Where Empires Meet: Orientalism and Marginality at the Former Russo-British Frontier

This article explores the intersection of orientalism and marginality in two regions at the former Russo-British frontier between Central and South Asia. Focussing on Tajikistan’s Gorno-Badakhshan and Gilgit-Baltistan in today’s Pakistan, an analysis of historical and contemporary orientalist projections on and in the two border regions reveals changing modes of domination through the course of the twentieth century (British, Kashmiri, Pakistani and Russian, Soviet, Tajik). In this regard, different local experiences of “ colonial ” rule, both in Gorno-Badakhshan and Gilgit-Baltistan, challenge “ classical ” periodisations of colonial/postcolonial and colonial/socialist/postsocialist. This article furthermore maintains that processes of marginalisation in both regions can be interpreted as effects of imperial and Cold War contexts that have led to the establishment of the frontier. Thus, a central argument is that neither the status of the frontier between Central and South Asia as a stable entity, nor the periodisations that have conventionally been ascribed to the two regions as linear timelines can be taken for granted.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

SOP: Parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centers from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.

Economies of Scale and Cost Efficiencies: A Panel-Data Stochastic-Frontier Analysis of Real Estate Investment Trusts

This paper extends the existing research on real estate investment trust (REIT) operating efficiencies. We estimate a stochastic-frontier panel-data model specifying a translog cost function, covering 1995 to 2003. The results disagree with previous research in that we find little evidence of scale economies and some evidence of scale diseconomies. Moreover, we also generally find smaller inefficiencies than those shown by other REIT studies. Contrary to previous research, the results also show that self-management of a REIT associates with more inefficiency when we measure output with assets. When we use revenue to measure output, selfmanagement associates with less inefficiency. Also contrary with previous research, higher leverage associates with more efficiency. The results further suggest that inefficiency increases over time in three of our four specifications.

A Dynamic Stochastic Frontier Production Model with Time-Varying Efficiency

In this paper we introduce technical efficiency via the intercept that evolve over time as a AR(1) process in a stochastic frontier (SF) framework in a panel data framework. Following are the distinguishing features of the model. First, the model is dynamic in nature. Second, it can separate technical inefficiency from fixed firm-specific effects which are not part of inefficiency. Third, the model allows one to estimate technical change separate from change in technical efficiency. We propose the ML method to estimate the parameters of the model. Finally, we derive expressions to calculate/predict technical inefficiency (efficiency).

Economic integration, location of industries, and frontier regions : evidence from Cambodia

We examine changes in the location of economic activity in Cambodia between 1998 and 2008 in terms of employment growth. During this period, Cambodia joined ASEAN and increased trade with neighboring countries. Drawing on the predictions of the new economic geography, we focus on frontier regions such as border regions and international port cities. We examine the changing state of manufacturing in Cambodia from its initial concentration in Greater Phnom Penh to its growth in the frontier regions. The results suggest that economic integration and concomitant trade linkages may lead to the industrial development of frontier regions as well as the metropolitan areas in Cambodia.