Differences of Opinion

Dionissi Aliprantis

Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, PMLR 62:1-12, 2017.

Abstract

This paper considers the resolution of ambiguity according to the scientific ideal of direct observation when there is a practical necessity for social learning. An agent faces ambiguity when she directly observes low-quality data yielding set-identified signals. I suppose the agent’s objective is to choose the single belief replicating what would occur with high-quality data yielding point-identified signals. I allow the agent to solve this missing data problem using signals observed through her network in combination with a model of social learning. In some cases the agent’s belief formation reduces to DeGroot updating and beliefs in a network reach a consensus. In other cases the agent’s updating can generate polarization and sustain clustered disagreement, even on a connected network where everyone observes the same data and processes that data with the same model.

Cite this Paper

BibTeX


@InProceedings{pmlr-v62-aliprantis17a,
  title = 	 {Differences of Opinion},
  author = 	 {Aliprantis, Dionissi},
  booktitle = 	 {Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications},
  pages = 	 {1--12},
  year = 	 {2017},
  editor = 	 {Antonucci, Alessandro and Corani, Giorgio and Couso, Inés and Destercke, Sébastien},
  volume = 	 {62},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--14 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v62/aliprantis17a/aliprantis17a.pdf},
  url = 	 {https://proceedings.mlr.press/v62/aliprantis17a.html},
  abstract = 	 {This paper considers the resolution of ambiguity according to the scientific ideal of direct observation when there is a practical necessity for social learning. An agent faces ambiguity when she directly observes low-quality data yielding set-identified signals. I suppose the agent’s objective is to choose the single belief replicating what would occur with high-quality data yielding point-identified signals. I allow the agent to solve this missing data problem using signals observed through her network in combination with a model of social learning. In some cases the agent’s belief formation reduces to DeGroot updating and beliefs in a network reach a consensus. In other cases the agent’s updating can generate polarization and sustain clustered disagreement, even on a connected network where everyone observes the same data and processes that data with the same model.}
}

Endnote

%0 Conference Paper
%T Differences of Opinion
%A Dionissi Aliprantis
%B Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications
%C Proceedings of Machine Learning Research
%D 2017
%E Alessandro Antonucci
%E Giorgio Corani
%E Inés Couso
%E Sébastien Destercke	
%F pmlr-v62-aliprantis17a
%I PMLR
%P 1--12
%U https://proceedings.mlr.press/v62/aliprantis17a.html
%V 62
%X This paper considers the resolution of ambiguity according to the scientific ideal of direct observation when there is a practical necessity for social learning. An agent faces ambiguity when she directly observes low-quality data yielding set-identified signals. I suppose the agent’s objective is to choose the single belief replicating what would occur with high-quality data yielding point-identified signals. I allow the agent to solve this missing data problem using signals observed through her network in combination with a model of social learning. In some cases the agent’s belief formation reduces to DeGroot updating and beliefs in a network reach a consensus. In other cases the agent’s updating can generate polarization and sustain clustered disagreement, even on a connected network where everyone observes the same data and processes that data with the same model.

APA


Aliprantis, D.. (2017). Differences of Opinion. Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 62:1-12 Available from https://proceedings.mlr.press/v62/aliprantis17a.html.

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