Market Transparency, Adverse Selection, and Moral Hazard

We study the effects of improvements in market transparency on eBay on seller exit and continuing sellers’ behavior. An improvement in market transparency by reducing strategic bias in buyer ratings led to a significant increase in buyer valuation especially of sellers rated poorly prior to the change, but not to an increase in seller exit. When sellers had the choice between exiting—a reduction in adverse selection—and improved behavior—a reduction in moral hazard—, they preferred the latter because of lower cost. Increasing market transparency improves on market outcomes

The Actual Structure of eBay’s Feedback Mechanism and Early Evidence on the Effects of Recent Changes

eBay’s feedback mechanism is considered crucial to establishing and maintaining trust on the world’s largest trading platform. The effects of a user’s reputation on the probability of sale and on prices are at the center of a large number of studies. More recent theoretical work considers aspects of the mechanism itself. Yet, there is confusion amongst users about its exact institutional details, which also changed substantially in the last few months. An understanding of these details, and how the mechanism is perceived by users, is crucial for any assessment of the system. We provide a thorough description of the institutional setup of eBay’s feedback mechanism, including recent changes to it. Most importantly, buyers now have the possibility to leave additional, anonymous ratings on sellers on four different criteria. We discuss the implications of these changes and provide first descriptive evidence on their impact on rating behavior

Last Minute Feedback

Feedback mechanisms that allow partners to rate each other after a transaction are considered crucial for the success of anonymous internet trading platforms. We document an asymmetry in the feedback behavior on eBay, propose an explanation based on the micro structure of the feedback mechanism and the time when feedbacks are given, and support this explanation by findings from a large data set. Our analysis implies that the informational content of feedback records is likely to be low. The reason for this is that agents appear to leave feedbacks strategically. Negative feedbacks are given late, in the "last minute," or not given at all, most likely because of the fear of retaliative negative feedback. Conversely, positive feedbacks are given early in order to encourage reciprocation. Towards refining our insights into the observed pattern, we look separately at buyers and sellers, and relate the magnitude of the effects to the trading partners' experience

The Actual Structure of eBay’s Feedback Mechanism and Early Evidence on the Effects of Recent Changes

eBay’s feedback mechanism is considered crucial to establishing and maintaining trust on the world’s largest trading platform. The effects of a user’s reputation on the probability of sale and on prices are at the center of a large number of studies. More recent theoretical work considers aspects of the mechanism itself. Yet, there is confusion amongst users about its exact institutional details, which also changed substantially in the last few months. An understanding of these details, and how the mechanism is perceived by users, is crucial for any assessment of the system. We provide a thorough description of the institutional setup of eBay’s feedback mechanism, including recent changes to it. Most importantly, buyers now have the possibility to leave additional, anonymous ratings on sellers on four different criteria. We discuss the implications of these changes and provide first descriptive evidence on their impact on rating behavior.eBay; reputation mechanism; strategic feedback behavior; informational content; reciprocity; fear of retaliation

Last Minute Feedback

Feedback mechanisms that allow partners to rate each other after a transaction are considered crucial for the success of anonymous internet trading platforms. We document an asymmetry in the feedback behavior on eBay, propose an explanation based on the micro structure of the feedback mechanism and the time when feedbacks are given, and support this explanation by findings from a large data set. Our analysis implies that the informational content of feedback records is likely to be low. The reason for this is that agents appear to leave feedbacks strategically. Negative feedbacks are given late, in the "last minute," or not given at all, most likely because of the fear of retaliative negative feedback. Conversely, positive feedbacks are given early in order to encourage reciprocation. Towards refining our insights into the observed pattern, we look separately at buyers and sellers, and relate the magnitude of the effects to the trading partners' experience.eBay; reputation mechanism; strategic feedback behavior; informational content; reciprocity; fear of retaliation

Relationship between herd size and measures of animal welfare on dairy cattle farms with freestall housing in Germany

