31 research outputs found

    Radicalization as a reaction to failure: An economic model of Islamic extremism

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    This paper views Islamist radicals as self-interested political revolutionaries and builds on a general model of political extremism developed in a previous paper (Ferrero, 2002). Extremism is modelled as a production factor whose effect on expected revenue is initially positive and then turns negative, and whose level is optimally chosen by a revolutionary organization. The organization is bound by a free-access constraint and hence uses the degree of extremism as a means of indirectly controlling its level of membership with the aim of maximizing expected per capita income of its members, like a producer co-operative. The gist of the argument is that radicalization may be an optimal reaction to perceived failure (a widespread perception in the Muslim world) when political activists are, at the margin, relatively strongly averse to effort but not so averse to extremism. This configuration is at odds with secular, Western-style revolutionary politics but seems to capture well the essence of Islamic revolutionary politics, embedded as it is in a doctrinal framework. Copyright Springer Science + Business Media, Inc. 2005

    Coordinating multi-location production and customer delivery

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    We study two parallel machine scheduling problems with equal processing time jobs and delivery times and costs. The jobs are processed on machines which are located at different sites, and delivered to a customer by a single vehicle. The first objective considered is minimizing the sum of total weighted completion time and total vehicle delivery costs. The second objective considered is minimizing the sum of total tardiness and total vehicle delivery costs. We develop several interesting properties of an optimal scheduling and delivery policy, and show that both problems can be solved by reduction to the Shortest-Path problem in a corresponding network. The overall computational effort of both algorithms is O(n m2+m+1) (where n and m are the number of jobs and the number of machines, respectively) by the application of the Directed Acyclic Graph (DAG) method. We also discuss several special cases for which the overall computational effort can be significantly reduced
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