Modeling noisy learning in a dynamic oligopoly experiment

Estimating demand before production poses a significant challenge for many industries, including vaccine manufacturing, newspapers, and perishable foods. Both industry professionals and experimental subjects in laboratory settings often struggle to determine optimal production. This paper sheds light on the cognitive processes that explain individuals' inability to act optimally, using data from Nagel and Vriend, 1999a, Nagel and Vriend, 1999b. In their experiment, participants, acting as firms, set production levels without prior knowledge of the demand generated by own and competitors' advertising efforts. We first reexamine their two-step learning model, which involves directional learning for production levels and a hill-climbing algorithm for advertising, utilizing exogenous adjustment size distributions. We improve upon this model, inspired by the macroeconomic learning literature, with agents using constant gain learning for production decisions and hill climbing for advertising decisions with endogenous adjustments. We demonstrate that our model provides a better fit to the data than the original model by Nagel and Vriend (1999a) and yields superior out-of-sample forecasts, both for one-period-ahead predictions and simulated paths.