# Bias and Identifiability in the Bounded Confidence Model

arXiv: https://arxiv.org/abs/2506.11751 · HTML: https://mlsanity.com/paper/2506.11751/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021)
- [On structural and practical identifiability](https://mlsanity.com/paper/2102.05100/) (2021)
- [Learning Opinion Dynamics From Social Traces](https://mlsanity.com/paper/2006.01673/) (2020)
- [On learning agent-based models from data](https://mlsanity.com/paper/2205.05052/) (2022)
- [Black-box Bayesian inference for economic agent-based models](https://mlsanity.com/paper/2202.00625/) (2022)
- [Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization](https://mlsanity.com/paper/2112.03874/) (2021)
- [A Bayesian Optimization approach for calibrating large-scale activity-based transport models](https://mlsanity.com/paper/2302.03480/) (2023)
- [Likelihood-Based Methods Improve Parameter Estimation in Opinion Dynamics Models](https://mlsanity.com/paper/2310.02766/) (2023)
- [Variational Inference of Parameters in Opinion Dynamics Models](https://mlsanity.com/paper/2403.05358/) (2024)

## Built on by (most cited first, 1 of 1)

- [Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models](https://mlsanity.com/paper/2509.17625/) (2025)

## Abstract (arXiv)

Opinion dynamics models such as the bounded confidence models (BCMs) describe how a population can reach consensus, fragmentation, or polarization, depending on a few parameters. Connecting such models to real-world data could help understanding such phenomena, testing model assumptions. To this end, estimation of model parameters is a key aspect, and maximum likelihood estimation provides a principled way to tackle it. Here, our goal is to outline the properties of statistical estimators of the two key BCM parameters: the confidence bound and the convergence rate. We find that their maximum likelihood estimators present different characteristics: the one for the confidence bound presents a small-sample bias but is consistent, while the estimator of the convergence rate shows a persistent bias. Moreover, the joint parameter estimation is affected by identifiability issues for specific regions of the parameter space, as several local maxima are present in the likelihood function. Our results show how the analysis of the likelihood function is a fruitful approach for better understanding the pitfalls and possibilities of estimating the parameters of opinion dynamics models, and more in general, agent-based models, and for offering formal guarantees for their calibration.