Learning in a Complex World: Insights from an OLG Lab Experiment

This paper brings novel insights into group coordination and price dynamics in complex environments. We implement an overlapping-generation model in the lab where output dynamics are given by the well-known chaotic quadratic map. This model structure allows us to study previously unexplored parameter regions where perfect-foresight dynamics exhibit chaotic dynamics. This paper highlights three key findings. First, the price converges to the simplest equilibria, namely either the monetary steady state or the two-cycle in all markets. Second, we document a novel and intriguing finding: a non-monotonicity of the behavior when complexity increases. Convergence to the two-cycle occurs for the intermediate parameter range, while the extreme scenarios of both a simple, stable two-cycle and highly nonlinear dynamics (chaos) lead to coordination on the steady state in the lab. All indicators of coordination and convergence significantly exhibit this non-monotonic relationship in the learning-to-forecast experiments. This finding also persists in the learning-to-optimize design. Finally, convergence in the learning-to-optimize experiment is more challenging to achieve: coordination on the two-cycle is never observed, although the two-cycle Pareto dominates the steady state.