The objective of this study was to examine the association of herd size with animal welfare in dairy cattle herds. Therefore, 80 conventional dairy cattle farms were classified by the number of cows into 4 herd size classes, C1 (100 cows), C2 (100-299 cows), C3 (300-499 cows), and C4 (≥500 cows), and assessed using multiple animal-based measures of the Welfare Quality Assessment protocol for dairy cattle. Data were recorded from April 2014 to September 2016 by an experienced single assessor in northern Germany. Each farm was visited 2 times at an interval of 6 mo (summer period and winter period) to avoid seasonal effects on the outcome. The average herd size was 383 ± 356 Holstein-Friesian cows (range 45 to 1,629). Only farms with freestall (cubicle) housing and a maximum of 6 h access to pasture per day were included in the study. Data were statistically analyzed using a generalized linear mixed model. None of the farms reached the highest overall rating of "excellent." The majority of the farms were classified as "enhanced" (30%) or "acceptable" (66%), and at 6 assessments the farms were rated as "not classified" (4%). Regarding single indicators, mean trough length per cow, percentage of cows with nasal discharge, and vulvar discharge increased with increasing herd size, whereas it was vice versa for displacements of cows. Percentage of lean cows, percentage of dirty lower legs, and duration of the process of lying down showed a curvilinear relationship with the number of cows per farm. Herd size was not associated with any other measures of the Welfare Quality protocol. In conclusion, herd size effects were small, and consequently herd size cannot be considered as a feasible indicator of the on-farm animal welfare level. Housing conditions and management practices seem to have a greater effect on animal welfare than the number of dairy cows per farm

Software components and formal methods from a computational viewpoint

Software components and the methodology of component-based development offer a promising approach to master the design complexity of huge software products because they separate the concerns of software architecture from individual component behavior and allow for reusability of components. In combination with formal methods, the specification of a formal component model of the later software product or system allows for establishing and verifying important system properties in an automatic and convenient way, which positively contributes to the overall correctness of the system. Here, we study such a combined approach. As similar approaches, we also face the so-called state space explosion problem which makes property verification computationally hard. In order to cope with this problem, we derive techniques that are guaranteed to work in polynomial time in the size of the specification of the system under analysis, i.e., we put an emphasis on the computational viewpoint of verification. As a consequence, we consider interesting subclasses of component-based systems that are amenable to such analysis. We are particularly interested in ideas that exploit the compositionality of the component model and refrain from understanding a system as a monolithic block. The assumptions that accompany the set of systems that are verifiable with our techniques can be interpreted as general design rules that forbid to build systems at will in order to gain efficient verification techniques. The compositional nature of software components thereby offers development strategies that lead to systems that are correct by construction. Moreover, this nature also facilitates compositional reduction techniques that allow to reduce a given model to the core that is relevant for verification. We consider properties specified in Computation Tree Logic and put an emphasis on the property of deadlock-freedom. We use the framework of interaction systems as the formal component model, but our results carry over to other formal models for component-based development. We include several examples and evaluate some ideas with respect to experiments with a prototype implementation

Adverse selection and moral hazard in anonymous markets

We study the effects of improvements in eBay’s rating mechanism on seller exit and continuing sellers’ behavior. Following a large sample of sellers over time, we exploit the fact that the rating mechanism was changed to reduce strategic bias in buyer rating. That improvement did not lead to increased exit of poorly rated sellers. Yet, buyer valuation of the staying sellers—especially the poorly rated ones—improved significantly. By our preferred interpretation, the latter effect results from increased seller effort; also, when sellers have the choice between exiting (a reduction in adverse selection) and improved behavior (a reduction in moral hazard), then they prefer the latter because of lower cost

Easy Consensus Algorithms for the Crash-Recovery Model

In the crash-recovery failure model of asynchronous distributed systems, processes can temporarily stop to execute steps and later restart their computation from a predefined local state. The crash-recovery model is much more realistic than the crash-stop failure model in which processes merely are allowed to stop executing steps. The additional complexity is reflected in the multitude of assumptions and the technical complexity of algorithms which have been developed for that model. We focus on the problem of consensus in the crash-recovery model, but instead of developing completely new algorithms from scratch, our approach aims at reusing existing crash-stop consensus algorithms in a modular way using the abstraction of failure detectors. As a result, we present three new and relatively simple consensus algorithms for the crash-recovery model for different types of assumptions