TY - JOUR

T1 - Forecasting and stabilizing chaotic regimes in two macroeconomic models via artificial intelligence technologies and control methods

AU - Alexeeva, Tatyana

AU - Diep, Quoc Bao

AU - Kuznetsov, Nikolay

AU - Zelinka, Ivan

PY - 2023/5/1

Y1 - 2023/5/1

N2 - One of the key tasks in the economy is forecasting the economic agents’ expectations of the future values of economic variables using mathematical models. The behavior of mathematical models can be irregular, including chaotic, which reduces their predictive power. In this paper, we study the regimes of behavior of two economic models and identify irregular dynamics in them. Using these models as an example, we demonstrate the effectiveness of evolutionary algorithms and the continuous deep Q-learning method in combination with Pyragas control method for deriving a control action that stabilizes unstable periodic trajectories and suppresses chaotic dynamics. We compare qualitative and quantitative characteristics of the model's dynamics before and after applying control and verify the obtained results by numerical simulation. Proposed approach can improve the reliability of forecasting and tuning of the economic mechanism to achieve maximum decision-making efficiency.

AB - One of the key tasks in the economy is forecasting the economic agents’ expectations of the future values of economic variables using mathematical models. The behavior of mathematical models can be irregular, including chaotic, which reduces their predictive power. In this paper, we study the regimes of behavior of two economic models and identify irregular dynamics in them. Using these models as an example, we demonstrate the effectiveness of evolutionary algorithms and the continuous deep Q-learning method in combination with Pyragas control method for deriving a control action that stabilizes unstable periodic trajectories and suppresses chaotic dynamics. We compare qualitative and quantitative characteristics of the model's dynamics before and after applying control and verify the obtained results by numerical simulation. Proposed approach can improve the reliability of forecasting and tuning of the economic mechanism to achieve maximum decision-making efficiency.

KW - Chaos

KW - Continuous deep Q-learning method

KW - Hénon map

KW - Overlapping generation model

KW - Pyragas control method

KW - Self-organized migration algorithm

KW - Spatio-temporal pricing model

UR - https://www.mendeley.com/catalogue/9736888f-51f1-36fe-a3c1-c5ee1621de08/

U2 - https://doi.org/10.1016/j.chaos.2023.113377

DO - https://doi.org/10.1016/j.chaos.2023.113377

M3 - Article

VL - 170

JO - Chaos, Solitons and Fractals

JF - Chaos, Solitons and Fractals

SN - 0960-0779

M1 - 113377

ER -