Wargames as Data: Addressing the Wargamer's Trilemma

Policymakers often want the very best data with which to make decisions--particularly when concerned with questions of national and international security. But what happens when this data is not available? In those instances, analysts have come to rely on synthetic data-generating processes--turning to modeling and simulation tools and survey experiments among other methods. In the cyber domain, where empirical data at the strategic level are limited, this is no different--cyber wargames are quickly becoming a principal method for both exploring and analyzing the security challenges posed by state and non-state actors in cyberspace. In this chapter, we examine the design decisions associated with this method.Comment: 3 figure

The Nuclear Network: Multiplex Network Analysis for Interconnected Systems

States facing the decision to develop a nuclear weapons program do so within a broader context of their relationships with other countries. How these diplomatic, economic, and strategic relationships impact proliferation decisions, however, remains under-specified. Adding to the existing empirical literature that attempts to model state proliferation decisions, this article introduces the first quantitative heterogeneous network analysis of how networks of conflict, alliances, trade, and nuclear cooperation interact to spur or deter nuclear proliferation. Using a multiplex network model, we conceptualize states as nodes linked by different modes of interaction represented on individual network layers. Node strength is used to quantify factors correlated with nuclear proliferation and these are combined in a weighted sum across layers to provide a metric characterizing the proliferation behavior of the state. This multiplex network modeling approach provides a means for identifying states with the highest relative likelihood of proliferation—based only on their relationships to other states. This work demonstrates that latent conflict and nuclear cooperation are positively correlated with proliferation, while an increased trade dependence suggests a decreased proliferation likelihood. A case study on Iran’s controversial nuclear program and past nuclear activity is also provided. These findings have clear, policy-relevant conclusions related to alliance posture, sanctions policy, and nuclear assistance. Abstract ©The Authors

137,138,139^{137,138,139}La(nn, γ\gamma) cross sections constrained with statistical decay properties of 138,139,140^{138,139,140}La nuclei

The nuclear level densities and $\gamma$-ray strength functions of $^{138,139,140}$La were measured using the $^{139}$La($^{3}$He, $\alpha$), $^{139}$La($^{3}$He, $^{3}$He$^\prime$) and $^{139}$La(d, p) reactions. The particle-$\gamma$ coincidences were recorded with the silicon particle telescope (SiRi) and NaI(Tl) (CACTUS) arrays. In the context of these experimental results, the low-energy enhancement in the A$\sim$140 region is discussed. The $^{137,138,139}$La($n, \gamma)$ cross sections were calculated at $s$- and $p$-process temperatures using the experimentally measured nuclear level densities and $\gamma$-ray strength functions. Good agreement is found between $^{139}$La($n, \gamma)$ calculated cross sections and previous measurements

Evidence of the unthinkable: Experimental wargaming at the nuclear threshold

Ongoing nuclear modernization programs in Russia, China, and the USA have reopened longstanding debates among scholars concerning whether tailored nuclear weapons are likely to have destabilizing consequences for international security. Without data to adjudicate this debate, however, these discussions have remained entirely theoretical. In this article, we introduce an experimental wargaming platform, SIGNAL, to quantify the effect of tailored nuclear capabilities on the nuclear threshold in a simulated environment. We then compare these results with a survey experiment using scenarios related to military basing, cyber operations, and nuclear threats from the wargame environment. While the survey experiments suggest that the presence of tailored nuclear capabilities increases the likelihood of conflict escalation, this trend diminishes in the wargaming context. Across both data-generating processes, we find support for the proposition that lower-yield nuclear weapons are used as a substitute for their higher-yield counterparts. These results have consequences for recent and ongoing policy debates concerning strategic posture and the future of arms control. This work also makes methodological contributions to the design and application of experimental wargaming for social science research, particularly for scenarios where data are limited or non-existent

Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain

Iron-rich microstructure records of high temperature multi-component silicate melt behavior in nuclear fallout.

Above-ground nuclear explosions that interact with the surface of the earth entrain materials from the surrounding environment, influencing the resulting physical and chemical evolution of the fireball, which can affect the final chemical phase and mobility of hazardous radionuclides that are dispersed in the environment as fallout particles. The interaction of iron with a nuclear explosion is of specific interest due to the potential for iron to act as a redox buffer and because of the likelihood of significant masses of metals to be present in urban environments. We investigated fallout from a historic surface interacting nuclear explosion conducted on a steel tower and report the discovery of widespread and diverse iron-rich micro-structures preserved within the samples, including crystalline dendrites and micron-scale iron-rich spheres with liquid immiscibility textures. We assert these micro-structures reflect local redox conditions and cooling rates and can inform interpretation of high temperature events, enabling new insights into fireball condensation physics and chemistry when metals from the local environment (i.e. structural steel) are vaporized or entrained. These observations also significantly expand the availability of silicate immiscibility datasets applicable to rapidly quenched systems such as meteorite impact melt glass

Next-generation wargames.

Technology enables new research designs, and more dat

Neutron Response of the EJ-254 Boron-Loaded Plastic Scintillator

Organic scintillators doped with capture agents provide a detectable signal for neutrons over a broad energy range. This work characterizes the fast and slow neutron response of EJ-254, an organic plastic scintillator with 5% natural boron loading by weight. For fast neutrons, the primary mechanism for light generation in organic scintillators is n-p elastic scattering. To study the fast neutron response, the proton light yield of EJ-254 was measured at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. Using a broad-spectrum neutron source and a double time-of-flight technique, the EJ-254 proton light yield was obtained over the energy range of approximately 270 keV to 4.5 MeV and determined to be in agreement with other plastic scintillators comprised of the same polymer base. To isolate the slow neutron response, an AmBe source with polyethylene moderator was made incident on the EJ-254 scintillator surrounded by an array of EJ-309 observation detectors. Events in the EJ-254 target coincident with the signature 477.6 keV $\gamma$ ray (resulting from deexcitation of the residual $^{7}$Li nucleus following boron neutron capture) were identified. Pulse shape discrimination was used to evaluate the temporal differences in the response of EJ-254 scintillation signals arising from $\gamma$-ray and fast/slow neutron interactions. Clear separation between $\gamma$-ray and fast neutrons signals was not achieved and the neutron capture feature was observed to overlap both the $\gamma$-ray and fast neutron bands. Taking into account the electron light nonproportionality, the neutron-capture light yield in EJ-254 was determined to be 89.4$\pm$1.1 keVee.Comment: 9 pages, 10 figure

Enhanced low-energy gamma-decay probability − implications for r-process (n,g) reaction rates

info:eu-repo/semantics/publishe