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.

翻译：经济学领域的关键任务之一是借助数学模型预测经济主体对未来经济变量取值的预期。数学模型的运行轨迹可能呈现非规则性（包括混沌态），这将削弱其预测能力。本文研究两种经济模型的行为模式，识别其中的非规则动态特征。以这些模型为例，我们展示了进化算法与连续深度Q学习法结合Pyragas控制法在生成控制作用方面的有效性——该控制作用能稳定非周期轨道并抑制混沌动态。通过对比施加控制前后模型动态的定性与定量特征，并运用数值仿真验证结果。所提出的方法可提升经济机制预测的可靠性及参数调优能力，从而最大化决策效率。