Reputation Information Research Articles

A P2P-Based Market-Guided Distributed Routing Mechanism for High-Throughput Hybrid Wireless Networks

In a hybrid wireless network that combines a mobile ad-hoc network and an infrastructure network, efficient and reliable data routing is important for high throughput. Existing routing schemes that simply combine ad-hoc and infrastructure routings inherit the drawbacks of ad-hoc routing including congestion and high overhead for route discovery and maintenance. Although current reputation systems help increase routing reliability, they rely on local information exchanges between nodes to evaluate node reputations, so they are not sufficiently effective and efficient. A challenge here is if we can coordinately develop an efficient routing algorithm and effective cooperation incentives for reliable routing. To handle this challenge, this paper presents a peer-to-peer (P2P)-based Market-guided Distributed Routing mechanism (MDR). MDR takes advantage of widespread base stations to coordinately realize highly efficient data routing, and effective reputation management and trading market management for reliable data routing. The packets from a source node are distributively transmitted to base stations directly or indirectly, and then they are transmitted to the destination. The base stations form a P2P structure for reputation collection and querying to avoid local information exchanges, and for managing the service transactions between nodes in the trading market. By leveraging the single-relay transmission feature, base stations can monitor the actual transmitted packets of relay nodes to more accurately and efficiently evaluate their reputations and execute trading market management, as well as detect falsely reported reputation information. We further propose market-based policies to strengthen cooperation incentives. Simulation results show that MDR outperforms the traditional hybrid routing schemes and reputation systems in achieving high throughput.

Activity Stereotypes, or How to Cope with Disconnection during Trust Bootstrapping

Trust-based systems have been proposed as means to fight against malicious agents in peer-to-peer networks, volunteer and grid computing systems, among others. However, there still exist some issues that have been generally overlooked in the literature. One of them is the question of whether punishing disconnecting agents is effective. In this paper, we investigate this question for these initial cases where prior direct and reputational evidence is unavailable, what is referred in the literature as trust bootstrapping. First, we demonstrate that there is not a universally optimal penalty for disconnection and that the effectiveness of this punishment is markedly dependent on the uptime and downtime session lengths. Second, to minimize the effects of an improper selection of the disconnection penalty, we propose to incorporate predictions into the trust bootstrapping process. These predictions based on the current activity of the agents shorten the trust bootstrapping time when direct and reputational information is lacking.

Intermediaries in Trust: Indirect Reciprocity, Incentives, and Norms

Any trust situation involves a certain amount of risk for trustors that trustees could abuse. In some cases, intermediaries exist who play a crucial role in the exchange by providing reputational information. To examine under what conditions intermediary opinion could have a positive impact on cooperation, we designed two experiments based on a modified version of the investment game where intermediaries rated the behaviour of trustees under various incentive schemes and different role structures. We found that intermediaries can increase trust if there is room for indirect reciprocity between the involved parties. We also found that the effect of monetary incentives and social norms cannot be clearly separable in these situations. If properly designed, monetary incentives for intermediaries can have a positive effect. On the one hand, when intermediary rewards are aligned with the trustor’s interest, investments and returns tend to increase. On the other hand, fixed monetary incentives perform less than any other incentive schemes and endogenous social norms in ensuring trust and fairness. These findings should make us reconsider the mantra of incentivization of social and public conventional policy.

Appearance-based first impressions and person memory.

Previous research has demonstrated that people preferentially remember reputational information that is emotionally incongruent to their expectations, but it has left open the question of the generality of this effect. Three conflicting hypotheses were proposed: (a) The effect is restricted to information relevant to reciprocal social exchange. (b) The effect is most pronounced for emotional (approach-and-avoidance-relevant) information. (c) The effect is due to a general tendency of the cognitive system to attend to unexpected and novel information regardless of its (emotional) content. Here, we varied the type of to-be-remembered person information across experiments. To stimulate expectations, we selected faces whose facial appearance was rated as pleasant or disgusting (Experiments 1 and 2), as intelligent or unintelligent (Experiment 3), or as being that of a lawyer or a farmer (Experiment 4). These faces were paired with behavior descriptions that violated or confirmed these appearance-based 1st impressions. Source memory for the association between the faces and the descriptions was assessed with surprise memory tests. The results show that people are willing to form various social expectations based on facial appearance alone, and they support the hypothesis that the classification of the faces in the memory test is biased by schema-congruent guessing. Source memory was generally enhanced for information violating appearance-based social expectations. In sum, the results show that person memory is consistently affected by different kinds of social expectations, supporting the idea that the mechanisms determining memory performance generalize beyond exchange-relevant reputational and emotional information.

