Template-Type: ReDIF-Paper 1.0 Author-Name: Cars Hommes Author-Name-First: Cars Author-Name-Last: Hommes Author-Email: c.h.hommes@uva.nl / CHommes@bank-banque-canada.ca Author-Workplace-Name: Canadian Economic Analysis, Bank of Canada; CeNDEF, Amsterdam School of Economics, University of Amsterdam; Tinbergen Institute, Amsterdam Author-Name: Stefanie J. Huber Author-Name-First: Stefanie J. Author-Name-Last: Huber Author-Email: shuber@uni-bonn.de Author-Workplace-Name: Department of Economics, University of Bonn; ECONtribute Research Cluster Author-Name: Daria Minina Author-Name-First: Daria Author-Name-Last: Minina Author-Email: d.minina2@uva.nl Author-Workplace-Name: CeNDEF, Amsterdam School of Economics, University of Amsterdam Author-Name: Isabelle Salle Author-Name-First: Isabelle Author-Name-Last: Salle Author-Email: isalle@uottawa.ca Author-Workplace-Name: Department of Economics, University of Ottawa; Macro and International Economics, Amsterdam School of Economics, University of Amsterdam; Tinbergen Institute, Amsterdam Title: Learning in a Complex World Insights from an OLG Lab Experiment Abstract: This paper brings novel insights into group coordination and price dynamics in complex environments. We implement an overlapping-generation model in the lab, where the output dynamics is given by the well-known chaotic quadratic map. This model structure allows us to study previously unexplored parameter regions where the perfect-foresight dynamics exhibits chaotic dynamics. This paper highlights three key findings. First, the price converges to the simplest equilibria, namely the monetary steady state or the two-cycle, in all markets. Second, we document a novel and intriguing finding: we observe a non-monotonicity of the behavior when complexity increases. Convergence to the two-cycle occurs for the intermediate parameter range, while both the extreme scenarios of a simple stable two-cycle and highly non-linear dynamics (with 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 and this non-monotonicity persists in the learning-to-optimize design. Third, 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 in our design. Length: 68 pages Classification-JEL: E70, E17, C92, C62 Keywords: Overlapping-generation (OLG) models, Complexity, Learning, Equilibria se- lection, Laboratory experiments Creation-Date: 2024-03 File-URL: https://www.econtribute.de/RePEc/ajk/ajkdps/ECONtribute_283_2024.pdf File-Format: Application/pdf File-Function: First version, 2024 Number: 283 Handle: RePEc:ajk:ajkdps:283