The Persistent Military Security Dilemma between China and India

This paper evaluates to what extent the improving Sino-Indian relations coincide with a mitigation of military threat perceptions. A critical review of the demilitarisation of the border, the military strategies with respect to the Indian Ocean and nuclear arms programmes, reveals that the two countries are still locked in a military security dilemma. Distrust still results in military balancing. The outcome is a complex and multi-level military balance of power that might not bring about peace but enhances stability.     

Rubber match: a third Lebanon war

Israel and Hezbollah have applied the lessons learned from the 2006 Lebanon War and have been preparing for a future conflict with both sides aiming for a more decisive victory. With improved capabilities from 2006, future hostilities will be far more destructive. This article analyzes their performance in 2006 and the lessons they have drawn to predict what a future conflict is likely to look like. The article analyzes both parties in three categories: ground forces; rockets, missiles, and anti-armor weapons; and lastly, Israel's air and sea dominance and Hezbollah's efforts to counter that dominance. Finally, this article argues that, contrary to conventional wisdom, a large-scale conflict between Israel and Hezbollah is unlikely to become a larger regional conflict involving Syria and Iran.     

Reassessing private military and security company (PMSC) ‘competition‘ in civil war: lessons from Sierra Leone

ABSTRACT

This paper challenges recent claims that competitive market dynamics incentivize Private Military and Security Companies (PMSCs) to fully commit to providing effective services, thereby reducing the duration of civil war. Our assessment of a most-likely case scenario for this argument – Sierra Leone – reveals four critical problems. First, there is rarely direct competition, even if numerous companies are present. Second, the presence of multiple PMSCs usually represents a collaboration among subsidiaries providing distinct services, often under the same corporate umbrella. Third, data aggregation obfuscates the overlap of PMSC presence, inflating the amount of perceived competition. Finally, we raise concerns regarding how quantitative analyses can conflate conflict intensity with conflict termination.     

Trading coups for civil war

This paper investigates civil conflict as a product of the survival strategies of African leaders. Specifically, the paper offers a theory of risk substitution that predicts coup-fearing leaders will undermine the military effectiveness of the state when making an effort to extend their own tenure. While ‘coup-proofing’ practices have often been noted as contributors to political survival, considerably less attention has been paid to the influence of these strategies on other forms of conflict. Utilising data from a number of cross-national datasets, the analyses show that having a higher number of ‘coup-proofing’ counterweights significantly worsens a state's civil conflict prospects. A brief consideration of multiple episodes of conflict further suggests that in addition to coup-proofing undermining the counterinsurgency capacity of the state, some leaders are simply indifferent to – or can even potentially benefit from – the existence of an insurgency.     

Computing equilibria via nonconvex programming

The problem of determining a vector that places a system in a state of equilibrium is studied with the aid of mathematical programming. The approach derives from the logical equivalence between the general equilibrium problem and the complementarity problem, the latter being explicitly concerned with finding a point in the set S = {x: < x, g(x)> = 0, g(x) ≦ 0, x ≧ 0}. An associated nonconvex program, min{? < x, g(x) > : g(x) ≦ 0, x ≧ 0}, is proposed whose solution set coincides with S. When the excess demand function g(x) meets certain separability conditions, equilibrium solutions are obtained by using an established branch and bound algorithm. Because the best upper bound is known at the outset, an independent check for convergence can be made at each iteration of the algorithm, thereby greatly increasing its efficiency. A number of examples drawn from economic and network theory are presented in order to demonstrate the computational aspects of the approach. The results appear promising for a wide range of problem sizes and types, with solutions occurring in a relatively small number of iterations.     

Appointment scheduling at a multidisciplinary outpatient clinic using stochastic programming

The purpose of this paper is to investigate the problem of constructing an appointment template for scheduling patients at a specific type of multidisciplinary outpatient clinic called an integrated practice unit (IPU). The focus is on developing and solving a stochastic optimization model for a back pain IPU in the face of random arrivals, an uncertain patient mix, and variable service times. The deterministic version of the problem is modeled as a mixed integer program with the objective of minimizing a weighted combination of clinic closing time (duration) and total patient waiting time (length of stay). A two‐stage stochastic program is then derived to account for the randomness and the sequential nature of the decisions. Although it was not possible to solve the two‐stage problem for even a limited number of scenarios, the wait‐and‐see (WS) problem was sufficiently tractable to provide a lower bound on the stochastic solution. The introduction of valid inequalities, limiting indices, and the use of special ordered sets helped to speed up the computations. A greedy heuristic was also developed to obtain solutions much more quickly. Out of practical considerations, it was necessary to develop appointment templates with time slots at fixed intervals, which are not available from the WS solution. The first to be derived was the expected value (EV) template that is used to find the expected value of the EV solution (EEV). This solution provides an upper bound on the objective function value of the two‐stage stochastic program. The average gap between the EEV and WS solutions was 18%. Results from extensive computational testing are presented for the EV template and for our adaptation of three other templates found in the literature. Depending on the relative importance of the two objective function metrics, the results demonstrate the trade‐off that exists between them. For the templates investigated, the “closing time” ranged from an average of 235 to 275 minutes for a 300‐minute session, while the corresponding “total patient time in clinic” ranged from 80 to 71 minutes.