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.     

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     

The “motivated bias” dilemma in warfare and intelligence

The goal of this article is to challenge the assumption of rationality in the behavior of decision-making units involved in security, defense, intelligence and warfare and to consider the influence of “motivated bias” in such instances. A review of motivational literature within international politics and a discussion of literature applying “motivated biases” to warfare and strategic surprise will offer an alternative view of the primacy of rationality in such decisions.     

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.     

Covariance of weibull order statistics

Let X₁ < X₂ <… < X_n denote an ordered sample of size n from a Weibull population with cdf F(x) = 1 - exp (?x^p), x > 0. Formulae for computing Cov (X_i, X_j) are well known, but they are difficult to use in practice. A simple approximation to Cov(X_i, X_j) is presented here, and its accuracy is discussed.     

Computerized scheduling of seagoing tankers

Computerized Scheduling of Seagoing Tankers The tanker scheduling problem considered in this paper is that of the Defense Fuel Supply Center (DFSC) and the Military Sealift Command (MSC) in the worldwide distribution of bulk petroleum products. Routes and cargoes which meet delivery schedule dates for a multiplicity of product requirements at minimum cost are to be determined for a fleet of tankers. A general mathematical programming model is presented, and then a mixed integer model is developed which attempts to reflect the true scheduling task of DFSC and MSC as closely as possible. The problem is kept to within a workable size by the systematic construction of a set of tanker routes which does not contain many possible routes that can be judged unacceptable from practical considerations alone.     

Generalized programming by linear approximation of the dual gradient: Equality constraints

A modified generalized programming procedure is presented for solving concave programming problems with equality constraints. The procedure constructs convenient linear approximations of the gradient of the dual and finds points where the approximating functions vanish. In the quadratic programming case, the procedure is finitely convergent. Global convergence is established for the non-quadratic case. Illustrative numerical examples are included.