Th e Arab Spring has advanced the prospects for democracy in the region. After years during which any democratic transition seemed implausible in the Arab World, masses across the region have risen to challenge the political status quo, inspired by the successful revolution in Tunisia. A major cause to the political unrest can be identifi ed in the large number of unemployed youth in Arab nations, whose political frustrations were aggravated by their inability to express themselves in a tightly controlled police state, political corruption, and the incapability of the state to deal with social and economic problems. In addition, social media was a vital vehicle in both sustaining reform movements within single countries, and spreading the wave of demonstrations across the region. Yet, the events of the Arab Spring have challenged the stability of countries undergoing these transitions. Th e possibility for the creation of failed states or international interventions, and the necessity of governments to deal with large numbers of refugees, sectarian tensions, and deeply rooted economic problems threaten to derail the recent political transformations. In spite of these challenges, however, the recent political changes do provide encouraging opportunities for creating peace in the region and moderating Islamic parties.
In this paper, we consider the problem of selecting the top [Formula: see text] systems when the number of alternative systems is very large. We propose a sequential procedure that consists of two stages to solve this problem. The procedure is a combination of the ordinal optimization (OO) technique and optimal computing budget allocation (OCBA) method. In the first stage, the OO is used to select a subset that overlaps with the set of actual best [Formula: see text] systems with high probability. Then in the second stage the optimal computing budget is used to select the top [Formula: see text] systems from the selected subset. The proposed procedure is tested on two numerical examples. The numerical tests show that the proposed procedure is able to select a subset of best systems with high probability and short simulation time.
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