Learning, forecasting and optimizing: an experimental study

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 converges to Rational Expectation Equilibrium (REE), but the at very different speed. We observe that performance is the best in treatment 1) and worst in the treatment 3). Most forecasters use an 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 treatment 4) where the forecast is made by the other member of the team, which confirms ``two heads are better than one" in finding REE.