Jonathan Teagan | Searcharxiv

arXiv2025

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models

This study applies a range of forecasting techniques,including ARIMA, Prophet, Long Short Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and XGBoost, to model and predict Russian equipment losses during the ongoing war in Ukraine. Drawing on daily and monthly open-source intelligence (OSINT) data from WarSpotting, we aim to assess trends in attrition, evaluate model performance, and estimate future loss patterns through the end of 2025. Our findings show that deep learning models, particularly TCN and LSTM, produce stable and consistent forecasts, especially under conditions of high temporal granularity. By comparing different model architectures and input structures, this study highlights the importance of ensemble forecasting in conflict modeling, and the value of publicly available OSINT data in quantifying material degradation over time.

cs.LG↗

arXiv2025

The Economics and Game Theory of OSINT Frontline Photography: Risk, Attention, and the Collective Dilemma

This paper develops an economic model of the Open Source Intelligence (OSINT) attention economy in contemporary armed conflict. We conceptualize attention (e.g. social media views, followers, likes) as revenue, and time and risk spent in analysis as costs. Using utility functions and simple game theoretic setups, we show how OSINT actors (amateurs, journalists, analysts, and state operatives) allocate effort to maximize net attention benefit. We incorporate strategic behaviors such as a first mover advantage (racing to publish) and prisoner's dilemma scenarios (to share information or hold it back). In empirical case studies, especially the Ukraine conflict actors like the UAV unit Madyar's Birds and volunteer channels like Kavkazfighter, illustrate how battlefront reporting translates into digital revenue (attention) at real cost. We draw on recent literature and data (e.g., public follower counts, viral posts) to examine trends such as OSINT virality. Finally, we discuss policy implications for balancing transparency with operational security, citing calls for verification ethics and attention sustaining narratives. Our analysis bridges conflict studies and economics, highlighting OSINT as both a public good and a competitive product in today's information war.

econ.GN↗