FR trust: a fuzzy reputation-based model for trust management in semantic P2P grids

A P2P grid is a special case of grid networks in which P2P communications are used for communication between nodes and trust management. This technology allows creation of a network with greater distribution and scalability. Semantic grids have appeared as an expansion of grid networks in which rich resource metadata are revealed and clearly handled. In a semantic P2P grid, nodes are clustered into different groups based on the semantic similarities between their services. This paper proposes a reputation model for trust management in a semantic P2P grid. We use fuzzy theory, in a trust overlay network named FR TRUST that models the network structure and the storage of reputation information. We present a reputation collection and computation system for semantic P2P grids. The system uses fuzzy theory to compute a peer trust level, which can be either: low, medium, or high. Experimental results demonstrate that FR TRUST combines desirable good computational complexity with high-ranking accuracy.

Partner Selection Using Reputation Information in n-player Cooperative Games

To study cooperation evolution in populations, it is common to use games to model the individuals interactions. When these games are n-player it might be difficult to assign defection responsibility to any particular individual. In this paper the authors present an agent based model where each agent maintains reputation information of other agents. This information is used for partner selection before each game. Any agent collects information from the successive games it plays and updates a private reputation estimate of its candidate partners. This approach is integrated with an approach of variable sized population where agents are born, interact, reproduce and die, thus presenting a possibility of extinction. The results now obtained, for cooperation evolution in a population, show an improvement over previous models where partner selection did not use any reputation information. Populations are able to survive longer by selecting partners taking merely into account an estimate of others’ reputations.

Improving peer neighborhood on P2P video distribution networks using Push/Pull protocol

Peer-to-peer architecture is used as infrastructure for video streaming on the Internet. Nodes that participate in the overlay network cooperate in the task of distributing and forwarding video chunks, making available their local resources to the network. Collectively, the sum of resources and the distribution of activities among the nodes can maintain a good-enough quality, with a more competitive cost compared to centralized solutions. In this context, nodes with uncooperative behaviors can degrade or turn infeasible video distribution with the expected quality. In this paper the behavior of uncooperative nodes with different scores is modeled and their effect on P2P networks that use the Push/Pull protocol to distribute video streams is evaluated. Based on the results, admission and dynamic neighbor selection mechanisms, which use reputation information, are proposed. The principle of these mechanisms is to improve neighborhood quality for cooperative nodes guaranteeing them good video quality, while imposing a penalty to uncooperative nodes, by connecting them to equally uncooperative nodes, as a way to encourage them to change their behavior. The proposed mechanisms were modeled and evaluated using the PeerSim simulator with P4S module. The results indicate the effectiveness of the proposed mechanisms, which impose some insulation to uncooperative nodes that will have equally uncooperative neighbors.

Appearance-based trust behaviour is reduced in children with autism spectrum disorder.

Typical individuals make rapid and reliable evaluations of trustworthiness from facial appearances, which can powerfully influence behaviour. However, the same may not be true for children with autism spectrum disorder. Using an economic trust game, the current study revealed that like typical children, children with autism spectrum disorder rationally modulate their trust behaviour based on non-face cues to partner trustworthiness (e.g. reputation information). Critically, however, they are no more likely to place their trust in partners with faces that look trustworthy to them, than those that look untrustworthy. These results cannot be accounted for by any group differences in children's conceptualization of trustworthiness, ability to read trustworthiness from faces or understanding of the experimental paradigm. Instead, they seem to suggest that there may be a selective failure to spontaneously use facial cues to trustworthiness to guide behaviour in an ecologically valid context.

