2009/06/30 by Silvio Gualdi, S. Gualdi, Andrea De Martino +1
Economics, Econometrics and Finance · Engineering · Mathematics · Physics and Astronomy · Psychology · #Affect (linguistics) #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Computer science #Econometrics #Economics #Engineering #Geography #Herding #Mathematics #Opinion Dynamics and Social Influence #Physics #Private information retrieval #Psychology #Quality (philosophy) #Range (aeronautics) #Statistics #Variable (mathematics) #physics.soc-ph
paper · pdf · doi:10.1016/j.physa.2009.09.040
published in Physica A Statistical Mechanics and its Applications 389(2), 323-329 (Elsevier BV) · 11 pages, 5 figures, updated version (to appear in Physica A)
arxiv created 2009/09/21 · openalex publication_date 2009/09/26 · arxiv updated 2015/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.