Bayesian Inverse Sampling and Shapley-Driven Calibration for Dual-Channel Preference Aggregation
DOI:
https://doi.org/10.54097/erpkp016Keywords:
Bayesian Inverse Sampling, Shapley Additive Attribution, Gradient Boosting Regression, Dual-Quantile Calibration, Dynamic Weight Allocation, Heterogeneous Preference AggregationAbstract
This paper presents a dual-channel preference aggregation framework that reconstructs latent user voting distributions and calibrates heterogeneous scoring channels through Bayesian inverse sampling and gradient boosting attribution. The target setting is a mixed-evaluation ranking system where expert scores and user votes drive elimination jointly. Three coupled problems are addressed: latent preference recovery from elimination outcomes, normalization-induced bias quantification across scoring schemes, and dynamic weight allocation under temporal drift. A Monte Carlo inverse sampling module reconstructs latent voting distributions across 34 episodes by enforcing elimination-consistency constraints with Bayesian priors, raising the simulation acceptance rate from 28.6% to 44.6%. A share dispersion metric quantifies variance convergence near elimination boundaries, with eliminated candidates registering dispersion 0.0252 against 0.0601 for advancing candidates. The Fan Dominance Index measures normalization-induced bias. Percentage-based aggregation amplifies user signals more aggressively than rank-based aggregation, while a normalized expert override neutralizes excessive popularity influence. An XGBoost regression with Shapley attribution reaches R-squared of 0.906, with candidate age (weight 0.324) as the dominant judging predictor and a per-expert efficacy hierarchy that registers marginal premiums up to +0.603. The D.A.R.E. system integrates dual-quantile calibration and dynamic weighting, and remains robust under 275 weeks of simulation with extreme voting fluctuations.
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