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Dimensionality And Disagreement: Asymptotic Belief Divergence In Response To Common Information

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  • Isaac Loh
  • Gregory Phelan

Abstract

We provide a model of boundedly rational, multidimensional learning and characterize when beliefs will converge to the truth. Agents maintain beliefs as marginal probabilities instead of joint probabilities, and agents' information is of lower dimension than the model. As a result, for some observations, agents may face an identification problem affecting the role of data in inference. Beliefs converge to the truth when these observations are rare, but beliefs diverge when observations presenting an identification problem are frequent. Robustly, two agents with differing priors who observe identical, unambiguous information may disagree forever, with stronger disagreement the more information received.

Suggested Citation

  • Isaac Loh & Gregory Phelan, 2019. "Dimensionality And Disagreement: Asymptotic Belief Divergence In Response To Common Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 60(4), pages 1861-1876, November.
    • Handle: RePEc:wly:iecrev:v:60:y:2019:i:4:p:1861-1876
    DOI: 10.1111/iere.12406
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    Cited by:

    1. Benoît, Jean-Pierre & Dubra, Juan, 2018. "When do populations polarize? An explanation," MPRA Paper 86173, University Library of Munich, Germany.
    2. Gabriel Martinez & Nicholas H. Tenev, 2020. "Optimal Echo Chambers," Papers 2010.01249, arXiv.org, revised Feb 2024.
    3. Axel Anderson & Nikoloz Pkhakadze, 2023. "Polarizing Persuasion," Working Papers gueconwpa~23-23-04, Georgetown University, Department of Economics.
    4. Stone, Daniel, 2018. "Just a big misunderstanding? Bias and Bayesian affective polarization," SocArXiv 58sru, Center for Open Science.

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