arXiv subjects

André L. M. Vilela

Publications and source records attributed to André L. M. Vilela.

10 recordsLinked to original sources

arXiv2026

Stochastic consensus dynamics for decentralized decision systems

A fundamental mechanism underlying collective phenomena in social, technological, and economic systems is decentralized decision-making, in which the behavior of individual agents follows from local interactions in the absence of central coordination. Consensus formation is a central feature of such systems, with relevance to blockchain networks, distributed artificial intelligence, autonomous multi-agent systems, distributed control, and collective decision networks. We investigate consensus formation using a stochastic consensus model on random networks and examine how initial conditions, network connectivity, and system size shape the emergence of unanimous states. We show that the stochastic dynamics strongly amplify small initial majorities, progressively suppress the competing state, and drive the system toward a predictable collective outcome. Network connectivity primarily controls the efficiency of this process: increasing connectivity accelerates the propagation of local agreement and reduces the likelihood that fluctuations reverse the initially dominant state, although these gains gradually saturate in highly connected networks. System size produces a complementary effect. Larger networks require more individual updates to reach unanimity, but they are also increasingly reliable in selecting the state favored by the initial majority. Finite-size analysis shows that the range of initial conditions associated with uncertain outcomes becomes progressively narrower as the network grows, decreasing approximately with the inverse square root of the system size. These results reveal a collective amplification mechanism by which weak initial asymmetries become increasingly decisive in large decentralized networks, and they provide a simple framework for understanding the efficiency, predictability, and reliability of consensus formation in distributed decision systems.

physics.comp-ph↗

arXiv2026

Adaptive workforce exploration in complex productivity landscapes

Specialization and task allocation enhance efficiency and innovation across diverse systems, from biological organisms to socioeconomic institutions. The evolution of task distribution and its influence on organizational productivity encapsulate the dynamics between task dependencies and adaptive strategies. We explore the organizational division of labor, inspired by the NK model of rugged landscapes, which is widely applied in evolutionary biology, and incorporate interdependencies among the attributes of technical experts within an organization. Our model considers two types of employees characterized by their task allocation strategies: specialists, who are permanently assigned to a single task, and generalists, who stochastically select a task at each time step. We investigate how the ruggedness of the productivity landscape, shaped by task interdependency, affects the organization's capacity to optimize labor division and meet market demands. Using group selection algorithms, we reveal the emergence of nonlinear adaptive dynamics, providing insights into how companies can adapt their strategies to meet market demands and foster innovation.

cond-mat.stat-mech↗

arXiv2026

Opinion dynamics on social proximity disordered networks

Understanding how local social pressure shapes collective opinion formation is essential in modern society, with implications in sociology, politics, finance, and technology. Complex networks are a powerful framework for investigating these processes and for representing social interactions in emergent phenomena, and canonical models such as random, scale-free, and small-world networks remain most relevant when Euclidean geometric proximity and contiguous interactions are not the dominant organizing factors. Our investigation explores the effects of Gabriel graphs, a spatially embedded network that utilizes proximity-based interactions, on social opinion dynamics. Using a geometric disorder parameter that randomly displaces agent coordinates, we transition from square lattices of social interactions to spatial random networks that preserve local constraints while introducing strong topological heterogeneity. Opinion dynamics operate through social pressure, with a level of nonconformity that affects dissent among opinions. We analyze magnetization, susceptibility, and Binder's fourth-order cumulant to characterize the emergence and breakdown of global social consensus. The model exhibits second-order phase transitions whose behavior depends on both geometric disorder and nonconformity levels. We use volumetric scaling and provide a reference frame appropriate for characterizing criticality in regular and heterogeneous interaction topologies. We find that the standard critical exponents remain compatible with the two-dimensional Ising universality class. Furthermore, the results suggest that spatially constrained, planar, and globally connected interaction topologies preserve the dimensionality of space in critical dynamics analysis.

cond-mat.stat-mech↗

arXiv2024

Consensus effects of social media synthetic influence groups on scale-free networks

Online platforms for social interactions are an essential part of modern society. With the advance of technology and the rise of algorithms and AI, content is now filtered systematically and facilitates the formation of filter bubbles. This work investigates the social consensus under limited visibility in a two-state majority-vote model on Barabási-Albert scale-free networks. In the consensus evolution, each individual assimilates the opinion of the majority of their neighbors with probability $1-q$ and disagrees with chance $q$, known as the noise parameter. We define the visibility parameter $V$ as the probability of an individual considering the opinion of a neighbor at a given interaction. The parameter $V$ enables us to model the limited visibility phenomenon that produces synthetic neighborhoods in online interactions. We employ Monte Carlo simulations and finite-size scaling analysis to obtain the critical noise parameter as a function of the visibility $V$ and the growth parameter $z$. We find the critical exponents $β/\barν$, $γ/\barν$ and $1/\barν$ of and validate their unitary relation for complex networks. Our analysis shows that installing and manipulating synthetic influence groups critically undermines consensus robustness.

cond-mat.stat-mech↗

arXiv2024

Three-state Opinion Dynamics for Financial Markets on Complex Networks

This work investigates the effects of complex networks on the collective behavior of a three-state opinion formation model in economic systems. Our model considers two distinct types of investors in financial markets: noise traders and fundamentalists. Financial states evolve via probabilistic dynamics that include economic strategies with local and global influences. The local majoritarian opinion drives noise traders' market behavior, while the market index influences the financial decisions of fundamentalist agents. We introduce a level of market anxiety $q$ present in the decision-making process that influences financial action. In our investigation, nodes of a complex network represent market agents, whereas the links represent their financial interactions. We investigate the stochastic dynamics of the model on three distinct network topologies, including scale-free networks, small-world networks and Erd{ö}s-Rényi random graphs. Our model mirrors various traits observed in real-world financial return series, such as heavy-tailed return distributions, volatility clustering, and short-term memory correlation of returns. The histograms of returns are fitted by coupled Gaussian distributions, quantitatively revealing transitions from a leptokurtic to a mesokurtic regime under specific economic heterogeneity. We show that the market dynamics depend mainly on the average agent connectivity, anxiety level, and market composition rather than on specific features of network topology.

