Improving Foreign Militaries -- The Effects of U.S. Military Aid in the Form of International Military Education and Training Programs

Great powers have often sought to achieve their strategic goals through the allocation of military aid. The United States is no exception, as it has frequently used military aid to influence the policies and military capacity of its allies and partners. However, our understanding of the effects of US military aid on the conflict behavior of recipient states - and especially the mechanisms underlying these effects - remains poorly understood. The results of previous studies of U.S. military aid are often contradictory, and are mostly based on over-aggregated, country-level data. In this dissertation, I argue that examining the individual-level effects will give us a better understanding of the mechanisms underlying country-level associations between US military aid and recipient behavior. I examine three research questions related to the manner in which military aid influences conflict in recipient countries. First, I explore the individual effects of U.S. IMET using semi-structures in-depth interviews and an original survey of Hungarian military officers and non-commissioned officers. This paper investigates the transmission of professional values and democratic norms to individual participants through the U.S. IMET programs. Second, I investigate the effects of U.S. IMET participation on civil conflict duration. I argue that government forces with more robust U.S. IMET participation will accumulate more and better military human capital, which incentivize rebels to hide and minimize their operations leading to a prolonged civil conflict. Finally, while exploring recipient states international conflict behavior I theorize that American educated and trained foreign military personnel return home with a better understanding about the role of the military as an instrument of national power, civil-military relations, the value of cooperation and the cost of war. I argue that these military personnel advise their political masters against the use of military force during international disputes leading to a decreased probability of MID initiation. I find support for each of the main arguments presented in the dissertation. Overall, this dissertation represents one of the first attempts to move beyond country-level data and explore the micro-foundations of US military assistance

Multi-stage stochastic optimization and reinforcement learning for forestry epidemic and covid-19 control planning

This dissertation focuses on developing new modeling and solution approaches based on multi-stage stochastic programming and reinforcement learning for tackling biological invasions in forests and human populations. Emerald Ash Borer (EAB) is the nemesis of ash trees. This research introduces a multi-stage stochastic mixed-integer programming model to assist forest agencies in managing emerald ash borer insects throughout the U.S. and maximize the public benets of preserving healthy ash trees. This work is then extended to present the first risk-averse multi-stage stochastic mixed-integer program in the invasive species management literature to account for extreme events. Significant computational achievements are obtained using a scenario dominance decomposition and cutting plane algorithm.The results of this work provide crucial insights and decision strategies for optimal resource allocation among surveillance, treatment, and removal of ash trees, leading to a better and healthier environment for future generations. This dissertation also addresses the computational difficulty of solving one of the most difficult classes of combinatorial optimization problems, the Multi-Dimensional Knapsack Problem (MKP). A novel 2-Dimensional (2D) deep reinforcement learning (DRL) framework is developed to represent and solve combinatorial optimization problems focusing on MKP. The DRL framework trains different agents for making sequential decisions and finding the optimal solution while still satisfying the resource constraints of the problem. To our knowledge, this is the first DRL model of its kind where a 2D environment is formulated, and an element of the DRL solution matrix represents an item of the MKP. Our DRL framework shows that it can solve medium-sized and large-sized instances at least 45 and 10 times faster in CPU solution time, respectively, with a maximum solution gap of 0.28% compared to the solution performance of CPLEX. Applying this methodology, yet another recent epidemic problem is tackled, that of COVID-19. This research investigates a reinforcement learning approach tailored with an agent-based simulation model to simulate the disease growth and optimize decision-making during an epidemic. This framework is validated using the COVID-19 data from the Center for Disease Control and Prevention (CDC). Research results provide important insights into government response to COVID-19 and vaccination strategies