How does informational heterogeneity affect the quality of forecasts?

TL;DR

This paper models how heterogeneity in private information among agents influences forecast quality, revealing that herding behavior emerges under maximal heterogeneity and hampers efficient information aggregation.

Contribution

It introduces a toy model analyzing the impact of informational heterogeneity on forecasting, highlighting herding as a dominant mechanism and exploring parameter effects on efficiency.

Findings

Herding dominates when heterogeneity is maximal.

Aggregation efficiency is limited despite learning.

Herding ratio approaches empirical values.

Abstract

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.