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Genetic Programming in the Overlapping Generations Model: An Illustration with the Dynamics of the Inflation Rate

机译:重叠几代模型中的遗传编程:通货膨胀率动态的插图

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In this paper, genetic programming (GP) is employed to model learning and adaptation in the overlapping generations model, one of the most popular dynamic economic models. Using a model of inflation with multiple equilibria as an illustrative example, we show that our GP-based agents are able to coordinate their actions to achieve the Pareto-superior equilibrium (the low-inflation steady state) rather than the Pareto-inferior equilibrium (the high-inflation steady state). We also test the robustness of this result with different initial conditions, economic parameters, and GP control parameters.
机译:在本文中,遗传编程(GP)用于在重叠几代模型中模拟学习和适应,是最受欢迎的动态经济模型之一。使用具有多个均衡的通胀模型作为说明性示例,我们表明我们的GP基因能够协调其行动以实现帕累托 - 卓越的平衡(低通胀稳态)而不是静脉较差的平衡(高通胀稳定状态)。我们还通过不同的初始条件,经济参数和GP控制参数测试这一结果的鲁棒性。

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