Learning, Forecasting and Optimizing: An Experimental Study

Abstract

Rational Expectations (RE) models have two crucial dimensions: 1) agents correctly forecast future prices given all available information, and 2) given expectations, agents solve optimization problems and these solutions in turn determine actual price realizations. Experimental testing of such models typically focuses on only one of these two dimensions. In this paper we consider both forecasting and optimization decisions in an experimental cobweb economy. We report results from four experimental treatments: 1) subjects form forecasts only, 2) subjects determine quantity only (solve an optimization problem), 3) they do both and 4) they are paired in teams and one member is assigned the forecasting role while the other is assigned the optimization task. All treatments converge to Rational Expectation Equilibrium (REE), but at very different speeds. We observe that performance is the best in treatment 1) and worst in the treatment 3). Most forecasters use a n adaptive expectations rule. Subjects are less likely to make conditionally optimal production decision for given forecasts in treatment 3) where the forecast is made by themselves, than in treatment 4) where the forecast is made by the other member of their team, which suggests that "two heads are better than one" in finding REE.

Keywords: Learning, Rational Expectations, Optimization, Experimental Economics, Bounded Rationality

JEL Classification: C91, C92, D83, D84