Referral Infomediaries and Retail Competition

An important phenomenon on the Internet has been the emergence of "infomediaries" or Internet referral services such as Autobytel.com and Carpoint.com in the automobile industry, Avviva.com in real estate and Healthcareadvocates.com in medicine. These services offer consumers the opportunity to get price quotes from enrolled brick-and-mortar retailers as also information on invoice prices, reviews and specifications before they commence the shopping process. Internet referral services also direct consumer traffic to particular retailers who join them. The view of industry analysts and practitioners is that these services are a boon to consumers who can use them to get better prices from retailers. What is less clear though is the manner in which these infomediaries affect the market competition between retailers. In this paper, we analyze the impact of referral infomediaries on the functioning of retail markets and the contractual arrangements that they should use in selling their services. We identify the market conditions under which the business model represented by these services would be viable and also provide an understanding of how this institution would evolve with the growth of the Internet. The model that we develop captures the key economic characteristics that define an Internet referral infomediary. On the consumer side, a referral infomediary performs the function of "price discovery": a consumer can use the service to costlessly get an additional retail price quote before purchase. On the firm side, a referral service endows an enrolled retailer with the ability to price discriminate between consumers who come through the service and those who come directly to the store. Specifically the model consists of a referral infomediary and a market with two downstream retailers who compete in price. The retail market is comprised of three consumer segments: a segment loyal to each retailer and a comparison shopping segment that shops on the basis of the lowest price. The referral infomediary reaches some proportion of the total consumer population and this characterizes the reach of the Internet in this market. The impact of the infomediary on the market is best illustrated by the case in which one of the retailers is enrolled in the institution. We show that the referral price will always be lower than the retail store price offered by an enrolled dealer. The incentives of the retailer while setting the on-line referral price are driven not only by the comparison shoppers who search at both stores, but also the consumers who would have searched only at the competing store. Thus the use of a referral service as a price discrimination mechanism leads to lower online prices. Next, the profits of the enrolled dealer first increase and then decrease with the reach of the institution. One might find this surprising because the referral service provides the enrolled retailer the benefit of price discrimination as well as the benefit of additional demand (because the retailer gets the opportunity to quote a price to all online customers, some of whom were not previously accessible). However, the referral service also creates a competitive effect because it helps an enrolled retailer to poach on its competitor's customers who were previously unavailable. The strategic response by the competitor is to price aggressively in order to protect its loyal base and this intensifies price competition leading to lower equilibrium profits. This competitive effect increases with the reach of the infomediary. As a result, the profits of the enrolled retailer first increases and then decreases with the reach of the referral infomediary. We also show that the referral infomediary should prefer an exclusive strategy of allowing only one of the two retailers to enroll. A non-exclusive strategy implies that consumers who use the service will get referral prices from both retailers leading to Bertrand type competition for these consumers. Interestingly, we find that the referral service can unravel (in the sense that neither retailer can get any net profit from joining) when its reach becomes too large. In this case, any retailer that joins can poach upon a large proportion of its competitor's customers leading to intense price competition. Consequently, the joining firm will make less profits than if it had not joined. This provides a rationale for the current attempts by firms such as Autobytel to diversify aggressively into additional service areas. We extend the model to the case where the referral infomediary can identify the different consumer segments and show that consumer identification can prevent the infomediary from unraveling when the reach of the institution increases. Finally, we extend the model to the cases in which retailer loyalty is asymmetric and in which the reach of the Internet can vary across the different segments.Referral Services, Infomediaries, Internet, Price Discrimination, Retail Competition. ,

DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures

Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models in the medical imaging domain. Furthermore, obtaining labeled medical data is difficult and expensive since it requires expert annotators like doctors, radiologists, etc. Active learning (AL) is a well-known method to mitigate labeling costs by selecting the most diverse or uncertain samples. However, current AL methods do not work well in the medical imaging domain with OOD data. We propose Diagnose (avoiDing out-of-dIstribution dAta usinG submodular iNfOrmation meaSurEs), a novel active learning framework that can jointly model similarity and dissimilarity, which is crucial in mining in-distribution data and avoiding OOD data at the same time. Particularly, we use a small number of data points as exemplars that represent a query set of in-distribution data points and a private set of OOD data points. We illustrate the generalizability of our framework by evaluating it on a wide variety of real-world OOD scenarios. Our experiments verify the superiority of Diagnose over the state-of-the-art AL methods across multiple domains of medical imaging.Comment: Accepted to MICCAI 2022 MILLanD Worksho

Effect of raster angle on mechanical properties of 3D printed short carbon fiber reinforced acrylonitrile butadiene styrene

The most common additive manufacturing technique fused filament fabrication (FFF) suffers from inter-bead porosity that reduces mechanical properties. Inter-bead pores follow the raster angle, which causes anisotropic mechanical properties. Yet, the effects of raster angle on the mechanical behavior of short-carbon-fiber-reinforced (SCFR) thermoplastics are unclear. In this study, we performed tensile, flexural, and fracture toughness tests on SCFR acrylonitrile butadiene styrene (ABS). Raster angles of 0°, 15°, 30°, 45°, 60°, 75°, and 90° were investigated. Tensile strength and elastic modulus decreased by 22–35% for a change from 0° to 15°. Flexural strength and modulus were less sensitive to raster angle. Flexural strengths were at least 50% more than tensile strength for the same raster angle. Whereas flexural modulus is at least 15% less than elastic modulus. Fracture toughness showed a non-linear relationship with the raster angle. Maximum fracture toughness was observed at 0° and 60° rasters. Crack deflection was observed as the toughening mechanism

Broker-mediated Multiple-Cloud Orchestration Mechanisms for Cloud Computing

Ph.DDOCTOR OF PHILOSOPH

CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification

Training deep learning models on medical datasets that perform well for all classes is a challenging task. It is often the case that a suboptimal performance is obtained on some classes due to the natural class imbalance issue that comes with medical data. An effective way to tackle this problem is by using targeted active learning, where we iteratively add data points to the training data that belong to the rare classes. However, existing active learning methods are ineffective in targeting rare classes in medical datasets. In this work, we propose Clinical (targeted aCtive Learning for ImbalaNced medICal imAge cLassification) a framework that uses submodular mutual information functions as acquisition functions to mine critical data points from rare classes. We apply our framework to a wide-array of medical imaging datasets on a variety of real-world class imbalance scenarios - namely, binary imbalance and long-tail imbalance. We show that Clinical outperforms the state-of-the-art active learning methods by acquiring a diverse set of data points that belong to the rare classes.Comment: Accepted to MICCAI 2022 MILLanD Worksho