Local perceptions of the EU’s role in peacebuilding: The case of security sector reform in Palestine

ABSTRACT

Perception research can make a valuable contribution to the study of the local dimension in EU peacebuilding. The conceptual framework developed in this article distinguishes between perceptions of the “legitimacy,” “effectiveness,” and “credibility” of EU peacebuilding practices, which are crucial factors for successful peacebuilding. Relying on the case of the EU’s support for security sector reform (SSR) in Palestine, this article shows that local stakeholders—which participate in various EU-sponsored training and capacity-building programs—display considerable support for liberal peacebuilding norms. Yet, perceived discrepancies between the EU’s peacebuilding rhetoric and its SSR activities have severely undermined the potential of the EU’s liberal peacebuilding model in the eyes of Palestinian stakeholders. Critical local perceptions are frequently articulated with reference to the EU’s own liberal peacebuilding discourse, pointing to a lack of inclusiveness of the SSR process and deficits in terms of democratic governance and the rule of law.     

Determining adjacent vertices on assignment polytopes

To rank the solutions to the assignment problem using an extreme point method, it is necessary to be able to find all extreme points which are adjacent to a given extreme solution. Recent work has shown a procedure for determining adjacent vertices on transportation polytopes using a modification of the Chernikova Algorithm. We present here a procedure for assignment polytopes which is a simplification of the more general procedure for transportation polytopes and which also allows for implicit enumeration of adjacent vertices.     

Aggregating courier deliveries

We consider the problem of efficiently scheduling deliveries by an uncapacitated courier from a central location under online arrivals. We consider both adversary‐controlled and Poisson arrival processes. In the adversarial setting we provide a randomized (3βΔ/2δ ? 1) ‐competitive algorithm, where β is the approximation ratio of the traveling salesman problem, δ is the minimum distance between the central location and any customer, and Δ is the length of the optimal traveling salesman tour overall customer locations and the central location. We provide instances showing that this analysis is tight. We also prove a 1 + 0.271Δ/δ lower‐bound on the competitive ratio of any algorithm in this setting. In the Poisson setting, we relax our assumption of deterministic travel times by assuming that travel times are distributed with a mean equal to the excursion length. We prove that optimal policies in this setting follow a threshold structure and describe this structure. For the half‐line metric space we bound the performance of the randomized algorithm in the Poisson setting, and show through numerical experiments that the performance of the algorithm is often much better than this bound.     

The Persistent Deficit of Militia Officers in the Swiss Armed Forces: An Opportunity Cost Explanation

The Swiss Armed Forces are suffering from a structural deficit of militia officers despite good pay and a general supportive attitude in the population. Whereas, prior studies have focused on motivation to explain understaffing in armed forces, we offer an alternative approach based on opportunity cost. We model decision alternatives both within and outside a military organization, taking private sector employment as the reference point. We then monetize opportunity costs of leisure, fringe benefits, and private sector income not compensated. Our results suggest that in terms of opportunity cost, service as a militia officer is the least attractive option, an effect that we believe explains the persistent staff deficit. Implications of these findings for the literature and recruitment policy are discussed.     

Cost-based due-date assignment with the use of classical and neural-network approaches

Traditional methods of due-date assignment presented in the literature and used in practice generally assume cost-of-earliness and cost-of-tardiness functions that may bear little resemblance to true costs. For example, practitioners using ordinary least-squares (OLS) regression implicitly minimize a quadratic cost function symmetric about the due date, thereby assigning equal second-order costs to early completion and tardy behavior. In this article the consequences of such assumptions are pointed out, and a cost-based assignment scheme is suggested whereby the cost of early completion may differ in form and/or degree from the cost of tardiness. Two classical approaches (OLS regression and mathematical programming) as well as a neural-network methodology for solving this problem are developed and compared on three hypothetical shops using simulation techniques. It is found for the cases considered that: (a) implicitly ignoring cost-based assignments can be very costly; (b) simpler regression-based rules cited in the literature are very poor cost performers; (c) if the earliness and tardiness cost functions are both linear, linear programming and neural networks are the methodologies of choice; and (d) if the form of the earliness cost function differs from that of the tardiness cost function, neural networks are statistically superior performers. Finally, it is noted that neural networks can be used for a wide range of cost functions, whereas the other methodologies are significantly more restricted. © 1997 John Wiley & Sons, Inc.