Güvenlik insanoğlunun en temel ihtiyaçlarından biridir. Günümüzdeki güvenlik kavramı bireyi, toplumu ve devletleri de kapsayan geniş bir kümedir. Güvenliğin ulusal ve uluslararası düzeyde sağlanabilmesi için, devletlerin kendilerini savunabilme kabiliyetlerini geliştirmeleri gerekmektedir. Savunma her türlü tehdide karşı kendini korumak olarak tanımlanabilir. Hızla gelişen teknoloji savunma sanayisinde de etkilerini göstererek, savunma harcamalarını dinamik bir hale getirmiştir. Devletler, kendi güvenliklerini sağlayabilmek için bu hızlı gelişen teknolojiye ayak uydurmalı, doğru ve stratejik savunma yatırımları zorundalardır. Bu çalışmada uluslararası silah ticaretiyle ilgili bilgiler verilmekte, savunma harcamalarının tanımı ve sınıflandırılması yapılmakta, Dünyada savunma harcamalarıyla ilgili çeşitli bilgiler sunulmaktadır.

This paper investigates how economic capacity, budget priorities, and governance conditions jointly shape defense spending in a transparent and auditable empirical framework. Using annual cross-country data for 2002–2023 compiled from widely used international sources, we evaluate the relative importance of economic indicators, fiscal allocation patterns, and institutional factors in explaining defense expenditure outcomes. We use Explainable Artificial Intelligence (XAI) to quantify the relative contribution of these factors in a transparent and interpretable manner. The results show that economic capacity and the way governments prioritize defense within overall public finances are the strongest and most consistent drivers of defense spending differences across countries. Governance conditions act as an institutional filter that can constrain or intensify these effects: more democratic and accountable environments tend to limit increases in defense spending, whereas lower political stability is associated with upward spending pressure. These findings are important because they clarify that defense expenditure is not determined by a single factor, but by a layered interaction between resources, priorities, and institutions. The study contributes a replicable and policy-relevant approach for interpreting defense spending dynamics and for supporting accountable decision-making in defense budgeting.

Financial markets exhibit complex nonlinear dynamics driven by interactions between firm-specific characteristics and macroeconomic conditions, requiring robust mathematical models capable of capturing structural changes and temporal heterogeneity. This study proposes an explainable machine learning framework that combines regime-switching analysis and predictive modeling to investigate the long-term resilience of firms listed in the BIST 100 Index. Structural breaks in monthly return and volatility series are first detected using the Pruned Exact Linear Time (PELT) algorithm, enabling the classification of firm trajectories into resilient and fragile market regimes. A Regime Persistence Score is then introduced to quantify the proportion of time each firm remains in the resilient state. This score serves as the response variable in a Random Forest model that evaluates the influence of firm-specific financial indicators and macroeconomic variables, including exchange rates, producer price inflation, and commercial loan interest rates. In addition, a forward-looking classification model estimates the probability that firms will transition into a fragile regime within the subsequent six months. The proposed framework achieves an AUC of 0.706 and demonstrates stable predictive performance under alternative regime definitions, penalty parameters, cost functions, and cross-validation strategies. Explainability analysis derived from Shapley Additive Explanations (SHAP) data shows book-to-market ratio and sensitivities to inflation and interest rate changes as the most important components of enduring resilience. The proposed methodology provides an interpretable mathematical framework for regime detection, nonlinear time-series modeling, and decision support in sustainable financial systems, offering practical value for risk assessment and resilience-oriented portfolio management.