Uncovering Salience-Driven Dynamics in Consumer Confidence with Generative Social Simulation

2026-06-29

AI Summary

This study addresses the limitations of traditional consumer confidence index (CCI) modeling, which often overlooks individual heterogeneity and divergent interpretations of economic information, thereby failing to capture dynamic responses to high-salience shocks. To overcome this, the authors propose ConsumerSim, a generative social simulation framework that integrates micro-calibrated synthetic populations with multi-source temporal signals. By incorporating survey-like response generation, post-stratified belief updating, and behavioral inertia modeling, the approach conceptualizes consumer confidence as an interpretable human–environment interaction process for the first time. The framework reveals that confidence dynamics are driven by salient events, exhibiting consistent directional shifts across groups but heterogeneous magnitudes, underscoring the necessity of both representative aggregation and individual-level heterogeneity. Evaluated on official CCI series from the U.S., Europe, and Japan, ConsumerSim significantly outperforms baseline models—particularly during shock periods—and enhances short-term forecasting of real-economy indicators such as housing activity through reconstructed confidence signals.

Abstract

Consumer confidence is typically modeled as a persistent macroeconomic index, yet its movements arise from households that interpret economic information through heterogeneous constraints, exposures, prior beliefs, and attention. We introduce ConsumerSim, a generative Human--Environment response framework that reconstructs Consumer Confidence Index (CCI) dynamics from a microdata-calibrated synthetic population, time-stamped macroeconomic, financial, policy, and news signals, survey-like response generation, post-stratified belief expansion, and behavioral inertia alignment. Across U.S., EU27, and Japanese official CCI target series, ConsumerSim ranks first among persistence, time-series, regression, and information-augmented baselines on the reported reconstruction metrics, with clear gains around high-salience shocks. Its reconstructed signal also improves short-horizon prediction of real activity, most consistently for housing outcomes. Mechanism analyses show that CCI movements concentrate around salient events; subgroup trajectories often align in direction while differing in magnitude; and signal sensitivity varies across income, homeownership, education, and political-alignment groups. Population-expansion and ablation results indicate that representative aggregation, situational signals, persona heterogeneity, and inertia are necessary for both accuracy and diagnosis. The findings support a behavioral view of consumer confidence as an interpretable Human--Environment response process rather than a purely aggregate time series.

Problem

• consumer confidence
• salience
• heterogeneous agents
• behavioral dynamics
• microfoundations

Innovation

• generative social simulation
• consumer confidence
• salience-driven dynamics
• microdata-calibrated synthetic population
• behavioral inertia