arXiv subjects

Mateus F. B. Granha

Publications and source records attributed to Mateus F. B. Granha.

3 recordsLinked to original sources

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↗

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↗

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↗