Heuristic Switching Model and Exploration-Explotation Algorithm to describe long-run expectations in LtFEs: a comparison

We compare the performance of two learning algorithms in replicating individual short and long-run expectations: the Exploration-Explotation Algorithm (EEA) and the Heuristic Switching Model (HSM). Individual expectations are elicited in a series of Learning-to-Forecast Experiments (LtFEs) with different feedback mechanisms between expectations and market price: positive and negative feedback markets. We implement the EEA proposed by Colasante et al. (2018c). Moreover, we modify the existing version of the HSM in order to incorporate the long-run predictions. Although the two algorithms provide a fairly good description of marker prices in the short-run, the EEA outperforms the HSM in replicating the main characteristics of individual expectation in the long-run, both in terms of coordination of individual expectations and convergence of expectations to the fundamental value.