Heuristic Switching Model and Exploration-Exploitation Algorithm to Describe Long-Run Expectations in LtFEs: a Compariso

Abstract

We elicit individual expectations 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. (J Evol Econ 2018b. https://doi.org/10.1007/S00191-018-0585-1). We compare the performance of two learning algorithms in replicating individual short and long-run expectations: the Exploration-Exploitation Algorithm (EEA) and the Heuristic Switching Model (HSM). 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 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.