Doing Business with Strangers: Reputation in Online Service Marketplaces

Online service marketplaces allow service buyers to post their project requests and service providers to bid for them. To reduce the transactional risks, marketplaces typically track and publish previous seller performance. By analyzing a detailed transactional data set with more than 1,800,000 bids corresponding to 270,000 projects posted between 2001 and 2010 in a leading online intermediary for software development services, we empirically study the effects of the reputation system on market outcomes. We consider both a structured measure summarized in a numerical reputation score and an unstructured measure based on the verbal praise left by previous buyers, which we encode using text mining techniques. We find that buyers trade off reputation (both structured and unstructured) and price and are willing to accept higher bids posted by more reputable bidders. Sellers also respond to changes in their own reputation through three different channels. They increase their bids with their reputation score (price effect) but primarily use a superior reputation to increase their probability of being selected (volume effect) as opposed to increasing their bid prices. Negative shocks in seller reputation are associated to an increase in the probability of seller exit (exit effect), but this effect is moderated by the investment that the seller has made in the site. We conclude that participants in this market are very responsive to the numerical reputation score and also to the unstructured reputational information, which behaves in a similar way to the structured numerical reputation score but provides complementary information.

Japanese stock market reaction to announcements of news affecting auditors’ reputation: The case of the Olympus fraud

The revelation of accounting fraud by the Olympus Corporation gave rise to shareholder allegations of audit failure against Olympus’ auditors—Ernst & Young ShinNihon LLC and KPMG AZSA LLC—in 2011. In this study, we investigate whether the auditors’ affiliation with Olympus contributes to divergent perceptions of audit quality in the event of news announcements affecting the reputation of Olympus’ auditors. First, we use a nonparametric generalized rank event study methodology on 918 sample firms from the First Section of the Tokyo Stock Exchange (TSE) to observe Japanese investors’ perceptions of auditor reputation as proxied by abnormal returns. Second, we perform a multivariate linear regression on firms’ abnormal returns after controlling for firm-specific variables. We find that Japanese investors do not respond to negative or neutral reputational information arising from news announcements concerning Olympus’ auditors for firms affiliated and not affiliated with those auditors. In the absence of legal penalties imposed on Olympus’ auditors, we argue that Japanese investors consider the Olympus fraud case as an expected occurrence of audit failure due to a lack of evidence suggesting systematic audit failure on the part of Olympus’ auditors and an expectation of lower audit quality in the Japanese capital market. As a result, Japanese investors do not consider news announcements affecting the Olympus auditors’ reputation as sufficient evidence to change their prior expectation regarding the reputations of the audit firms affiliated with the Olympus fraud case.

An incentive compatible reputation mechanism for P2P systems

In peer-to-peer (P2P) systems, peers often must interact with unknown or unfamiliar peers without the benefit of trusted third parties or authorities to mediate the interactions. Trust management through reputation mechanism to facilitate such interactions is recognized as an important element of P2P systems. It is, however, faced by the problems of how to stimulate reputation information sharing and honest recommendation elicitation. This paper presents an incentive compatible reputation mechanism for P2P systems. It has two unique features: (1) a recommender's trustworthiness and level of confidence about the recommendation is considered for a more accurate calculation of reputations and fair evaluation of recommendations. (2) Incentive for participation and honest recommendation is implemented through a fair differential service mechanism. It relies on peer's level of participation and on the recommendation credibility. Theoretic analysis and simulation show that the reputation mechanism we propose can help peers effectively detect dishonest recommendations in a variety of scenarios where more complex malicious strategies are introduced. Moreover, it can also stimulate peers to send sufficiently honest recommendations. The latter is realized by ensuring that active and honest recommenders, compared to inactive or dishonest ones, can elicit the most honest (helpful) recommendations and thus suffer the least number of wrong trust decisions.

Familiarity with interest breeds gossip: contributions of emotion, expectation, and reputation.

