Academic Journal of Computing & Information Science, 2026, 9(7); doi: 10.25236/AJCIS.2026.090713.
Yu Pei1, Yitong Ling2, Xinyu Li3
1School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Suzhou, China
2School of Intelligent Manufacturing Ecosystem, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Suzhou, China
3School of Robotics, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Suzhou, China
Elimination competitions combine expert judgement with audience participation, yet public datasets usually report judge scores and elimination outcomes without revealing fan votes. This missing-observation structure makes it difficult to determine whether a contestant survived because of technical quality, latent popularity, or the rule used to aggregate both signals. This paper develops a Bayesian replay framework for latent fan-vote estimation and voting-rule bias analysis. A latent popularity utility is mapped to weekly fan-share probabilities through a softmax function, combined with standardized judge scores in an elimination likelihood, and smoothed over time to obtain posterior fan-support distributions. These posterior samples are then replayed under rank aggregation, percentage aggregation, and a bottom-two judges-save mechanism. The results indicate that the estimated latent fan signal is predictive and calibrated, with overall Hit@3 close to 0.69 and expected calibration error near 0.0047. Counterfactual replay further shows that rule choice changes placement and elimination timing, particularly for controversial contestants. The judges-save mechanism increases judge influence, weakens the connection between fan support and final placement, and generates heterogeneous winner/loser effects.
Latent Fan Vote; Bayesian Inference; Posterior Replay; Voting Rule; Rule Bias; Elimination Competition
Yu Pei, Yitong Ling, Xinyu Li. A Bayesian Replay Framework for Latent Fan Vote Estimation and Voting Rule Bias Analysis. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 7: 100-109. https://doi.org/10.25236/AJCIS.2026.090713.
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