Expert Insight-Based Modeling of Non-Kinetic Strategic Deterrence of Rare Earth Supply Disruption:A Simulation-Driven Systematic Framework
- URL: http://arxiv.org/abs/2506.11645v1
- Date: Fri, 13 Jun 2025 10:18:59 GMT
- Title: Expert Insight-Based Modeling of Non-Kinetic Strategic Deterrence of Rare Earth Supply Disruption:A Simulation-Driven Systematic Framework
- Authors: Wei Meng,
- Abstract summary: This study constructs a quantifiable modelling framework to simulate non-kinetic strategic deterrence pathways in rare earth supply disruption scenarios.<n>Data is derived from expert interviews and scenario analyses centered on U.S.-China dynamics in ISR, electronic warfare, and rare earth control.<n>Results show institutional signals have strong tempo and path-coupling effects, capable of causing rapid degradation of strategic capabilities.
- Score: 3.5516803380598074
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This study constructs a quantifiable modelling framework to simulate non-kinetic strategic deterrence pathways in rare earth supply disruption scenarios, based on structured responses from expert interviews led by Dr. Daniel O'Connor, CEO of the Rare Earth Exchange (REE). Focusing on disruption impacts on national security systems, the study proposes four core modelling components: Security Critical Zones (SCZ), Strategic Signal Injection Function (SSIF), System-Capability Migration Function (SCIF), and Policy-Capability Transfer Function (PCTF). The framework integrates parametric ODEs, segmented function modelling, path-overlapping covariance matrices, and LSTM networks to simulate nonlinear suppression trajectories triggered by regime signals. Data is derived from expert interviews and scenario analyses centered on U.S.-China dynamics in ISR, electronic warfare, and rare earth control. Results show institutional signals have strong tempo and path-coupling effects, capable of causing rapid degradation of strategic capabilities. The model is adaptable across national resource frameworks and extendable to AI sandbox engines for situational simulation and counterfactual reasoning. This research introduces the first unified system for modelling, visualizing, and forecasting non-kinetic deterrence, offering methodological support to policymakers and analysts navigating institutionalized strategic competition.
Related papers
- TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training [53.93696896939915]
Training tool-use agents typically rely on Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks.<n>We propose TopoCurate, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology.<n>TopoCurate achieves consistent gains of 4.2% (SFT) and 6.9% (RL) over state-of-the-art baselines.
arXiv Detail & Related papers (2026-03-02T10:38:54Z) - Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance [86.46794021499511]
We show a previously underexplored gap between strategy usage and strategy executability.<n>We propose Selective Strategy Retrieval (SSR), a test-time framework that explicitly models executability.<n> SSR yields reliable and consistent improvements over direct solving, in-context learning, and single-source guidance.
arXiv Detail & Related papers (2026-02-26T03:34:23Z) - Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting [63.8116386935854]
We demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized trainings.<n>We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector.<n>We find that our framework design is robust to the choice of probabilistic estimators, seamlessly supporting interpolants, diffusion models, and CRPS-based ensemble training.
arXiv Detail & Related papers (2026-01-26T03:52:16Z) - Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
arXiv Detail & Related papers (2026-01-25T12:47:25Z) - Towards agent-based-model informed neural networks [0.5787117733071417]
We present a framework for designing neural networks consistent with the underlying principles of agent-based models.<n>We validate the framework across three case studies of increasing complexity.
arXiv Detail & Related papers (2025-12-05T14:50:50Z) - Strategic Counterfactual Modeling of Deep-Target Airstrike Systems via Intervention-Aware Spatio-Causal Graph Networks [3.5516803380598074]
This study addresses the lack of structured causal modeling between tactical strike behavior and strategic delay in current strategic-level simulations.<n>We propose the Intervention-Aware Spatio-Temporal Graph Neural Network (IA-STGNN), a novel framework that closes the causal loop from tactical input to strategic delay output.
