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Conflict resolution through mutuality: Lessons from Bayesian updating

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  • Srijit Mishra

    (Indira Gandhi Institute of Development Research)

Abstract

If priors are deterministic (zero or unity) and conditional evidence is uncertain (lies between zero and one) then Bayesian updating will lead to posteriors that are the same as priors. This in a sense explains the persistence of fundamentalist belief. Under such a belief system, only if conditional evidence is deterministic and diametrically opposite to that of the prior then a process of change can set in. Conflict resolution is possible through dialogues that calls for mutual respect and allows reasonable pluralism - a Rawlsian prerequisite. If interaction is the basis then self-defeating scenarios can be avoided by giving space to others. Thus, in the political sphere one has to be accommodative.

Suggested Citation

  • Srijit Mishra, 2011. "Conflict resolution through mutuality: Lessons from Bayesian updating," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2011-001, Indira Gandhi Institute of Development Research, Mumbai, India.
    • Handle: RePEc:ind:igiwpp:2011-001
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    File URL: http://www.igidr.ac.in/pdf/publication/WP-2011-001.pdf
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    1. Srijit Mishra, 2011. "Conflict Resolution through Mutuality: Lessons from Bayesian Updating," Journal of Quantitative Economics, The Indian Econometric Society, vol. 9(1), pages 41-52.
    2. Srijit Mishra, 2011. "Conflict Resolution through Mutuality: Lessons from Bayesian Updating," Journal of Quantitative Economics, The Indian Econometric Society, vol. 9(1), pages 41-52.

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    More about this item

    Keywords

    Bayesian updating; belief polarization; conflict resolution; fundamentalist belief; interaction; mutual respect; reasonable pluralism;
    All these keywords.

    JEL classification:

    • A13 - General Economics and Teaching - - General Economics - - - Relation of Economics to Social Values
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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