1,521 research outputs found
The Efficiency and Evolution of R&D Networks
This work introduces a new model to investigate the efficiency and evolution of networks of firms exchanging knowledge in R&D partnerships. We first examine the efficiency of a given network structure in terms of the maximization of total profits in the industry. We show that the efficient network structure depends on the marginal cost of collaboration. When the marginal cost is low, the complete graph is efficient. However, a high marginal cost implies that the efficient network is sparser and has a core-periphery structure. Next, we examine the evolution of the network struc- ture when the decision on collaborating partners is decentralized. We show the existence of mul- tiple equilibrium structures which are in general inefficient. This is due to (i) the path dependent character of the partner selection process, (ii) the presence of knowledge externalities and (iii) the presence of severance costs involved in link deletion. Finally, we study the properties of the emerg- ing equilibrium networks and we show that they are coherent with the stylized facts of R&D net- works.R&D networks, technology spillovers, network efficiency, network formation
An infrared origin of leptonic mixing and its test at DeepCore
Fermion mixing is generally believed to be a low-energy manifestation of an
underlying theory whose energy scale is much larger than the electroweak scale.
In this paper we investigate the possibility that the parameters describing
lepton mixing actually arise from the low-energy behavior of the neutrino
interacting fields. In particular, we conjecture that the measured value of the
mixing angles for a given process depends on the number of unobservable flavor
states at the energy of the process. We provide a covariant implementation of
such conjecture, draw its consequences in a two neutrino family approximation
and compare these findings with current experimental data. Finally we show that
this infrared origin of mixing will be manifest at the Ice Cube DeepCore array,
which measures atmospheric oscillations at energies much larger than the tau
lepton mass; it will hence be experimentally tested in a short time scale.Comment: 14 pages, 1 figure; version to appear in Int.J.Mod.Phys.
Elites, communities and the limited benefits of mentorship in electronic music
While the emergence of success in creative professions, such as music, has been studied extensively, the link between individual success and collaboration is not yet fully uncovered. Here we aim to fill this gap by analyzing longitudinal data on the co-releasing and mentoring patterns of popular electronic music artists appearing in the annual Top 100 ranking of DJ Magazine. We find that while this ranking list of popularity publishes 100 names, only the top 20 is stable over time, showcasing a lock-in effect on the electronic music elite. Based on the temporal co-release network of top musicians, we extract a diverse community structure characterizing the electronic music industry. These groups of artists are temporally segregated, sequentially formed around leading musicians, and represent changes in musical genres. We show that a major driving force behind the formation of music communities is mentorship: around half of musicians entering the top 100 have been mentored by current leading figures before they entered the list. We also find that mentees are unlikely to break into the top 20, yet have much higher expected best ranks than those who were not mentored. This implies that mentorship helps rising talents, but becoming an all-time star requires more. Our results provide insights into the intertwined roles of success and collaboration in electronic music, highlighting the mechanisms shaping the formation and landscape of artistic elites in electronic music
DebtRank: A microscopic foundation for shock propagation
The DebtRank algorithm has been increasingly investigated as a method to estimate the impact of shocks in financial networks, as it overcomes the limitations of the traditional default-cascade approaches. Here we formulate a dynamical "microscopic" theory of instability for financial networks by iterating balance sheet identities of individual banks and by assuming a simple rule for the transfer of shocks from borrowers to lenders. By doing so, we generalise the DebtRank formulation, both providing an interpretation of the effective dynamics in terms of basic accounting principles and preventing the underestimation of losses on certain network topologies. Depending on the structure of the interbank leverage matrix the dynamics is either stable, in which case the asymptotic state can be computed analytically, or unstable, meaning that at least one bank will default. We apply this framework to a dataset of the top listed European banks in the period 2008-2013. We find that network effects can generate an amplification of exogenous shocks of a factor ranging between three (in normal periods) and six (during the crisis) when we stress the system with a 0.5% shock on external (i.e. non-interbank) assets for all banks
