We propose an approach for uncovering characteristic response paths of a population from an individual-level multivariate time series data set. The approach is based on a model that accommodates a set of arbitrary distributions for endogenous variables and interstep intervals and variables. The model enables reliable estimation of individual-level parameters by uncovering and statistically pooling clusters of similar individuals. We show that using such a model one can distribute the response of an outcome variable to an impulse over all possible preceding activity sequences. When a few such sequences explain most of the response, they describe the population’s characteristic response paths from the impulse to the outcome. We apply the proposed approach to a customer touchpoint data set from a large multichannel specialty retailer. This application uncovers six customer segments, each with unique characteristic paths to purchase. These paths provide insights into the behavior of customers and the optimal over-time communication strategy for different customer segments. Summary of Contribution: Uncovering users’ paths through physical and virtual spaces has been of considerable interest in the computing and operations research domain. The existing research suggests a demand for visualizing the primary paths of agents through geographic, online, and activity spaces. Thus far, most of the research have developed approaches that are unique to specific domains providing insight into the domain in the process. There is a need for a general statistically robust approach that can be applied to a broad range of domains to uncover variable sequences that lead to outcomes of interest. We propose a computational approach to uncover characteristic response paths of a population from an individual-level multivariate time series data set. The approach is based on a statistical model that accommodates arbitrary and mixed set of distributions for the endogenous variables, accommodates intersession intervals and variables, and reliably estimates individuals’ parameters through statistical pooling by uncovering clusters of similar members. These features make the proposed model suitable for a large variety of real-world datasets. We show that using such a model one can extract characteristic paths over possible activity sequences starting from an impulse leading up to a target variable of interest.

In this paper, we study the question and answer (Q&A) feature of electronic commerce platforms, an increasingly common form of user-generated content (UGC) that allows consumers to publicly ask product-specific questions and receive responses, either from the platform or from other customers. Using data from a major online retailer, we show that Q&As complement reviews and ratings: unlike reviews, Q&As primarily happen pre-purchase, focus on clarification of product attributes (rather than discussion of quality), and convey fit-specific information in a sentiment-free way. Our main hypothesis is that Q&As mitigate product fit uncertainty, leading to better matches between products and consumers, and therefore improved product ratings. We show that when low-rated products start receiving Q&As, their subsequent ratings improve by approximately 0.5 stars. We further show that the extent of the rating increase due to Q&As is moderated by the degree of ex-ante fit uncertainty. Overall, our findings suggest that, by resolving product fit uncertainty in an e-commerce setting, the addition of Q&As can be a viable way for retailers to improve ratings and sales of low-rated products, particularly those products that have incurred low ratings due to customer-product fit mismatch.

Few industries have suffered more severe disruption by digital technologies than news and journalism. Traditional content creators, such as newspapers, are witnessing their geographical monopolies dissolving into the globally competitive Internet and some of their most important sources of revenue, such as classified ads, migrating to specialized online marketplaces like eBay and Craigslist. User-generated content, such as blogs and online reviews, has increased the supply of content that often competes head-on for readers' attention with professionally produced content.

This paper proposes a novel mechanism for inducing cooperation in online auction settings with noisy monitoring of quality and adverse selection. The mechanism combines the ability of electronic markets to solicit feedback from buyers with the more traditional ability to levy listing fees from sellers. Each period the mechanism charges a listing fee contingent on a seller's announced expected quality. It subsequently pays the seller a reward contingent on both his announced quality and the rating posted for that seller by that period's winning bidder. I show that, in the presence of a continuum of seller types with different cost functions, imperfect private monitoring of a seller's effort level and a simple "binary" feedback mechanism that asks buyers to rate a transaction as "good" or "bad", it is possible to derive a schedule of fees and rewards that induces all seller types to produce at their respective first-best quality levels and to truthfully announce their intended quality levels to buyers. The mechanism maximizes average social welfare for the entire community and is robust to a number of contingencies of particular concern in online environments, such as easy name changes and the existence of inept sellers. On the other hand, the mechanism distorts the resulting payoffs of individual sellers relative to the complete information case, transferring part of the payoffs of more efficient sellers to less efficient sellers. The magnitude of this distortion is proportional to the amount of noise associated with observing and reporting the quality of a good.

