C&SA | Contents

Volume 62 >>> № 2 MARCH — APRIL 2026

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UDC 004.81: 004.85: 681.51

O. Palagin
V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine,
palagin_a@ukr.net

D. Symonov
V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine,
denys.symonov@gmail.com

O. Symonova
Kyiv National Economic University named after Vadym Hetman,
ar.symonova@gmail.com

AGENT BEHAVIOR PREDICTION BASED ON A DYNAMIC COGNITIVE MODEL

Abstract. The paper presents a novel functional model of a dynamic cognitive system designed to simulate agent behavior in complex socio-informational environments. The proposed approach integrates cognitive, emotional, motivational, and social components within a unified hierarchical architecture supported by multimodal data collection. Special emphasis is placed on the use of state-of-the-art machine learning techniques to optimize action-selection policies, enhance prediction accuracy, and improve adaptability to exogenous disturbances. Simulation results demonstrate the model’s relevance for scenario analysis, recovery dynamics following informational shocks, and the elucidation of underlying group behavior structures. These findings provide a foundation for practical applications in strategic management, education, rehabilitation, and social analyticss.

Keywords: dynamic cognitive system, entropy-regularized Q-learning, phase portrait analysis, agent-based modeling, machine learning.

full text

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