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

• URL: http://arxiv.org/abs/2606.30395v1
• Date: Mon, 29 Jun 2026 14:46:22 GMT
• Title: Uncovering Salience-Driven Dynamics in Consumer Confidence with Generative Social Simulation
• Abstract summary: We introduce ConsumerSim, a generative Human--Environment response framework that reconstructs Consumer Confidence Index dynamics.<n>Across U.S., EU27, and Japanese official CCI target series, ConsumerSim ranks first among persistence, time-series, regression, and shocks information-augmented baselines.<n>Findings support a behavioral view of consumer confidence as an interpretable Human--Environment response process.
• Score: 56.70690339492564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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