Sensitivity of the Deffuant model to measurement error v1.0.0

Dino Carpentras

Sensitivity of the Deffuant model to measurement error (version 1.0.0)

Submitted by: Dino Carpentras Software Framework: Jupyter Notebook Python Programming Language: Python

This code can be used to analyze the sensitivity of the Deffuant model to different measurement errors. Specifically to:
- Intrinsic stochastic error
- Binning of the measurement scale
- Random measurement noise
- Psychometric distortions

The script provides graphs for each one of these

Release Notes

First version with the complete analysis of the different type of measurement errors.

Cite this Model

Dino Carpentras (2021, June 07). “Sensitivity of the Deffuant model to measurement error” (Version 1.0.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/4a51fbfb-9117-4561-b915-b023fc4b5bdd/releases/1.0.0/

Replication of a previously described model

https://doi.org/10.1142/S0219525900000078

References

Carpentras, Dino, Alejandro Dinkelberg, and Michael Quayle. “A new degree of freedom for opinion dynamics models: the arbitrariness of scales.” arXiv preprint arXiv:2010.04788 (2020).

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Contributors

Dino Carpentras

DOI

No assigned DOI

Model Version

1.0.0

License

CC-BY-NC-4.0

Operating System

Windows

Programming Language

Python

Dependencies

None listed

Publish Date

Monday, June 07, 2021

Last Updated

Monday, June 07, 2021

Downloads 29

Peer Review

Unreviewed

Model Versions

Version	Submitter	First published	Last modified	Status
1.0.0	Dino Carpentras	Mon Jun 7 18:28:25 2021	Mon Jun 7 18:28:25 2021	Published

Computational Model Library