Design Of Reputation Systems Research Articles

With whom should I work? Ratings consideration for partner selection in a P2P supply chain network

Partner selection is a challenge central to dynamic peer-to-peer (P2P) supply chain networks with decentralized architecture. In this study, we examine how ratings information can be used to select the right trading partners in a P2P network. Using data collected from a bitcoin platform, we first show that the behavior patterns of fraudsters differ from those of other users. We analyze the topology of a P2P network and demonstrate that it is a small-world network and that various internal and external factors can affect the ratings. We find that it is also temporal and that effective rating indices for partner selection are also time-varying. We design a system to simulate the partner selection process and access its impacts on the effectiveness of the ratings information under different information revelation policies—all past information, three-month information, and one-month information—along with different levels of tolerance for deception. Our results provide practical insights into reputation system design in a P2P platform and have implications for partner selection in a P2P supply chain network.

Information about average evaluations spurs cooperation: An experiment on noisy reputation systems

Abstract Reputation systems are an important part of many markets and online platforms. One central design choice that market designers face pertains to how much feedback to collect, and how to aggregate it. To explore this matter, we vary the design of reputation systems in the lab to evaluate how aggregation choices affect (i) evaluation behavior, (ii) expectations and (iii) rates of cooperation. We find that aggregation matters, even when disaggregated information is also available. Results show that with average evaluations, incentives for reputation building are stronger, evaluations are “more extreme”, expectations are more optimistic, and contributions are about 50% higher.

Tit for Tat? The Difficulty of Designing Two-Sided Reputation Systems

Abstract In a two-sided reputation system, it is in the interest of both buyers and sellers to be a good transaction partner. What sounds wonderful in theory is unfortunately not so easy to implement in reality. Reputation systems can have flaws due to factors such as reciprocity and retaliation, selective reviewing, and reputation inflation. These flaws cause the ratings collected on the platform to diverge from the actual experiences that marketplace participants are having. When reputation systems are not thoughtfully designed, it can be hard to distinguish between the “high quality” and “low quality” interactions. This makes it difficult to identify and remove bad actors and increases the chances of a “bad match”. Innovations in reputation system design, such as simultaneous reveal of information, review incentives, and greater reliance on private feedback, are making it easier to implement two-sided systems while avoiding the common pitfalls.

Design of Platform Reputation Systems: Optimal Information Disclosure

Reputation systems play a central role in a variety of marketplaces to eliminate information asymmetry between sellers and buyers. Despite their crucial role, the design of reputation systems remains a complicated issue. Some platforms try to disclose as much product information as possible through their reputation systems, by encouraging consumers to leave reviews or ratings. In contrast, some platforms disclose only partial product information to the public. Platforms, as the designers of such reputation systems, are thus an additional player added to conventional seller-buyer games. This paper studies the optimal information disclosure in a reputation system from the platform's profit standpoint. In a three-player game, sellers with different base quality decide investment in quality, the platform decides the amount of information about the product quality (i.e., ratings) to disclose through its reputation system, and the future consumer as the receiver makes the purchase decision after observing the information about the product quality. By modeling consumers as Bayesian learners, we specify conditions under which withholding some private information of the sellers can serve the platform's goal of maximizing profits by incentivizing seller investment in quality. We find that, although fully disclosing information can eliminate information asymmetry, it may hinder the sellers' incentive to invest in quality, which hurts the platform's profits. Two main effects are in favor of withholding private information: the quality improving effect, which makes sellers more willing to invest in quality, and the sales increasing effect, which leads to more transactions on the platform.

Closed-Loop Feedback Computation Model of Dynamical Reputation Based on the Local Trust Evaluation in Business-to-Consumer E-Commerce

Trust and reputation are important factors that influence the success of both traditional transactions in physical social networks and modern e-commerce in virtual Internet environments. It is difficult to define the concept of trust and quantify it because trust has both subjective and objective characteristics at the same time. A well-reported issue with reputation management system in business-to-consumer (BtoC) e-commerce is the “all good reputation” problem. In order to deal with the confusion, a new computational model of reputation is proposed in this paper. The ratings of each customer are set as basic trust score events. In addition, the time series of massive ratings are aggregated to formulate the sellers’ local temporal trust scores by Beta distribution. A logical model of trust and reputation is established based on the analysis of the dynamical relationship between trust and reputation. As for single goods with repeat transactions, an iterative mathematical model of trust and reputation is established with a closed-loop feedback mechanism. Numerical experiments on repeated transactions recorded over a period of 24 months are performed. The experimental results show that the proposed method plays guiding roles for both theoretical research into trust and reputation and the practical design of reputation systems in BtoC e-commerce.