This paper introduces a model for analyzing marketplaces, such as eBay, which rely on binary reputation mechanisms for quality signaling and quality control. In our model sellers keep their actual quality private and choose what quality to advertise. The reputation mechanism is primarily used to determine whether sellers advertise truthfully. Buyers may exercise some leniency when rating sellers, which needs to be compensated by corresponding strictness when judging sellers'feedback profiles. It is shown that, the more lenient buyers are when rating sellers, the more likely it is that sellers will find it optimal to settle down to steady-state quality levels, as opposed to oscillating between good quality and bad quality. Furthermore, the fairness of the market outcome is determined by the relationship between rating leniency and strictness when assessing a seller's feedback profile. If buyers judge sellers too strictly (relative to how leniently they rate) then, at steady state, sellers will be forced to understate their true quality. On the other hand, if buyers judge too leniently then sellers can get away with consistently overstating their true quality. An optimal judgment rule, which results in outcomes where at steady state buyers accurately estimate the true quality of sellers, is analytically derived. However, it is argued that this optimal rule depends on several system parameters, which are difficult to estimate from the information that marketplaces, such as eBay, currently make available to their members. It is therefore questionable to what extent unsophisticated buyers are capable of deriving and applying it correctly in actual settings.

Reputation reporting systems have emerged as an important risk management mechanism in online trading communities.However, the predictive value of these systems can be compromised in situations where conspiring buyers intentionally give unfair ratings to sellers or, where sellers discriminate on the quality of service they provide to different buyers.This paper proposes and evaluates a set of mechanisms, which eliminate, or significantly reduce the negative effects of such fraudulent behavior.The proposed mechanisms can be easily integrated into existing online reputation systems in order to safeguard their reliability in the presence of potentially deceitful buyers and sellers.

Software agent marketplaces require the development of new architectures, which are capable of coping with unreliable computational and network infrastructures, limited trust among independently developed agents and the possibility of systemic failures.In analogy with human societies, agent marketplaces will benefit from the introduction of appropriate electronic exception handling institutions, whose role will be to help guarantee efficiency and fairness in the face of these challenges.This paper presents a research methodology for designing and evaluating such electronic institutions.It also describes how the methodology has been applied in order to design and evaluate an exception handling architecture for robust software agent marketplaces based on the contract net protocol.

In this paper we describe a new algorithm for maintaining a balanced search tree on a message-passing MIMD architecture; the algorithm is particularly well suited for implementation on a small number of processors. We introduce a (2/sup B-2/, 2/sup B/) search tree that uses a bidirectional ring of O(log n) processors to store n entries. Update operations use a bottom-up node-splitting scheme, which performs significantly better than top-down search tree algorithms. The bottom-up algorithm requires many fewer messages and results in less blocking due to synchronization than top-down algorithms. Additionally, for a given cost ratio of computation to communication the value of B may be varied to maximize performance. Implementations on a parallel-architecture simulator are described.

PROTEUS is a high-performance simulator for MIMD multiprocessors. It is fast, accurate, and flexible: it is one to two orders of magnitude faster than comparable simulators, it can reproduce results from real multiprocessors, and it is easily configured to simulate a wide range of architectures. PROTEUS provides a modular structure that simplifies customization and independent replacement of parts of architecture. There are typically multiple implementations of each module that provide different combinations of accuracy and performance; users pay for accuracy only when and where they need it. Finally, PROTEUS provides repeatability, nonintrusive monitoring and debugging, and integrated graphical output, which result in a development environment superior to those available on real multiprocessors.