  We investigate a toy model of inductive interacting agents aiming to forecast
a continuous, exogenous random variable E. Private information on E is spread
heterogeneously across agents. Herding turns out to be the preferred
forecasting mechanism when heterogeneity is maximal. However in such conditions
aggregating information efficiently is hard even in the presence of learning,
as the herding ratio rises significantly above the efficient-market expectation
of 1 and remarkably close to the empirically observed values. We also study how
different parameters (interaction range, learning rate, cost of information and
score memory) may affect this scenario and improve efficiency in the hard
phase.