Strategic Behavior in Online Reputation Systems: Evidence from Revoking on eBay

This study examines how sellers respond to changes in the design of reputation systems on eBay. Specifically, we focus on one particular strategic behavior on eBay’s reputation system: sellers’ explicit retaliation against negative feedback provided by

Strategic Behavior in Online Reputation Systems: Evidence from eBay

This study examines how sellers respond to changes in the design of reputation systems on eBay. Specifically, we focus on one particular strategic behavior on eBay’s reputation system – the sellers’ explicit retaliation and subsequent revoking of negative feedback provided by buyers. We examine how these strategic sellers respond to the removal of their ability to retaliate against buyers and to revoke negative feedback. We utilize the biggest policy change of eBay’s reputation system in its history, which provides a natural experimental setting that allows us to infer the causal impact of the reputation system on seller behavior. Our results show that revoking enables low quality sellers to manipulate their reputations and masquerade as high-quality sellers. We find that these sellers react strongly to eBay’s announcement of a proposed ban on revoking. Interestingly, after the power of these revokers is curtailed, we find evidence that they exert more effort to improve their reputation scores. This study provides valuable insights about the relationship between reputation system and seller behavior, which have important implications for the design of online reputation mechanisms.

Do different reputation systems provide consistent signals of seller quality: a canonical correlation investigation of Chinese C2C marketplaces

In recent years, consumer-to-consumer (C2C) marketplaces such as eBay and Taobao have adopted a component rating system, and run it simultaneously with but independent of a binary rating system. This paper investigates the extent to which binary rating and component rating systems are able to provide consistent signals of sellers’ quality, focusing on the reputation system design under the Chinese context. Using field data from Taobao, we performed canonical correlation analyses and found that the reputation signals of the two systems are generally correlated. As expected, negative and neutral ratings accurately reveal buyer dissatisfaction. Our results, however, show that positive ratings exhibit negative correlations with the three component ratings (i.e., item-as-described, customer service, and on-time delivery), suggesting that large numbers of positive ratings on Taobao may encourage trust in the platform but do not help to choose credible sellers. Our results elucidate the role of cultural difference in explaining the negative relationship in China and provide important implications for the design of reputation systems.

REPUTATION MANAGEMENT SYSTEM FOR PEER-TO-PEER COMMUNITIES

Abstract Peer-to-Peer (P2P) reputation systems are essential to evaluate the trustworthiness of participating peers and to combat the selfish, dishonest, and malicious peer behaviors. The system collects locally-generated peer feedbacks and aggregates them to yield the global reputation scores. Surprisingly, most previous work ignored the distribution of peer feedbacks. We use a trust overlay network (TON) to model the trust relationships among peers. After examining the eBay transaction trace of over 10,000 users, we discovered a power-law distribution in user feedbacks. Our mathematical analysis justifies that power-law distribution is applicable to any dynamicall yg rowing P2P systems, either structured or unstructured. We develop a robust and scalable P2P reputation system, PowerTrust, to leverage the power-law feedback characteristics. The PowerTrust system dynamically selects small number of power nodes that are most reputable using a distributed ranking mechanism. By using a look ahead random walk strategy and leveraging the power nodes, the PowerTrust significantly improves in global reputation accuracy and aggregation speed. PowerTrust is adaptable to dynamics in peer joining and leaving and robust to disturbance by malicious peers. Through P2P network simulation experiments, we find significant performance gains in using PowerTrust. This power-law guided reputation system design proves to achieve high query success rate in P2P file-sharing applications. The system also reduces the total job make span and failure rate in large-scale, parameter-sweeping P2P Grid applications. A peer to peer network exploits diverse connectivity between participants in a network and the cumulative bandwidth of network participants rather than conventional centralized resources where a relatively low number of servers provide the core value to a service or application. A pure peer to peer network doesn't have the notion of clients or servers, but only equal peer nodes that simultaneously function as both clients and servers to the other nodes on the network. Peers act as equals, merging the roles of clients and server. There is no central server managing the network. There is no central router.

Money Talks: Rebate Mechanisms in Reputation System Design

Reputation systems that rely on voluntary feedback from traders are important in creating and sustaining trust in markets. Feedback nevertheless is a public good, and providing it is often costly. We combine theory with a laboratory experiment to study the effect of a seller precommitment mechanism: Sellers have an option to commit by providing a rebate to reduce the buyer's feedback reporting cost before making purchasing decisions. Our theory predicts that this mechanism induces noncooperative sellers to cooperate in the listed-price market. Using a buyer–seller trust game with a unilateral feedback scheme, we find that the seller's rebate decision has a significant impact on the buyer's purchasing decision via signaling the seller's cooperative type. More importantly, market efficiency under the precommitment mechanism increases with the probability that sellers will provide a rebate. Compared with the no rebate mechanism market, more efficient trades can be achieved when the sellers offer a rebate to the buyers in the market with the rebate mechanism, even when the rebate does not cover the full cost of feedback reporting. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1848 . This paper was accepted by Teck-Hua Ho, behavioral economics.