Although gossip serves several important social functions, it has relatively infrequently been the topic of systematic investigation. In two experiments, we advance a cognitive-informational approach to gossip. Specifically, we sought to determine which informational components engender gossip. In Experiment 1, participants read brief passages about other people and indicated their likelihood to share this information. We manipulated target familiarity (celebrity, non-celebrity) and story interest (interesting, boring). While participants were more likely to gossip about celebrity than non-celebrity targets and interesting than boring stories, they were even more likely to gossip about celebrity targets embedded within interesting stories. In Experiment 2, we additionally probed participants' reactions to the stories concerning emotion, expectation, and reputation information conveyed. Analyses showed that while such information partially mediated target familiarity and story interest effects, only expectation and reputation accounted for the interactive pattern of gossip behavior. Our findings provide novel insights into the essential components and processing mechanisms of gossip.

Industrial buyers' use of references, word-of-mouth and reputation in complex buying situation

Purpose – The aim of this paper is to provide understanding on how the buyer can mobilize experience-based information scattered around the business network, by means of customer references, word-of-mouth and reputation, and how this facilitates the buying process. Design/methodology/approach – The qualitative study scrutinizes eight cases comprising buyers of knowledge intensive services and technology innovations. The paper draws on the literature on buying and purchasing, customer references, word-of-mouth and reputational information. Findings – The findings identify the different roles of references, word-of-mouth, collegial advice networks, and reputation, and suggest that experience-based information provides information on offerings, suppliers and the problem solving situation in complex buying per se. Research limitations/implications – The article's contribution is to provide a framework depicting the employment of experience-based information in complex buying, which ensues through focal and continuous buying processes. Insights from this research are broadly applicable to the contexts of knowledge intensive, innovation and solutions business. Further qualitative research should aim to form constructs and define their interrelations to be tested in subsequent quantitative research. Originality/value – This study generates new understanding on how buyers gather and use experience-based information to solve complex problems in buying. It contributes by merging references, word-of-mouth, collegial social networks, and reputation as sources of experience-based information, identifying information embedded in those means, and exploring how the information and means are used throughout the complex buying situation.

From Hitting to Tattling to Gossip: An Evolutionary Rationale for the Development of Indirect Aggression

Adult humans are characterized by low rates of intra-group physical aggression. Since children tend to be more physically aggressive, an evolutionary developmental account shows promise for explaining how physical aggression is suppressed in adults. I argue that this is achieved partly through extended dominance hierarchies, based on indirect reciprocity and linguistic transmission of reputational information, mediated by indirectly aggressive competition. Reviewing the literature on indirect and related forms of aggression provides three pieces of evidence for the claim that evolutionarily old impulses towards physical aggression are socialized into indirect aggression in humans: (i) physical aggression falls in early childhood over the same age range at which indirect aggression increases; (ii) the same individuals engage in both direct and indirect aggression; and (iii) socially dominant individuals practice indirect aggression more frequently. Consideration of the developmental course of indirect aggression is complemented by analysis of similar developments in verbal behaviors that are not always thought of as aggressive, namely tattling and gossip. An important puzzle concerns why indirect aggression becomes more covert, and tattling more derogated, in preadolescence and adolescence. This may be due to the development of new strategies aimed at renegotiating social identity and friendship alliances in the peer group.

Institutions and Cooperation in an Ecology of Games

Social dilemmas have long been studied formally as cooperation games that pit individual gains against those of the group. In the real world, individuals face an ecology of games where they play many such games simultaneously, often with overlapping co-players. Here, we study an agent-based model of an ecology of public goods games and compare the effectiveness of two institutional mechanisms for promoting cooperation: a simple institution of limited group size (capacity constraints) and a reputational institution based on observed behavior. Reputation is shown to allow much higher relative payoffs for cooperators than do capacity constraints, but only if (1) the rate of reputational information flow is fast enough relative to the rate of social mobility, and (2) cooperators are relatively common in the population. When these conditions are not met, capacity constraints are more effective at protecting the interests of cooperators. Because of the simplicity of the limited-group-size rule, capacity constraints can also generate social organization, which promotes cooperation much more quickly than can reputation. Our results are discussed in terms of both normative prescriptions and evolutionary theory regarding institutions that regulate cooperation. More broadly, the ecology-of-games approach developed here provides an adaptable modeling framework for studying a wide variety of problems in the social sciences.

