A probabilistic analysis of the risk of nuclear deterrence failure
Abstract/Contents
- Abstract
- This research develops a quantitative risk analytic method to assess the risk of deterrence failure given modern nuclear weapon arsenals and policies. The model includes multiple antagonist behaviors, levels of conflict escalation, weapon capabilities and effects, and a spectrum of policies, both for protagonists and antagonists. It is based on infinite-horizon, risk-sensitive Interactive Partially Observable Markov Decision Processes. This model allows multiple agents to identify optimal policies in the management of conflict scenarios given the trade-offs between their political goals and the consequences of various forms of conflict. We develop a set of metrics for deterrence effectiveness based on the probability of specific opponent actions and on the evaluation of different conflict outcomes. The model and analysis capture complex behaviors and escalation dynamics, identify approximately optimal policies in specific conflicts, and can be extended to a large spectrum of possible scenarios. An illustration is presented, based on the analysis of fictitious data for a bilateral conflict scenario between two nuclear-armed, peer states. In this example, we evaluate various nuclear arsenals and stated policies about their use, based on the results of the model, including optimal conflict management and comparison of deterrence-effectiveness metrics. The results can provide valuable insights to policy and decision makers by allowing them to consider a spectrum of consequences involved in various alternatives.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Reinhardt, Jason Christian | |
|---|---|---|
| Degree supervisor | Paté-Cornell, M. Elisabeth (Marie Elisabeth) | |
| Thesis advisor | Paté-Cornell, M. Elisabeth (Marie Elisabeth) | |
| Thesis advisor | Hecker, Siegfried S | |
| Thesis advisor | Kochenderfer, Mykel J, 1980- | |
| Thesis advisor | Nacht, Michael | |
| Degree committee member | Hecker, Siegfried S | |
| Degree committee member | Kochenderfer, Mykel J, 1980- | |
| Degree committee member | Nacht, Michael | |
| Associated with | Stanford University, Department of Management Science and Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jason Christian Reinhardt. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Jason Christian Reinhardt
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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