Money Talks? An Experimental Study of Rebate in Reputation System Design

Reputation systems that rely on voluntary feedback from traders are important in creating and sustaining trust in markets. Feedback nevertheless is a public good, and providing it is often costly. We combine theory with a laboratory experiment to study the effect of a seller precommitment mechanism: Sellers have an option to commit by providing a rebate to reduce the buyer’s feedback reporting cost before making purchasing decisions. Our theory predicts that this mechanism induces noncooperative sellers to cooperate in the listed-price market. Using a buyer-seller trust game with a unilateral feedback scheme, we find that the seller’s rebate decision has a significant impact on the buyer’s purchasing decision via signaling the seller’s cooperative type. More importantly, market efficiency under the precommitment mechanism increases with the probability that sellers will provide a rebate. Compared with the no rebate mechanism market, more efficient trades can be achieved when the sellers offer a rebate to the buyers in the market with the rebate mechanism, even when the rebate does not cover the full cost of feedback reporting.

Outline Reputation Scores: How Well are They Understood?

This paper presents a study comparing existing reputation systems to determine if the different reputation system designs are equally capable of eliciting meaningful feedback from users and if the information from these systems is equally useful for evaluating whether or not to purchase from a given seller. A survey of online consumers and data from both the eBay and the Amazon reputation systems are used to determine the impact of reputation system design on overall system effectiveness. Results of this research indicate that the five-star response system used by Amazon may be more useful to users attempting to determine which sellers to buy from.

Reputation Systems for Self-Organized Networks

Self-organized networks such as mobile ad-hoc, Internet-based peer-to-peer, wireless mesh and Fourth generation (4G) wireless networks depend on cooperation of nodes. Reputation systems help nodes decide with whom to cooperate and which nodes to avoid. They have been studied and applied almost separately in diverse disciplines such as economics, computer science, and social science, resulting in effort duplication and inconsistent terminology. We aim to bring together these efforts by outlining features and fundamental questions common to reputation systems in general. We derive methodologies to address these questions for both reputation system design and research from our own experiences and evaluations by simulation and analytical modeling. We argue for using deviation tests, discounting, passing on only first-hand information, introducing secondary response, and stressing the importance of identity.

PowerTrust: A Robust and Scalable Reputation System for Trusted Peer-to-Peer Computing

Peer-to-Peer (P2P) reputation systems are essential to evaluate the trustworthiness of participating peers and to combat the selfish, dishonest, and malicious peer behaviors. The system collects locally-generated peer feedbacks and aggregates them to yield the global reputation scores. Surprisingly, most previous work ignored the distribution of peer feedbacks. We use a trust overlay network (TON) to model the trust relationships among peers. After examining the eBay transaction trace of over 10,000 users, we discover a power-law distribution in user feedbacks. Our mathematical analysis justifies that power-law distribution is applicable to any dynamically growing P2P systems, either structured or unstructured. We develop a robust and scalable P2P reputation system, PowerTrust, to leverage the power-law feedback characteristics. The PowerTrust system dynamically selects small number of power nodes that are most reputable using a distributed ranking mechanism. By using a look-ahead random walk strategy and leveraging the power nodes, PowerTrust significantly improves in global reputation accuracy and aggregation speed. PowerTrust is adaptable to dynamics in peer joining and leaving and robust to disturbance by malicious peers. Through P2P network simulation experiments, we find significant performance gains in using PowerTrust. This power-law guided reputation system design proves to achieve high query success rate in P2P file-sharing applications. The system also reduces the total job makespan and failure rate in large-scale, parameter-sweeping P2P Grid applications.

Fuzzy techniques for trust and reputation management in anonymous peer‐to‐peer systems

AbstractPeer‐to‐peer (P2P) applications are rapidly gaining acceptance among users of Internet‐based services, especially because of their capability of exchanging resources while preserving the anonymity of both requesters and providers. However, concerns have been raised about the possibility that malicious users can exploit the network to spread tampered‐with resources (e.g., malicious programs and viruses). A considerable amount of research has thus focused on the development of trust and reputation models in P2P networks. In this article, we propose to use fuzzy techniques in the design of reputation systems based on collecting and aggregating peers' opinions. Fuzzy techniques are used in the evaluation and synthesis of all the opinions expressed by peers. The behavior of the proposed system is described by comparison with probabilistic approaches.

Evaluation and design of online cooperative feedback mechanisms for reputation management

This research evaluates and analyzes existing mechanisms of online reputation systems based on cooperative feedbacks of past transaction information. We find existing popular feedback systems do not provide sustained incentives for sellers to behave honestly over time. We propose a new design of reputation system based on exponential smoothing. This mechanism is shown to be more robust compared to the existing systems. We relax the assumption of a static product value and have implemented a two-level exponential smoothing policy to process and aggregate reputation information. The simulation results show that the policy can serve as a sustained incentive mechanism for the seller.