the will of the people. We formulate a parametric test detecting
these statistical properties in election results. Remarkably, this technique
produces similar outcomes irrespective of the data resolution and thus allows
for cross-country comparisons.Comment: For data see also
http://www.complex-systems.meduniwien.ac.at/elections/election.htm
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