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March 7, 2026Royal Society Open ScienceOpen Access

Bias and identifiability in the bounded confidence model

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Authors

CBClaudio BorileJLJacopo LentiVGValentina Ghidini

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Overview

This analysis outlines parameter estimation in bounded confidence models, revealing implications for model accuracy.

Key Points

  • Outline the properties of statistical estimators for key parameters in bounded confidence models.
  • Analyzed the maximum likelihood estimation of the confidence bound and convergence rate.
  • Examined the characteristics of estimators in small sample conditions.
  • Investigated identifiability issues related to local maxima in the likelihood function.
  • Confidence bound estimator shows small-sample bias but is consistent.
  • Convergence rate estimator exhibits persistent bias.
  • Identifiability issues arise in certain regions of the parameter space.

Cite This Study

Borile et al. (2026) studied this question.

synapsesocial.com/papers/69abc2615af8044f7a4ebf28https://doi.org/10.1098/rsos.251253
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