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Abstract: Summarized by the efcient market hypothesis, the idea that stock prices fully refect all available information is always confronted with the behavior of real-world markets. While there is plenty of evidence indicating and quantifying the efciency of stock markets, most studies assume this efciency to be constant over time so that its dynamical and collective aspects remain poorly understood. Here we defne the time-varying efciency of stock markets by calculating the permutation entropy within sliding time-windows of log-returns of stock market indices. We show that major world stock markets can be hierarchically classifed into several groups that display similar long-term efciency profles. However, we also show that efciency ranks and clusters of markets with similar trends are only stable for a few months at a time. We thus propose a network representation of stock markets that aggregates their short-term efciency patterns into a global and coherent picture. We fnd this fnancial network to be strongly entangled while also having a modular structure that consists of two distinct groups of stock markets. Our results suggest that stock market efciency is a collective phenomenon that can drive its operation at a high level of informational efciency, but also places the entire system under risk of failure.
Keywords: collective dynamics, social physics, econophysics, stock market
Published in DKUM: 14.01.2025; Views: 0; Downloads: 2
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Abstract: The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.
Keywords: neuronal dynamics, coherence resonance, excitatory neurons, inhibitory neurons, neural network, multilayer network, interlayer connectivity
Published in DKUM: 20.12.2024; Views: 0; Downloads: 2
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Abstract: In this study, we focus on training recurrent spiking neural networks to generate spatiotemporal patterns in the form of closed two-dimensional trajectories. Spike trains in the trained networks are examined in terms of their dissimilarity using the Victor-Purpura distance. We apply algebraic topology methods to the matrices obtained by rank-ordering the entries of the distance matrices, specifically calculating the persistence barcodes and Betti curves. By comparing the features of dierent types of output patterns, we uncover the complex relations between low-dimensional target signals and the underlying multidimensional spike trains.
Keywords: topological features, neural networks, spatiotemporal patterns, nonlinear dynamics
Published in DKUM: 27.11.2024; Views: 0; Downloads: 0
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Abstract: The spreading of cooperation in structured population is a challenging problem which can be observed at diferent scales of social and biological organization. Generally, the problem is studied by evaluating the chances that few initial invading cooperators, randomly appearing in a network, can lead to the spreading of cooperation. In this paper we demonstrate that in many scenarios some cooperators are more infuential than others and their initial positions can facilitate the spreading of cooperation. We investigate six diferent ways to add initial cooperators in a network of cheaters, based on diferent network-based measurements. Our research reveals that strategically positioning the initial cooperators in a population of cheaters allows to decrease the number of initial cooperators necessary to successfully seed cooperation. The strategic positioning of initial cooperators can also help to shorten the time necessary for the restoration of cooperation. The optimal ways in which the initial cooperators should be placed is, however, non-trivial in that it depends on the degree of competition, the underlying game, and the network structure. Overall, our results show that, in structured populations, few cooperators, well positioned in strategically chosen places, can spread cooperation faster and easier than a large number of cooperators that are placed badly.
Keywords: cooperation, evolutionary game theory, social physics, collective dynamics, complex system
Published in DKUM: 22.10.2024; Views: 0; Downloads: 1
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Abstract: Pursuing vaccinations against COVID-19 brings hope to limit the spread of SARS-CoV-2 and remains the most rational decision under pandemic conditions. However, it does not come without challenges, including temporary shortages in vaccine doses, significant vaccine inequity, and questions regarding the durability of vaccine-induced immunity that remain unanswered. Moreover, SARS-CoV-2 has undergone evolution with the emergence of its novel variants, characterized by enhanced transmissibility and ability to at least partially evade neutralizing antibodies. At the same time, serum antibody levels start to wane within a few months after vaccination, ultimately increasing the risk of breakthrough infections. This article discusses whether the administration of booster doses of COVID-19 vaccines is urgently needed to control the pandemic. We conclude that, at present, optimizing the immunity level of wealthy populations cannot come at the expense of low-income regions that suffer from vaccine unavailability. Although the efficiency of vaccination in protecting from infection may decrease over time, current data show that efficacy against severe disease, hospitalization, and death remains at a high level. If vaccine coverage continues at extremely low levels in various regions, including African countries, SARS-CoV-2 may sooner or later evolve into variants better adapted to evade natural and vaccine-induced immunity, ultimately bringing a global threat that, of course, includes wealthy populations. We offer key recommendations to increase vaccination rates in low-income countries. The pandemic is, by definition, a major epidemiological event and requires looking beyond one's immediate self-interest; otherwise, efforts to contain it will be futile.
Keywords: COVID-19, pandemic, disease dynamics, exponential growth, virality, vaccination strategy, immunology, massive vaccinations, vaccine inequity, SARS-CoV-2
Published in DKUM: 14.10.2024; Views: 0; Downloads: 6
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