Push or Pull? Design of Content Delivery Systems

Advances in information technology, especially the expansion of cellular and WiFi networks, are dramatically changing how people consume digital content. These changes in user access behavior present a challenge of delivering content to a diversified consumer base. This article addresses this challenge by identifying the key factors for the design of content delivery systems (gross value of content, delivery delay, sensitivity to delivery delay, accessing cost, and processing cost) and explicitly modeling their interactions. We investigate two content delivery systems—push and pull systems, and solve for the content provider's optimal push frequency decision and consumers’ push versus pull decisions. We show that the content provider's selection of push frequency plays a critical role determining the segmentation of the consumer market into the push group and the pull group. Our findings suggest that firms should set a relatively high push frequency to cater to high‐type consumers, which leads to two consumer groups with low‐type consumers belonging to the pull group and high‐type consumers belonging to the push group.     

Assessing Observer Effects on the Fidelity of Implementation of Functional Analysis Procedures

Instructing and training others to implement functional analysis (FA) procedures can be a cumbersome and time-consuming task. Students and practitioners are required to learn all of the various components to establish conditions and analyze results while also learning to conduct the sessions. The current study assessed the fidelity of individuals implementing FA conditions after observing and rating the fidelity of videos models, using a multiple-baseline design across FA conditions. Video models of each condition were provided throughout; however, participants were only asked to provide fidelity ratings for one video. Results demonstrated the intervention was successful in increasing participant performance above baseline levels for 16 of 17 participants, with 7 participants requiring an additional intervention in which they were asked to observe their own performance. Further research should evaluate the degree to which this procedure may effectively prepare individuals working with non-confederate children and carry over to other responses and settings.     

Replenishment Strategies for Micro‐Retailers in Developing Countries

When formal distribution channels are absent in developing countries, micro‐retailers travel a long distance to replenish their stocks directly from suppliers. This “informal” replenishment strategy is inefficient due to high imputed travel costs involved in the replenishment process. To improve efficiency, one “hybrid” replenishment strategy has emerged under which one of the micro‐retailers in a neighborhood, while continuing its own retail business, also operates as a wholesaler to serve other micro‐retailers in the neighborhood. A major obstacle for the transition from the informal strategy to the hybrid strategy is that the micro‐retailers are reluctant to source from a wholesaler who also competes with them in the retail market. Thus, it is not clear when the micro‐retailers adopt the hybrid strategy instead of the informal strategy. Meanwhile, the micro‐retailers may prefer the “formal” strategy: a traditional replenishment strategy under which one of the micro‐retailers relinquishes its retail business and operates purely as a wholesaler. We examine a situation when competing micro‐retailers contemplate with the three potential aforementioned replenishment strategies. Our equilibrium analysis of the two‐store model reveals that the dominant strategy is: (a) the hybrid strategy when the travel cost is high; (b) the formal strategy when the travel cost is medium; and (c) the informal strategy when the travel cost is low. This key insight is shown continue to hold when we extend the two‐store model to incorporate other issues including: quantity discounts from the supplier, variable operating costs, price competition, local monopolies, and different decision sequences. One additional finding is that the transition from the informal strategy to the hybrid strategy could benefit consumers if the micro‐retailers receive quantity discounts from the supplier. Furthermore, when there are more than two stores, we show that the formal strategy is never dominant. Moreover, when the number of stores increases, the hybrid strategy becomes more preferable to the informal strategy.     

Improving robustness of next-hop routing

A weakness of next-hop routing is that following a link or router failure there may be no routes between some source-destination pairs, or packets may get stuck in a routing loop as the protocol operates to establish new routes. In this article, we address these weaknesses by describing mechanisms to choose alternate next hops. Our first contribution is to model the scenario as the following tree augmentation problem. Consider a mixed graph where some edges are directed and some undirected. The directed edges form a spanning tree pointing towards the common destination node. Each directed edge represents the unique next hop in the routing protocol. Our goal is to direct the undirected edges so that the resulting graph remains acyclic and the number of nodes with outdegree two or more is maximized. These nodes represent those with alternative next hops in their routing paths. We show that tree augmentation is NP-hard in general and present a simple \(\frac{1}{2}\)-approximation algorithm. We also study 3 special cases. We give exact polynomial-time algorithms for when the input spanning tree consists of exactly 2 directed paths or when the input graph has bounded treewidth. For planar graphs, we present a polynomial-time approximation scheme when the input tree is a breadth-first search tree. To the best of our knowledge, tree augmentation has not been previously studied.     

Strengthening the Systemic Ties that Bind: Integrating Common Factors into Marriage and Family Therapy Curricula

Specific models guide the training of marriage and family therapists (MFTs) as they offer both structure and organization for both therapists and clients. Learning models may also benefit therapists‐in‐training by instilling confidence and preventing atheoretical eclecticism. The moderate common factors perspective argues that models are essential, but should not be taught as “the absolute truth,” given there is no evidence for relative efficacy of one empirically validated model versus another, and no single model works in all instances. The following article provides a blueprint for infusing a common factors perspective into MFT programmes by reviewing innovations in course design, outlining specific teaching strategies, and highlighting potential implementation challenges.     

Is Who Delivers the Treatment More Important than the Treatment Itself? The Role of The Therapist in Common Factors

In this article, we discuss the role of the therapist in change in couple and family therapy. We argue that the therapist is a key change ingredient in most successful therapy. We situate our discussion in the common factors debate and show how both broad and narrow common factor views involve the therapist as a central force. We review the research findings on the role of the therapist, highlight the strengths and weaknesses of this literature, and provide directions for future research. We then use this review as a foundation for our recommendations for theory integration, training, and practice.     

Nonparametric Bayes Stochastically Ordered Latent Class Models

Latent class models (LCMs) are used increasingly for addressing a broad variety of problems, including sparse modeling of multivariate and longitudinal data, model-based clustering, and flexible inferences on predictor effects. Typical frequentist LCMs require estimation of a single finite number of classes, which does not increase with the sample size, and have a well-known sensitivity to parametric assumptions on the distributions within a class. Bayesian nonparametric methods have been developed to allow an infinite number of classes in the general population, with the number represented in a sample increasing with sample size. In this article, we propose a new nonparametric Bayes model that allows predictors to flexibly impact the allocation to latent classes, while limiting sensitivity to parametric assumptions by allowing class-specific distributions to be unknown subject to a stochastic ordering constraint. An efficient MCMC algorithm is developed for posterior computation. The methods are validated using simulation studies and applied to the problem of ranking medical procedures in terms of the distribution of patient morbidity.