physics.soc-ph↗

arXiv2023

Entropy Production on Cooperative Opinion Dynamics

As one of the most widespread social dynamics, cooperative behavior is among the most fascinating collective phenomena. Several animal species, from social insects to human beings, feature social groups altruistically working for a common benefit. This collaborative conduct pervades the actions and opinions of individuals, yielding strategic decision-making between political, religious, ethnic, and economic social puzzles. Here, we explore how cooperative behavior phenomena impact collective opinion dynamics and entropy generation in social groups. We select a random fraction $f$ of community members as collaborative individuals and model the opinion dynamics using a social temperature parameter $q$ that functions as a social anxiety noise. With probability $q$, regular individuals oppose their companions about a social decision, assuming group dissent. Collaborative agents experience a reduced effective social noise $μq$, where $0 < μ< 1$ is the social anxiety noise sensibility parameter that enhances social validation. We perform numerical simulations and mean-field analysis and find the system undergoes nonequilibrium order-disorder phase transitions with expressive social entropy production. Our results also highlight the effects of an individual social anxiety attenuation level in enhancing group consensus and inducing exuberant collective phenomena in complex systems.

cond-mat.stat-mech↗

arXiv2022

Opinion Dynamics in Financial Markets via Random Networks

We investigate the financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize the individuals in a financial market by their trading strategy, namely noise traders and fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the fundamentalist agents' decisions. We introduce a noise parameter $q$ to represent a level of anxiety and perceived uncertainty regarding the market behavior, enabling the possibility for an adrift financial action. We place the individuals as nodes in an Erdös-Rényi random graph, where the links represent their social interaction. At a given time, they assume one of two possible opinion states $\pm 1$ regarding buying or selling an asset. The model exhibits such fundamental qualitative and quantitative real-world market features as the distribution of logarithmic returns with fat-tails, clustered volatility, and long-term correlation of returns. We use Student's t distributions to fit the histograms of logarithmic returns, showing the gradual shift from a leptokurtic to a mesokurtic regime, depending on the fraction of fundamentalist agents. We also compare our results with the distribution of logarithmic returns of several real-world financial indices.

q-fin.ST↗

arXiv2020

Short-time Monte Carlo simulation of the majority-vote model on cubic lattices

We perform short-time Monte Carlo simulations to study the criticality of the isotropic two-state majority-vote model on cubic lattices of volume $N = L^3$, with $L$ up to $2048$. We obtain the precise location of the critical point by examining the scaling properties of a new auxiliary function $Ψ$. We perform finite-time scaling analysis to accurately calculate the whole set of critical exponents, including the dynamical critical exponent $z=2.027(9)$, and the initial slip exponent $θ= 0.1081(1)$. Our results indicate that the majority-vote model in three dimensions belongs to the same universality class of the three-dimensional Ising model.

cond-mat.stat-mech↗

arXiv2019

Three-state Majority-Vote Model on Barabási-Albert and Cubic Networks and the Unitary Relation for Critical Exponents

We investigate the three-state majority-vote model with noise on scale-free and regular networks. In this model, an individual selects an opinion equal to the opinion of the majority of its neighbors with probability $1 - q$ and opposite to it with probability $q$. The parameter $q$ is called the noise parameter of the model. We build a network of interactions where $z$ neighbors are selected by each added site in the system, yielding a preferential attachment network with degree distribution $k^{-λ}$, where $λ\sim 3$. In this work, $z$ is called growth parameter. Using finite-size scaling analysis, we show that the critical exponents associated with the magnetization and magnetic susceptibility add up to unity when a volumetric scaling is used, regardless of the dimension of the network of interactions. Using Monte Carlo simulations, we calculate the critical noise parameter $q_c$ as a function of $z$ for the scale-free networks and obtain the phase diagram of the model. We find that the critical noise is an increasing function of the growth parameter $z$, and we define and verify numerically the unitary relation $\upsilon$ for the critical exponents by calculating $β/\barν$, $γ/\barν$ and $1/\barν$ for several values of the network parameter $z$. We also obtain the critical noise and the critical exponents for the two and three-state majority-vote model on cubic lattices networks where we illustrate the application of the unitary relation with a volumetric scaling.

cond-mat.stat-mech↗

arXiv2019

A Three-state Opinion Formation Model for Financial Markets

We propose a three-state microscopic opinion formation model for the purpose of simulating the dynamics of financial markets. In order to mimic the heterogeneous composition of the mass of investors in a market, the agent-based model considers two different types of traders: noise traders and contrarians. Agents are represented as nodes in a network of interactions and they can assume any of three distinct possible states (e.g. buy, sell or remain inactive). The time evolution of the state of an agent is dictated by probabilistic dynamics that include both local and global influences. A noise trader is subject to local interactions, tending to assume the majority state of its nearest neighbors, whilst a contrarian is subject to a global interaction with the behavior of the market as a whole, tending to assume the state of the global minority of the market. The model exhibits the typical qualitative and quantitative features of real financial time series, including distributions of returns with heavy tails, volatility clustering and long-time memory for the absolute values of the returns. The distributions of returns are fitted by means of coupled Gaussian distributions, quantitatively revealing transitions between leptokurtic, mesokurtic and platykurtic regimes in terms of a non-linear statistical coupling which describes the complexity of the system.

physics.soc-ph↗