A model of learning and emulation with artificial adaptive agents

Article Abstract:

A model of learning and emulation shows that heterogenous agents found in a competitive economy communicate with each other and test ways on how to predict what would happen in the future. The model is believed to offer a better description of trial-and-error learning that can be utilized in macroeconomic models. The use of learning and emulation model revealed three possible long-term results, such as failure to coordinate within a given time period and coordination during low inflation.

Economics, Research and Development in the Social Sciences and Humanities, Learning, Macroeconomics, Teaching models

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Hererogeneity in capital and skills in a neoclassical stochestic growth model

Article Abstract:

A model of leisure/labor choice, aggregated for simplicity, exhibits an appropriate number of hours worked, given a sufficient amount of data. Several previously unexplained facts about labor market time series are demonstrated in the model.

Work hours, Consumption (Economics), Leisure

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Approximating and simulating the stochestic growth model: parameterized expectations, neural networks, and the genetic algorithm

Article Abstract:

Neural networks prove more useful than polynomial expansions in parameterized expectiation solutions. A combination of genetic algorithms and parameterized expectations should be used to check or models.

Statistical Data Included, Usage, Computer networks, Neural networks, Economic development, Genetic algorithms

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