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Learning to be Biased

Author

Listed:
  • Ch'ng, Kean Siang
  • Zaharim, Norzarina

Abstract

We simulate societal opinion dynamics when there is confirmation bias in information gathering and spread. If decision making is influenced by confirmation bias, the agent puts more weight on positive information to confirm hypothesis or reservation in the learning process, which renders selectivity in information gathering. If the utility discovered post purchase is low, it is externalized rather than internalized (i.e., self blame) for the selectivity of information. This causes the agent to outweigh the negative information. These two mechanisms are simulated to investigate the societal opinion dynamics and explain behavioral patterns such as overconfidence, stickiness of response and ``success breeds success" phenomenon.

Suggested Citation

  • Ch'ng, Kean Siang & Zaharim, Norzarina, 2009. "Learning to be Biased," MPRA Paper 14362, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:14362
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    References listed on IDEAS

    as
    1. Amos Tversky & Daniel Kahneman, 1991. "Loss Aversion in Riskless Choice: A Reference-Dependent Model," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 1039-1061.
    2. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Confirmation bias; Opinion percolation and convergence; Selectivity in information search; Hypothesis testing;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles

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