Gossip and Ostracism Promote Cooperation in Groups

The widespread existence of cooperation is difficult to explain because individuals face strong incentives to exploit the cooperative tendencies of others. In the research reported here, we examined how the spread of reputational information through gossip promotes cooperation in mixed-motive settings. Results showed that individuals readily communicated reputational information about others, and recipients used this information to selectively interact with cooperative individuals and ostracize those who had behaved selfishly, which enabled group members to contribute to the public good with reduced threat of exploitation. Additionally, ostracized individuals responded to exclusion by subsequently cooperating at levels comparable to those who were not ostracized. These results suggest that the spread of reputational information through gossip can mitigate egoistic behavior by facilitating partner selection, thereby helping to solve the problem of cooperation even in noniterated interactions.

A SUPER-AGENT-BASED FRAMEWORK FOR REPUTATION MANAGEMENT AND COMMUNITY FORMATION IN DECENTRALIZED SYSTEMS

In this article, we propose a novel super-agent-based framework for reputation management and community formation in decentralized systems. We describe this framework in the context of Web service selection where agents with more capabilities act as super-agents. These super-agents serve as reputation managers to maintain reputation information of services and share the information with other consumer agents that have fewer capabilities than the super-agents. In addition, super-agents can maintain communities and build community-based reputation for a service based on the opinions from all community members that have similar interests and judgement criteria as the super-agents or the other community members. A practical reward mechanism is also introduced to create incentives for super-agents to contribute their resources to maintain reputation and form communities and provide truthful reputation information. Experimental results obtained through simulation confirm that our approach achieves better effectiveness and scalability compared to the systems that do not use super-agents and that do not form communities.

Resource assignment in intelligent environments based on similarity, trust and reputation

This paper offers an agent-based mechanism in intelligent environments IE to recommend profiles to users according to their requirements. This mechanism is based on similarity among profiles as well as trust and reputation among agents. Our proposal is employed in an intelligent university campus where there are different activities that require user profiling e.g., management of resources such as classrooms, meeting rooms, computers in labs, etc. In these activities, a user profile can be seen as a set of recommendations on resources configurations or services that fulfill users' requirements. The proposed mechanism uses an ontology to model the basic concepts related to the profile assignment process. The ontology is provided along with two extensions for representing two specific application domains at the university campus: assignment of meeting rooms and configuration of virtualized environments for iLabs. The performance of the proposed mechanism is evaluated using a university campus scenario simulated by means of a localization network and a prototype application. The results presented in this paper are collected during the interactions between two types of agents user and broker aimed to select the most adequate profiles in each situation. Broker agents estimate the suitability of a profile using trust and reputation information coming from its own experiences or from other brokers. Under uncertainty conditions, arguments offered by broker agents and similarity between two set of user's requirements and preferences are also taken into account to select the best profile.

2A2-X05 集合知を利用したコンテンツ価値推定(スワームロボティクス)

This paper proposes a new method to evaluate content values from reputation information by using a particle filter. In reputation information sites such as Tabelog and Amazon.com, a user can post reviews and vote the evaluation of contents such as books, camera and restaurant. Reputation information sites estimate content values by statistically processing the evaluations which many users have voted, and provide an information for making a decision when many users to select contents. However, it is not always true that all users can vote the content values always correctly. So we need to correct the content value voted by users. In this paper, we model the reputation information site, to consider how to implement the particle filter.

An efficient reputation-based system for wireless ad hoc networks

Consider multi-path routing and forwarding scenarios in wireless ad hoc networks. Selfish and malicious nodes both might deviate from the protocol. However, their intentions and behaviours are different. To extend our previous work on generalised second price auction for stimulating selfish nodes for cooperation, we propose a reputation-based mechanism to cope with malicious behaviours. The core of our mechanism is a decentralised reputation system. Based on available evidence, each node relies on Bayesian inference to internally update its reputation beliefs that how reliable each other node is. Instead of relying on the commonly used passive overhearing technique, 'watchdog', our proposed mechanism requires each source node to actively trigger the detection process to collect evidence towards malicious behaviours. In addition, our mechanism includes a flocking algorithm to allow careful dissemination of reputation information and thus shortens the misbehaviour detection time.