Cyclical or chaotic competitive equilibria that do not exist under perfect foresight are shown to occur in a decentralized growth model under constant gain adaptive learning. This paper considers an economy populated by boundedly rational households making one-period ahead constant gain adaptive input price forecasts, and using simple expectation rules to predict long-run physical capital holdings and consumption. Under these hypotheses, lifetime decisions are derived as time unfolds, and analytical solutions to the representative household's problem exist for a standard class of preferences. Under various characteristics of the model's functional forms, competitive equilibrium trajectories under learning may exhibit opposite local stability properties depending whether the underlying information set accommodates all contemporary data. Calibrated to the U.S. economy, the model with boundedly rational households may exhibit endogenous business cycles around the permanent regime which is a saddle point under perfect foresightbounded rationality, constant gain adaptive learning, endogenous business cycles
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