This article analyses the pricing policy equilibria emerging in a duopoly when one firmmay choose whether to engage in behaviour-based price discrimination or uniform pricing while the rival price discriminates. The question we address is: should a firm price discriminate when facing a price discriminating rival? Our main conclusion is that, if the consumers are sufficiently myopic, it is bette...

BACKGROUND To further explore characteristics of myopia and changes in factors associated with myopia among students at Inner Mongolia Medical University. METHODS Two cross-sectional censuses were conducted in 2011 and 2013. Participants were medical students residing on campus in 2011 and 2013. Logistic regression analysis was performed to ascertain associations with basic information, genet...

PURPOSE To compare the clinical outcome of AT-45 implantation between high myopic eyes and non-high myopic eyes. METHODS Retrospective, non-randomized, comparative trial. The medical charts of 28 patients with 35 eyes who had phacoemulsification and AT-45 implantation were retrospectively reviewed. 13 eyes of 10 patients were included in the high myopic group (axial length > or = 26.0 mm) and...

This paper considers the issue of optimal subcarrier allocation in OFDMA. We show, via a counter example, that water-filling based subcarrier allocation policies, contrary to conventional wisdom, fail to provide rate-stability for an otherwise stabilizable OFDMA system. Water-filling is too myopic when considering long-time average performance, e.g. delay, queue lengths, and even long-run throu...

Knowing each customer’s preferences enhances the ability to design and market the right offer in terms of content and price. The more a retailer interacts with its customers, the more it learns as it observes revealed preferences. Lower prices typically lead to more interactions and more learning but lower short-term profits. This paper brings the concept of learning functions to this dynamic p...

We consider a combination of state space partitioning and random search methods for solving deterministic global optimization problem. We assume that function computations are costly and nding global optimum is diicult. Therefore, we may decide to stop searching long before we found a solution close to the optimum. Final reward of the algorithm is deened as the best found function value minus t...

This paper addresses the non-preemptive single machine scheduling problem to minimize total tardiness. We are interested in the online version of this problem, where orders arrive at the system at random times. Jobs have to be scheduled without knowledge of what jobs will come afterwards. The processing times and the due dates become known when the order is placed. The order release date occurs...

Running experiments on physical robotic systems typically requires significant resources (personnel, time, and money), restricting the number that can be run during training. Additionally, these systems typically operate over a range of external conditions and can be highly non-linear. We focus on the problem of learning a globally optimal policy to adapt controllers for such systems based on t...

We consider the periodic review inventory control problem in which the purchasing cost of the product changes, in a Markovian fashion, from one period to the next. After establishing (with and without the non-speculation assumption) that an order upto policy is optimal, we develop an efficient recursive solution procedure to compute the optimal levels. In addition, we propose a measure for the ...