Combining Combined Forecasts: a Network Approach

TL;DR

This paper examines how expert communication networks influence the efficiency of forecast aggregation, revealing that network structure significantly impacts the distortion of combined forecasts.

Contribution

It introduces a network-based framework to analyze forecast aggregation, highlighting the role of network topology and degree heterogeneity in aggregation efficiency.

Findings

Regular networks minimize aggregation distortion among connected networks.

Star networks generate the largest distortions in sparse structures.

Aggregation efficiency approaches the regular-network benchmark as expected degree varies significantly.

Abstract

This paper studies how communication across experts prior to aggregation by a decision-maker affects the efficiency of forecast combination. When experts exchange information before reporting their forecasts, their signals become correlated through the communication network, altering aggregation efficiency even when forecasts are unbiased. The analysis introduces a statistic that characterizes how network structure shapes aggregation efficiency and shows that degree heterogeneity plays a central role. Among connected networks, regular networks attain the minimal level of aggregation distortion, while star networks generate the largest distortions within sparse connected structures. Random network benchmarks show that aggregation efficiency approaches the regular-network benchmark when expected degree either vanishes or becomes large as network size increases, whereas networks with constant expected degree generate intermediate distortions. These results provide a theoretical foundation for understanding how communication across experts affects forecast combination and establish a connection between the forecast combination literature and models of social learning in networks.