The Political Economy of Legislation on Terrorism

^{1 1}This research was funded in part by a grant from the Lowe Institute of Political Economy. The corresponding author is S. Brock Blomerg. We thank Chris Blomberg and Brianna Loyosa for their excellent research assistance on the project. This paper presents an empirical analysis of what drives congressional legislation on terrorism during the period 1995 to 2010. We utilize and augment current methodology to compile and analyze data on sponsorship and cosponsorship of terrorism related data. Our results on the sources of legislation on terrorism are largely in line with past examinations of the importance of committee membership and leadership, party majority-ship, and other political factors. Further, we find that the most significant and robust drive for legislation on terrorism is the September 11^th attacks. And while the impact of 9/11 affected legislative productivity everywhere, we find that it most significantly affected states surrounding New York and Washington D.C. Our results indicate that the economy may be one factor motivating politicians to legislate on terrorism; however, these results are not robust.     

France and the Gulf War of 1990–1991: Political‐military lessons learned

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The German campaign in Norway 1940 as a joint operation

The German invasion in Norway in April 1940 was unique in that it was the first major ‘triphibious’ campaign. It was an operation that was equally dependent upon naval, air and ground forces for success. Norway had long been of interest to German strategists, especially naval thinkers. However, no serious Wehrmacht plans for occupying Norway were even started until December 1939. A small group of staff officers of the three services put together a comprehensive plan in a matter of weeks. The article examines the effectiveness of the German interservice co‐operation and OKW leadership in a very tough and close run campaign. Although there were numerous problems, interservice co‐operation was fairly effective at the tactical and operational levels. Indeed, the Germans won the campaign largely because their services worked together much better than their Allied opponents.     

Scheduling jobs with random processing times on a single machine subject to stochastic breakdowns to minimize early-tardy penalties

We examine the problem of scheduling n jobs with a common due date on a single machine. The processing time of each job is a random variable, which follows an arbitrary distribution with a known mean and a known variance. The machine is not reliable; it is subject to stochastic breakdowns. The objective is to minimize the expected sum of squared deviations of job completion times from the due date. Two versions of the problem are addressed. In the first one the due date is a given constant, whereas in the second one the due date is a decision variable. In each case, a general form of the deterministic equivalent of the stochastic scheduling problem is obtained when the counting process related to the machine uptime distribution is a generalized Poisson process. A sufficient condition is derived under which optimal sequences are V-shaped with respect to mean processing times. Other characterizations of optimal solutions are also established. Based on the optimality properties, algorithms with pseudopolynomial time complexity are proposed to solve both versions of the problem. © 1996 John Wiley & Sons, Inc.     

The interactive fixed charge linear programming problem

In many decision-making situations, each activity that can be undertaken may have associated with it both a fixed and a variable cost. Recently, we have encountered serveral practical problems in which the fixed cost of undertaking an activity depends upon which other activities are also undertaken. To our knowledge, no existing optimization model can accomodate such a fixed cost structure. To do so, we have therefore developed a new model called the interactive fixed charge linear programming problem (IFCLP). In this paper we present and motivate problem (IFCLP), study some of its characteristics, and present a finite branch and bound algorithm for solving it. We also discuss the main properties of this algorithm.     

Successive approximation in separable programming: An improved procedure for convex separable programs

We implement a solution procedure for general convex separable programs where a series of relatively small piecewise linear programs are solved as opposed to a single large one, and where, based on bound calculations developed in [13] and [14], the ranges of linearization are systematically reduced for successive programs. The procedure inherits ε-convergence to the global optimum in a finite number of steps, but perhaps its most distinct feature is the rigorous way in which ranges containing an optimal solution are reduced from iteration to iteration. This paper describes the procedure, called successive approximation, discusses its convergence, tightness of the bounds, bound-calculation overhead, and its robustness. It presents a computer implementation to demonstrate its effectiveness for general problems and compares it (1) with the more standard separable programming approach and (2) with one of the recent augmented Lagrangian methods [10] included in a comprehensive study of nonlinear programming codes [12]. It seems clear from over 130 cases resulting from 80 distinct problems studied here that significant savings in terms of computational effort can be realized by a judicious use of the procedure, and the ease with which it can be used is appreciably increased by the robustness it shows. Moreover, for most of these problems, the advantage increases as the size, nonlinearity, and the degree of desired accuracy increase. Other important benefits include significantly smaller storage requirements, the ability to estimate the error in the current solution, and to terminate the algorithm as soon as the acceptable level of accuracy has been achieved. Problems requiring up to about 10,000 nonzero elements in their specification and about 45,000 nonzero elements in the generated separable programs resulting from up to 70 original nonlinear variables and 70 nonlinear constraints are included in the computations.     

Warranty design under buyer and seller risk aversion

This paper provides a framework in which warranty policies for non-repairable items can be evaluated according to risk preferences of both buyers and sellers. In particular, a warranty price schedule is established such that sellers are indifferent among the policies. Given this schedule, a buyer's response is expressed by selecting the price-warranty combination that minimizes disutility. Within this framework, a warranty can be viewed as an instrumet of risk management that can induce more sales and greater profitability. For given utility functions, analytical results for the development of a price schedule are developed. Numerical results illustrate the substitution effects between warranty terms, prices, and risk parameters.     

Financial versus human resources in the Greek‐Turkish arms race: A forecasting investigation using artificial neural networks

This paper aims at forecasting the burden on the Greek economy resulting from the arms race against Turkey and at concentrating on the leading determinants of this burden. The military debt and the defence share of GDP are employed alternatively in order to approximate the measurement of the arms race pressure on Greece, and the method used is that of artificial neural networks. The use of a wide variety of explanatory variables in combination with the promising results derived, suggest that the impact on the Greek economy resulting from this arms race is determined, to a large, extent, by demographic factors which strongly favour the Turkish side. Prediction on both miltary debt and defence expenditure exhibited highly satisfactory accuracy, while the estimation of input significance, indicates that variables describing the Turkish side are often dominant over the corresponding Greek ones.     

Deterring the Undeterrable: Coercion,Denial, and Delegitimization in Counterterrorism

This article argues that deterrence theory can be applied to counterterrorism. Doing so requires broadening the traditional concept of deterrence by punishment, expanding deterrence by denial to include defense, mitigation, and strategic hindrance, and developing deterrence by delegitimization to influence the political, ideological, and religious rationales informing terrorist behavior. In practice, deterring terrorism requires tailoring threats against state and individual facilitators, diffusing the intended consequences of terrorism, and manipulating terrorist self–restraints. When these and other deterrent leverages are applied simultaneously against various actors and processes involved in terrorism, coercion can be achieved.