Dangerous work: Terrorism against U.S. diplomats

The goal of diplomats is to represent their countries’ interests through diplomacy, not arms. Because they are not military personnel, they may be perceived as at lower risk of being the target of terrorists. However, recent events have called this perception into question. Despite this danger, there has been little research on terrorist attacks against diplomats. Drawing on the terrorism studies literature, this article argues that diplomats are targeted more than non-diplomatic targets in countries where certain U.S. foreign policies are implemented. An empirical analysis of 471 attacks against U.S. diplomats from 1970 to 2011 reveals that while U.S. alliances and foreign aid increase the likelihood of attacks against diplomats, U.S. military intervention and civil war, on the other hand, increase the risk of terrorism against non-diplomatic targets. This finding is relevant because it shows terrorist attacks against diplomats result from certain types of foreign policy.     

Life in the “Gray Zone”: observations for contemporary strategists

The term “Gray Zone” is gaining in popularity as a way of describing contemporary security challenges. This article describes the “short-of-war” strategies – the fait accompli, proxy warfare, and the exploitation of ambiguous deterrence situations, i.e. “salami tactics” – that are captured by the term and offers several explanations for why state and non-state actors are drawn to these strategies. The analysis highlights why defense postures based on deterrence are especially vulnerable to the short-of-war strategies that populate the “Gray Zone.” The article concludes by suggesting how defense officials might adapt defense policies to life in the “Gray Zone.”     

Cyclic best first search: Using contours to guide branch‐and‐bound algorithms

The cyclic best‐first search (CBFS) strategy is a recent search strategy that has been successfully applied to branch‐and‐bound algorithms in a number of different settings. CBFS is a modification of best‐first search (BFS) that places search tree subproblems into contours which are collections of subproblems grouped in some way, and repeatedly cycles through all non‐empty contours, selecting one subproblem to explore from each. In this article, the theoretical properties of CBFS are analyzed for the first time. CBFS is proved to be a generalization of all other search strategies by using a contour definition that explores the same sequence of subproblems as any other search strategy. Further, a bound is proved between the number of subproblems explored by BFS and the number of children generated by CBFS, given a fixed branching strategy and set of pruning rules. Finally, a discussion of heuristic contour‐labeling functions is provided, and proof‐of‐concept computational results for mixed‐integer programming problems from the MIPLIB 2010 database are shown. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 64–82, 2017     

Exact algorithms and bounds for the dynamic assignment interdiction problem

We study a multi‐stage dynamic assignment interdiction (DAI) game in which two agents, a user and an attacker, compete in the underlying bipartite assignment graph. The user wishes to assign a set of tasks at the minimum cost, and the attacker seeks to interdict a subset of arcs to maximize the user's objective. The user assigns exactly one task per stage, and the assignment costs and interdiction impacts vary across stages. Before any stage commences in the game, the attacker can interdict arcs subject to a cardinality constraint. An interdicted arc can still be used by the user, but at an increased assignment cost. The goal is to find an optimal sequence of assignments, coupled with the attacker's optimal interdiction strategy. We prove that this problem is strongly NP‐hard, even when the attacker can interdict only one arc. We propose an exact exponential‐state dynamic‐programming algorithm for this problem as well as lower and upper bounds on the optimal objective function value. Our bounds are based on classical interdiction and robust optimization models, and on variations of the DAI game. We examine the efficiency of our algorithms and the quality of our bounds on a set of randomly generated instances. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 373–387, 2017     

All Counterinsurgency is Local: Counterinsurgency and Rebel Legitimacy

Abstract

Although the concept of legitimacy is central to Western counterinsurgency theory, most discourse in this area black-boxes the concept. It hence remains under-specified in many discussions of counterinsurgency. Fortunately, recent research on rebel governance and legitimacy contributes to our understanding of the problems faced by counterinsurgents who want to boost state legitimacy while undermining that of the rebels. Taken together, this research illustrates that a rational choice approach to legitimacy is simplistic; that micro-level factors ultimately drive legitimacy dynamics; and that both cooption of existing legitimate local elites and their replacement from the top–down is unlikely to succeed. Western counterinsurgency doctrine has failed to grasp the difficulties this poses for it.     

Pareto-optimality in classical inventory problems

In this paper the inventory problem with backorders both deterministic and stochastic is studied using trade-off analysis in the context of vector optimization theory. The set of Pareto-optimal solutions is geometrically characterized in both the constrained and unconstrained cases. Moreover, a new way of utilizing Pareto-optimality concepts to handle classical inventory problems with backorders is derived. A new analysis of these models is done by means of a trade-off analysis. New solutions are shown, and an error bound for total inventory cost is provided. Other models such as multi-item or stochastic lead-time demand inventory problems are addressed and their Pareto-optimal solution sets are obtained. An example is included showing the additional applicability of this kind of analysis to handle parametric problems. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 83–98, 1998     

Solving a selected class of location problems by exploiting problem structure: A decomposition approach

For many combinatorial optimization problems that are NP-hard, a number of special cases exist that can be solved in polynomial time. This paper addresses the issue of solving one such problem, the well-known m-median problem with mutual communication (MMMC), by exploiting polynomially solvable special cases of the problem. For MMMC, a dependency graph is defined that characterizes the structure of the interactions between decision variables. A Lagrangian decomposition scheme is proposed that partitions the problem into two or more subproblems, each having the same structure as the original problem, but with simpler dependency graphs. The dual problems are solved using subgradient or multiplier adjustment methods. An efficient method of adjusting the multiplier values is given. Computational results are reported that show the method to be quite effective. In addition, applications of the approach to other difficult location problems is discussed. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 791–815, 1998     

CHOOSING GENOCIDE: ECONOMIC PERSPECTIVES ON THE DISTURBING RATIONALITY OF RACE MURDER

An economic theory of genocide is presented with application to Rwanda‐1994. The theory considers ‘macro’ conditions under which an authority group chooses genocide and ‘micro’ conditions that facilitate the spread of genocide. From the macro perspective, a bargaining model highlights four rational explanations for an authority’s choice of genocide: prevention of loss of power, indivisibility, elimination of a persistent rival, and political bias. From the micro perspective, an evolutionary game model shows how supporters of genocide gain the upper hand in group dynamics over resisters and bystanders. The theory and application suggest that the conditions for genocide are not exceptional.     

ARMY RE‐ENLISTMENT DURING OIF/OEF: BONUSES,DEPLOYMENT, AND STOP‐LOSS

In 2004, stretched by wartime deployments, the US Army countered declining retention by increasing re‐enlistment bonuses and implementing stop‐loss to prevent soldiers from separating at the end of their enlistment. We estimate the effects of bonuses, deployment, and stop‐loss on re‐enlistment between FY 2002 and 2006. We estimate that the baseline propensity to re‐enlist fell by 20%. However, we find that deployed soldiers are more likely to re‐enlist and that the estimated effects of re‐enlistment bonuses are similar to those estimated in peacetime. We evaluate the reasons for our findings, and calculate the cost effectiveness of re‐enlistment bonuses.