Abstract
This paper provides an integrated structure that defines conditions for the success and failure of deterrence. The conditional deterrence model presented can be applied to global and regional interactions that drive nuclear proliferation. The objective is to further extend the dyadic logic established to anticipate challenges generated by the proliferation of nuclear capabilities and their acquisitions by non-state dissatisfied agents. Key elements included in this assessment are relative capabilities, risk propensity associated with the status quo, and physical exposure to preemptive-attack or retaliation. This work uses ABM to generalize insights to deterrence environments with multiple competing actors. We show that deterrence is stable when the capabilities of a dissatisfied challenger are inferior to that of a dominant and satisfied defender. Deterrence is tenuous when a dissatisfied challenger approaches parity in capability with the dominant and satisfied defender, or when a violent non-state actor obtains nuclear weapon or other WMDs.
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Notes
- 1.
Although risk is defined in various ways by different disciplines, I consider a risky event to be any event where the outcome is not known with certainty in advance. To evaluate an unknown future, I follow the finance approach to the concept of risk where risk represents a quantifiable source of uncertainty, as distinguished by Knight [12]. Specifically, risk is defined in terms of the quantifiable variability of actual outcomes around an expected outcome [13]. Such notions are frequently used in financial risk assessment because it allows risk to be conceptualized in a fashion that can be incorporated into a decision on how much to hedge against or exploit a specific risk (for detailed discussions, see Morgan and Henrion [14], Varian [15], Damodaran [16]). As will be shown later, I will rely on the mean-variance analysis commonly used in financial economics to represent risk.
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Yang, Z., Kang, K., Kugler, J. (2018). An Agent-Based Approach on Conditional Deterrence. In: Cassenti, D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2017. Advances in Intelligent Systems and Computing, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-60591-3_30
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