arXiv Detail & Related papers (2025-06-30T04:26:10Z) - Topology-Assisted Spatio-Temporal Pattern Disentangling for Scalable MARL in Large-scale Autonomous Traffic Control [14.929720580977152]
This paper introduces a novel MARL framework that integrates Dynamic Graph Neural Networks (DGNNs) and Topological Data Analysis (TDA)<n>Inspired by the Mixture of Experts (MoE) architecture in Large Language Models (LLMs), a topology-assisted spatial pattern disentangling (TSD)-enhanced MoE is proposed.<n> Extensive experiments conducted on real-world traffic scenarios, together with comprehensive theoretical analysis, validate the superior performance of the proposed framework.
arXiv Detail & Related papers (2025-06-14T11:18:12Z) - Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction [10.21659221112514]
We introduce a novel trajectory prediction framework that leverages causal inference to enhance predictive robustness, generalization, and accuracy.<n>Our findings highlight the potential of causal reasoning to transform trajectory prediction, paving the way for robust autonomous driving systems.
arXiv Detail & Related papers (2025-05-11T05:56:07Z) - Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator [50.191655141020505]
Reinforcement Learning (RL) has demonstrated impressive capabilities in robotic control but remains challenging due to high sample complexity, safety concerns, and the sim-to-real gap.<n>We introduce Offline Robotic World Model (RWM-O), a model-based approach that explicitly estimates uncertainty to improve policy learning without reliance on a physics simulator.
arXiv Detail & Related papers (2025-04-23T12:58:15Z) - Generalized Factor Neural Network Model for High-dimensional Regression [50.554377879576066]
We tackle the challenges of modeling high-dimensional data sets with latent low-dimensional structures hidden within complex, non-linear, and noisy relationships.<n>Our approach enables a seamless integration of concepts from non-parametric regression, factor models, and neural networks for high-dimensional regression.
arXiv Detail & Related papers (2025-02-16T23:13:55Z) - A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty [0.0]
This study presents the Occam Plausibility Algorithm for surrogate models (OPAL-surrogate)
OPAL-surrogate provides a systematic framework to uncover predictive neural network-based surrogate models.
It balances the trade-off between model complexity, accuracy, and prediction uncertainty.
arXiv Detail & Related papers (2024-03-13T18:45:51Z) - Towards Robust Federated Learning via Logits Calibration on Non-IID Data [49.286558007937856]
Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks.
Recent studies have shown that FL is vulnerable to adversarial examples, leading to a significant drop in its performance.
In this work, we adopt the adversarial training (AT) framework to improve the robustness of FL models against adversarial example (AE) attacks.
arXiv Detail & Related papers (2024-03-05T09:18:29Z) - Hierarchical Framework for Interpretable and Probabilistic Model-Based
Safe Reinforcement Learning [1.3678669691302048]
This paper proposes a novel approach for the use of deep reinforcement learning in safety-critical systems.
It combines the advantages of probabilistic modeling and reinforcement learning with the added benefits of interpretability.
arXiv Detail & Related papers (2023-10-28T20:30:57Z) - DR-Label: Improving GNN Models for Catalysis Systems by Label
Deconstruction and Reconstruction [72.20024514713633]
We present a novel graph neural network (GNN) supervision and prediction strategy DR-Label.
The strategy enhances the supervision signal, reduces the multiplicity of solutions in edge representation, and encourages the model to provide node predictions robust.
DR-Label was applied to three radically distinct models, each of which displayed consistent performance enhancements.
arXiv Detail & Related papers (2023-03-06T04:01:28Z) - Exploiting Temporal Structures of Cyclostationary Signals for
Data-Driven Single-Channel Source Separation [98.95383921866096]
We study the problem of single-channel source separation (SCSS)
We focus on cyclostationary signals, which are particularly suitable in a variety of application domains.
We propose a deep learning approach using a U-Net architecture, which is competitive with the minimum MSE estimator.
arXiv Detail & Related papers (2022-08-22T14:04:56Z) - On the benefits of robust models in modulation recognition [53.391095789289736]
Deep Neural Networks (DNNs) using convolutional layers are state-of-the-art in many tasks in communications.
In other domains, like image classification, DNNs have been shown to be vulnerable to adversarial perturbations.
We propose a novel framework to test the robustness of current state-of-the-art models.
arXiv Detail & Related papers (2021-03-27T19:58:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.