{"ID":5346717,"CreatedAt":"2026-06-30T04:09:55.830587294Z","UpdatedAt":"2026-07-02T14:44:57.46949413Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.30395","arxiv_id":"2606.30395","title":"Uncovering Salience-Driven Dynamics in Consumer Confidence with Generative Social Simulation","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.","short_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 reconstr...","url_abs":"https://arxiv.org/abs/2606.30395","url_pdf":"https://arxiv.org/pdf/2606.30395v1","authors":"[\"Yixu Huang\",\"Yunlu Yin\",\"Jiayu Lin\",\"Xinnong Zhang\",\"Jia Wang\",\"Siyuan Wang\",\"Xuanjing Huang\",\"Liyin Jin\",\"Zhongyu Wei\"]","published":"2026-06-29T14:46:22Z","proceeding":"cs.CY","tasks":"[\"cs.CY\",\"cs.CL\",\"cs.SI\"]","methods":"[]","has_code":false}