arXiv subjects

Asmeret Naugle

Publications and source records attributed to Asmeret Naugle.

5 recordsLinked to original sources

arXiv2026

AI Sovereignty: A Qualitative Model of Strategic Competition as AI Becomes an Instrument of National Power

AI sovereignty is the extent to which a nation independently controls its artificial intelligence (AI) technologies. The race toward ever-more-sophisticated frontier AI models is of increasing strategic importance, with nations considering how AI might improve their economic situations, competitive advantage, and overall national power. However, the costs of AI sovereignty are enormous, and we lack definitions and conceptual models to navigate evolving AI sovereignty dynamics. We address this gap with definitions relevant to AI sovereignty, along with a first-of-its-kind qualitative model that incorporates micro, meso, and macro contributors. Model-based qualitative forecasts highlight competitive dynamics and evolving potential for AI-driven national power. The model identifies key leverage points that nations can use to enhance their own growth or degrade an adversary's, including consideration of accelerators, electricity, water, data sets and skilled workforce. These leverage points can be activated at strategic and operational levels through both direct kinetic actions, such as Iran's targeting of data centers with drones, and indirect non-kinetic effects including cyber, space, information, economic coercion and diplomacy. If our assumptions and hypotheses are valid, this strategic competition may come to define how nations improve their economic situations, competitive advantage, and overall national power in the 21st Century.

cs.CY↗

arXiv2025

Trust-Aware Multimodal Data Fusion for Yield Estimation: A Case Study of the 2020 Beirut Explosion

The estimation of explosive yield from heterogeneous observational data presents fundamental challenges in inverse problems, particularly when combining traditional physical measurements with modern artificial intelligence-interpreted modalities. We present a novel Bayesian fractional posterior framework that fuses seismic waves, crater dimensions, synthetic aperture radar imagery, and vision-language model interpreted ground-level images to estimate the yield of the 2020 Beirut explosion. Unlike conventional approaches that may treat data sources equally, our method learns trust weights for each modality through a Dirichlet prior, automatically calibrating the relative information content of disparate observations. Applied to the Beirut explosion, the framework yields an estimate of 0.34--0.48 kt TNT equivalent, representing 12 to 17 percent detonation efficiency relative to the 2.75 kt theoretical maximum from the blast's stored ammonium nitrate. The fractional posterior approach demonstrates superior uncertainty quantification compared to single-modality estimates while providing robustness against systematic biases. This work establishes a principled framework for integrating qualitative assessments with quantitative physical measurements, with applications to explosion monitoring, disaster response, and forensic analysis.

stat.AP↗

arXiv2025

The Trust Calibration Maturity Model for Characterizing and Communicating Trustworthiness of AI Systems

Recent proliferation of powerful AI systems has created a strong need for capabilities that help users to calibrate trust in those systems. As AI systems grow in scale, information required to evaluate their trustworthiness becomes less accessible, presenting a growing risk of using these systems inappropriately. We propose the Trust Calibration Maturity Model (TCMM) to characterize and communicate information about AI system trustworthiness. The TCMM incorporates five dimensions of analytic maturity: Performance Characterization, Bias & Robustness Quantification, Transparency, Safety & Security, and Usability. The TCMM can be presented along with system performance information to (1) help a user to appropriately calibrate trust, (2) establish requirements and track progress, and (3) identify research needs. Here, we discuss the TCMM and demonstrate it on two target tasks: using ChatGPT for high consequence nuclear science determinations, and using PhaseNet (an ensemble of seismic models) for categorizing sources of seismic events.

cs.HC↗

arXiv2024

Toward path-invariant embeddings for local distance source characterization

This work builds on recent advances in foundation models in the language and image domains to explore similar approaches for seismic source characterization. We rely on an architecture called Barlow Twins, borrowed from an understanding of the human visual cortical system and originally envisioned for the image domain and adapt it for learning path invariance in seismic event time series. Our model improves the performance on event characterization tasks such as source discrimination across catalogs by 10-12% and provides more reliable predictive uncertainty estimates. We suggest that dataset scale and diversity more than architecture may determine aspects of the current ceiling on performance. We leverage decision trees, linear models, and visualization to understanding the dependencies in learned representations.

cs.CE↗

arXiv2023

What is System Dynamics Modeling? Defining Characteristics and the Opportunities they Create

A clear definition of system dynamics modeling can provide shared understanding and clarify the impact of the field. We introduce a set of characteristics that define quantitative system dynamics, selected to capture core philosophy, describe theoretical and practical principles, and apply to historical work but be flexible enough to remain relevant as the field progresses. The defining characteristics are: (1) models are based on causal feedback structure, (2) accumulations and delays are foundational, (3) models are equation-based, (4) concept of time is continuous, and (5) analysis focuses on feedback dynamics. We discuss the implications of these principles and use them to identify research opportunities in which the system dynamics field can advance. These research opportunities include causality, disaggregation, data science and artificial intelligence, and contributing to scientific advancement. Progress in these areas has the potential to improve both the science and practice of system dynamics.

eess.SY↗