The Agency Model and MFN Clauses JOHNSON, JUSTIN P.
The Review of economic studies,
07/2017, Volume:
84, Issue:
3 (300)
Journal Article
Peer reviewed
I provide an analysis of vertical relations in markets with imperfect competition at both layers of the supply chain and where exchange is intermediated either with wholesale prices or ...revenue-sharing contracts. Revenue-sharing is extremely attractive to firms that are able to set the revenue shares but often makes the firms that set retail prices worse off. This is so whether revenue-sharing lowers or raises industry profits. These results are strengthened when a market moves from "the wholesale model" of sales to "the agency model" of sales, which results in retailers setting revenue shares and suppliers setting retail prices. I also show that retail price-parity restrictions raise industry prices. These results provide a potential explanation for why many online retailers have adopted the agency model and retail price-parity clauses.
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high ...class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, highly imbalanced data poses added difficulty, as most learners will exhibit bias towards the majority class, and in extreme cases, may ignore the minority class altogether. Class imbalance has been studied thoroughly over the last two decades using traditional machine learning models, i.e. non-deep learning. Despite recent advances in deep learning, along with its increasing popularity, very little empirical work in the area of deep learning with class imbalance exists. Having achieved record-breaking performance results in several complex domains, investigating the use of deep neural networks for problems containing high levels of class imbalance is of great interest. Available studies regarding class imbalance and deep learning are surveyed in order to better understand the efficacy of deep learning when applied to class imbalanced data. This survey discusses the implementation details and experimental results for each study, and offers additional insight into their strengths and weaknesses. Several areas of focus include: data complexity, architectures tested, performance interpretation, ease of use, big data application, and generalization to other domains. We have found that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered. Several traditional methods for class imbalance, e.g. data sampling and cost-sensitive learning, prove to be applicable in deep learning, while more advanced methods that exploit neural network feature learning abilities show promising results. The survey concludes with a discussion that highlights various gaps in deep learning from class imbalanced data for the purpose of guiding future research.
The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. Recent methods have explored unsupervised ...pretraining to scale to vast quantities of unlabeled images. In contrast, we aim to learn high-quality visual representations from fewer images. To this end we revisit supervised pretraining, and seek data-efficient alternatives to classification-based pretraining. We propose VirTex - a pretraining approach using semantically dense captions to learn visual representations. We train convolutional networks from scratch on COCO Captions, and transfer them to downstream recognition tasks including image classification, object detection, and instance segmentation. On all tasks, VirTex yields features that match or exceed those learned on ImageNet - supervised or unsupervised - despite using up to ten times fewer images.
I analyze a model of dynamic competition between retail platforms in the presence of consumer lock-in. Two different revenue models are considered, one in which platforms set final retail prices and ...one in which the suppliers set final retail prices. Platforms have long-term (or strategic) pricing incentives but suppliers do not, which implies that the inter-temporal price path faced by consumers depends on the revenue model in place. When suppliers set prices instead of platforms, prices may be higher in early periods but lower in later periods, suggesting that appropriate antitrust enforcement ought to consider more than initial price changes when an industry shifts to the agency model. Indeed, consumers may (but need not) prefer the agency model even when prices increase in initial periods. A potential downside of the agency model is that it may align the incentives of suppliers and platforms and thereby encourage platforms to lower the competitiveness of the supplier market, harming consumers; no such incentives exist under the wholesale model. I relate my results to events in the market for electronic books.
I examine how the increasing ability of firms to target their ads influences market outcomes when consumers have access to advertising-avoidance tools. Although firms generally benefit from improved ...targeting, consumers need not. I also show that there may be too little blocking of ads in equilibrium and consider the role of targeted advertising when niche firms compete against mass-market firms.
We investigate the ability of a platform to design its marketplace to promote competition, improve consumer surplus, and increase its own payoff. We consider demand‐steering rules that reward firms ...that cut prices with additional exposure to consumers. We examine the impact of these rules both in theory and by using simulations with artificial intelligence pricing algorithms (specifically Q‐learning algorithms, which are commonly used in computer science). Our theoretical results indicate that these policies (which require little information to implement) can have strongly beneficial effects, even when sellers are infinitely patient and seek to collude. Similarly, our simulations suggest that platform design can benefit consumers and the platform, but that achieving these gains may require policies that condition on past behavior and treat sellers in a nonneutral fashion. These more sophisticated policies disrupt the ability of algorithms to rotate demand and split industry profits, leading to low prices.
The marriage of colloidal semiconductor nanocrystals and functional organic molecules has brought unique opportunities in emerging photonic and optoelectronic applications. Traditional semiconductor ...nanocrystals have been widely demonstrated to initiate efficient triplet energy transfer at the nanocrystal–acene interface. Herein, we report that unlike conventional semiconductor nanocrystals, lead halide perovskite nanocrystals promote an efficient Dexter-like singlet energy transfer to surface-anchored pentacene molecules rather than triplet energy transfer. Subsequently, molecular pentacene triplets are efficiently generated via singlet fission on the nanocrystal surface. Our demonstrated strategy not only unveils the obscure energy dynamics between perovskite nanocrystal and acenes, but also brings important perspectives of utilizing singlet fission throughout the solar spectrum.
We introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language. The dense captioning task generalizes ...object detection when the descriptions consist of a single word, and Image Captioning when one predicted region covers the full image. To address the localization and description task jointly we propose a Fully Convolutional Localization Network (FCLN) architecture that processes an image with a single, efficient forward pass, requires no external regions proposals, and can be trained end-to-end with a single round of optimization. The architecture is composed of a Convolutional Network, a novel dense localization layer, and Recurrent Neural Network language model that generates the label sequences. We evaluate our network on the Visual Genome dataset, which comprises 94,000 images and 4,100,000 region-grounded captions. We observe both speed and accuracy improvements over baselines based on current state of the art approaches in both generation and retrieval settings.
I propose a framework in which asymmetric multiproduct retailers compete for one-stop shoppers who have biased beliefs about their future purchase probabilities (and so make unplanned purchases). One ...firm carries a full portfolio of products while the other carries an incomplete but endogenous one. Using this framework, I examine the phenomenon of loss leading, the optimal product portfolio of the smaller firm, and the effects of banning loss leading. Among other results, I show that there is a nonpredatory (and possibly procompetitive) justification for the observation that such larger firms may charge below cost on the core product lines of their smaller rivals.
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