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Uncertainty and Conflict Decision

Author

Listed:
  • Natasa Bilkic

    () (University of Paderborn)

  • Thomas Gries

    () (University of Paderborn)

Abstract

Though violent conflicts bear dramatic uncertainties, there is no closed theory addressing large uncertainties in conflict decisions. If a violent conflict is an investment in social, political, or economic change for rebels, how do large uncertainties like threatening waves of persecution affect their decision to turn violent? In order to capture such discontinuous uncertain large stochastic shocks we apply a real option model and introduce a more general stochastic process than the often used geometric Brownian motion, namely an Ito-Lévy Jump Diffusion processes. A major result is that large uncertain shocks have opposite effects on the decision to launch a conflict than small variations, indicated by normal volatility. While an increase in volatility of e.g. general economic conditions, indicated by (marginal) volatility, would give hope and postpone a potential attack, more uncertain large threats, indicated by discontinuous negative shock, will lead to an earlier outbreak of conflict. Hence, in latent conflicts, either general disaster shocks, or even deliberately announced oppressive threats by a government, often meant to awe the oppressed group, may provoke a violent outbreak, if the threat is strong enough.

Suggested Citation

  • Natasa Bilkic & Thomas Gries, 2014. "Uncertainty and Conflict Decision," Working Papers CIE 78, Paderborn University, CIE Center for International Economics.
  • Handle: RePEc:pdn:ciepap:78
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    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP78.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    theory of social conflict; decision under uncertainty; non-marginal stochastic shocks;

    JEL classification:

    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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