Violence and the state: Lessons from Colombia

Whether or not Colombia has improved is a controversial topic. If improvement has occurred, what, if any, lessons from the Colombian model can be learned? The first lesson is that Colombia's problems were caused more by a weak state than by drugs. The second lesson is that improving state capacity requires more than just increased security. The state must also provide the basic social services that citizens require to gain and maintain their support. Additionally, government institutions need to improve their professionalism, protect human rights, and root out impunity and corruption. The alternative is to suffer from new cycles of violence as old foes are vanquished, but new ones emerge.     

Limiting survival functions of self‐similar structures

We show the existence of a unique analytic single parameter limiting survival function arising from the repeated composition of a coherent structure as the number of components tends to infinity. Examples include the repeated composition process of the bridge structure. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Higher‐order Markov chain models for categorical data sequences*

In this paper we study higher‐order Markov chain models for analyzing categorical data sequences. We propose an efficient estimation method for the model parameters. Data sequences such as DNA and sales demand are used to illustrate the predicting power of our proposed models. In particular, we apply the developed higher‐order Markov chain model to the server logs data. The objective here is to model the users' behavior in accessing information and to predict their behavior in the future. Our tests are based on a realistic web log and our model shows an improvement in prediction. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

A facility reliability problem: Formulation,properties, and algorithm

Having a robustly designed supply chain network is one of the most effective ways to hedge against network disruptions because contingency plans in the event of a disruption are often significantly limited. In this article, we study the facility reliability problem: how to design a reliable supply chain network in the presence of random facility disruptions with the option of hardening selected facilities. We consider a facility location problem incorporating two types of facilities, one that is unreliable and another that is reliable (which is not subject to disruption, but is more expensive). We formulate this as a mixed integer programming model and develop a Lagrangian Relaxation‐based solution algorithm. We derive structural properties of the problem and show that for some values of the disruption probability, the problem reduces to the classical uncapacitated fixed charge location problem. In addition, we show that the proposed solution algorithm is not only capable of solving large‐scale problems, but is also computationally effective. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Coercive diplomacy and the Donbas: Explaining Russian strategy in Eastern Ukraine

What were Russia’s objectives in Eastern Ukraine, and why was it seemingly unable to achieve a successful or decisive outcome? In contrast to Russia’s seizure of Crimea, the uprising in Eastern Ukraine was marked by disorganization and chaos. Using proxy and surrogate actors, along with military exercises and the injection of Russian troops, Russia sought to institutionalize a political entity inside Ukraine to influence its domestic politics. In this article, I analyze the mechanisms by which Russia attempted to implement, and later salvage, its strategy. The article contributes to clearer theoretical and practical understanding of limited force in coercive diplomacy, signaling, and a more rigorous treatment of the role and uses of proxy actors.     

On the optimality of the bayes prediction policy in two-echelon multistation inventory models

The present paper provides a proof that the Bayes prediction ordering policy developed in [3] is an optimal policy, in the sense that it minimizes the total expected discounted cost of ordering for any finite planning horizon.