Changes in the Environment and Individual Learning
The ability to adjust to changes in the environment (shifts in regime) has not been demonstrated in the models of individual behavior studied in the literature. This is an important issue that has not been given much attention in the learning literature. In this paper, we study the behavior of Individual Evolutionary Learning model (Arifovic and Ledyard, 2003) in the games created by the Groves-Ledyard mechanism for provision of public good where regime changes occur. This model has proven to have excellent convergence properties that match the results from the experiments with human subjects. In this paper, we take our model a step further. We design the following methodology: We initialize the agents' individual algorithms at the equilibrium values of a given regime and simulate that regime for 100 periods. Then, we introduce a regime change, i.e. a shift in one of the model's parameter values. We record the speed of convergence of the algorithm to the `new regime' s equilibrium values. After the convergence, we simulate the new environment for another 100 periods to examine the model's stability features. Our results demonstrate the model's ability to adapt fast to the new regime and converge to the new regime's equilibrium values. We analyze the dynamics of adjustment and the characteristics of the model that are important in the model's adaptability. We also compare the results to the results of the experiments with human subjects.
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