Multilayer motif analysis of brain networks
This work was partially supported by the EU-LASAGNE Project, Contract No. 318132 (STREP)
Pathways towards instability in financial networks
Following the financial crisis of 2007-2008, a deep analogy between the origins of instability in financial systems and complex ecosystems has been pointed out: in both cases, topological features of network structures influence how easily distress can spread within the system. However, in financial network models, the details of how financial institutions interact typically play a decisive role, and a general understanding of precisely how network topology creates instability remains lacking. Here we show how processes that are widely believed to stabilize the financial system, that is, market integration and diversification, can actually drive it towards instability, as they contribute to create cyclical structures which tend to amplify financial distress, thereby undermining systemic stability and making large crises more likely. This result holds irrespective of the details of how institutions interact, showing that policy-relevant analysis of the factors affecting financial stability can be carried out while abstracting away from such details
Systemic Risk in a Unifying Framework for Cascading Processes on Networks
We introduce a general framework for models of cascade and contagion
processes on networks, to identify their commonalities and differences. In
particular, models of social and financial cascades, as well as the fiber
bundle model, the voter model, and models of epidemic spreading are recovered
as special cases. To unify their description, we define the net fragility of a
node, which is the difference between its fragility and the threshold that
determines its failure. Nodes fail if their net fragility grows above zero and
their failure increases the fragility of neighbouring nodes, thus possibly
triggering a cascade. In this framework, we identify three classes depending on
the way the fragility of a node is increased by the failure of a neighbour. At
the microscopic level, we illustrate with specific examples how the failure
spreading pattern varies with the node triggering the cascade, depending on its
position in the network and its degree. At the macroscopic level, systemic risk
is measured as the final fraction of failed nodes, , and for each of
the three classes we derive a recursive equation to compute its value. The
phase diagram of as a function of the initial conditions, thus allows
for a prediction of the systemic risk as well as a comparison of the three
different model classes. We could identify which model class lead to a
first-order phase transition in systemic risk, i.e. situations where small
changes in the initial conditions may lead to a global failure. Eventually, we
generalize our framework to encompass stochastic contagion models. This
indicates the potential for further generalizations.Comment: 43 pages, 16 multipart figure
Topological enslavement in evolutionary games on correlated multiplex networks
Governments and enterprises strongly rely on incentives to generate favorable
outcomes from social and strategic interactions between individuals. The
incentives are usually modeled by payoffs in evolutionary games, such as the
prisoner's dilemma or the harmony game, with imitation dynamics. Adjusting the
incentives by changing the payoff parameters can favor cooperation, as found in
the harmony game, over defection, which prevails in the prisoner's dilemma.
Here, we show that this is not always the case if individuals engage in
strategic interactions in multiple domains. In particular, we investigate
evolutionary games on multiplex networks where individuals obtain an aggregate
payoff. We explicitly control the strength of degree correlations between nodes
in the different layers of the multiplex. We find that if the multiplex is
composed of many layers and degree correlations are strong, the topology of the
system enslaves the dynamics and the final outcome, cooperation or defection,
becomes independent of the payoff parameters. The fate of the system is then
determined by the initial conditions
Determination of the forward slope in and elastic scattering up to LHC energy
In the analysis of experimental data on (or ) elastic
differential cross section it is customary to define an average forward slope
in the form , where is the momentum transfer. Taking as
working example the results of experiments at Tevatron and SPS, we will show
with the help of the impact picture approach, that this simplifying assumption
hides interesting information on the complex non-flip scattering amplitude, and
that the slope is not a constant. We investigate the variation of this
slope parameter, including a model-independent way to extract this information
from an accurate measurement of the elastic differential cross section. An
extension of our results to the LHC energy domain is presented in view of
future experiments.Comment: 12 pages, 6 figures, to appear in EPJ
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