Unfair Ratings Research Articles

The effect of sovereign credit ratings on democracy in sub-Saharan Africa

PurposeThis study investigates (1) whether democratization drives sovereign credit ratings (SCR) changes (the “democratic advantage”) or whether SCR changes affect democratization, (2) whether the degree of democratization in sub-Saharan African (SSA) countries affects the associations and (3) whether the associations are significantly affected by resource dependence.Design/methodology/approachThis study investigates the effects of SCR changes on democracy in 22 SSA countries over the period of 2000–2020 VEC Granger causality/block exogeneity Wald tests, and impulse responses and variance decomposition analyses with Cholesky ordering and Monte Carlo standard errors in a panel VECM framework.FindingsThe full sample impulse responses find that a SCR shock has a long-run detrimental effect on the democracy and political rights but only a short-run positive impact on civil liberties. Among the sub-samples, it is found that the extent of natural resource dependence does not affect the magnitude of SCR shocks on democratization mentioned above but it is found that a SCR shock affects long-run democracy in SSA countries that are relatively more democratic but is more likely to drive democratic deepening in less democratic SSA countries. The full sample variance decompositions further finds that the variance of SCR to a political rights shock outweighs the effects of all the macroeconomic factors, whereas in more diversified SSA countries, the variances of SCR are much greater for democracy and political rights shocks, which suggests that democratization and political rights in diversified SSA economies are severely affected by SCR changes. In the case of the high and low democracy sub-samples, it is found that the variance of SCR in the relatively higher democracy sub-sample is greater than in the low democracy sub-sample.Social implicationsThese results have three implications for democratization in SSA. First, the effect of a SCR change is not a democratically agnostic and impacts political rights to a greater extent than civil liberties. Second, SCR changes have the potential to spark a negative cycle in SSA countries whereby a downgrade leads to a deterioration in socio-political stability coupled with increased financial economic constraints that in turn drive further downgrades and macroeconomic hardship. Finally, SCR changes are potentially detrimental for democracy in more democratic SSA countries but democratically supportive in less democratic SSA countries. Thus, SSA countries that are relatively politically sophisticated are more exposed to the effects of SCR changes, whereas less politically sophisticated SSA countries can proactively shape their SCRs by undertaking political reforms.Originality/valueThis study is the first to examine the associations between SCR and democracy in SSA. This is critical literature for the Africa’s scholarly work given that the debate on unfair rating actions and claims of subjective rating methods is ongoing.

Applying interrater reliability measure for user credibility assessment in reputation-oriented service discovery

Service Oriented Architecture is built on services in which web service discovery is one of the most widely explored domains. Service reputation plays a vital role in the discovery process while selecting the optimal service from a large pool of functionally equivalent services. Reputation mechanism attempts to forecast the future performance of a service based on its past behaviors generally obtained in the form of feedback ratings submitted by users of the service. These feedback ratings about a service may vary from user to user. Variations in feedback ratings could be because of different users’ different subjective judgments and/or dishonest users’ purposely submitted unfair ratings. This paper proposes a service reputation measurement approach counting such diversified act of rating. To realize this goal, an efficient user credibility assessment methodology has been devised by employing the measure of interrater reliability. Experiments are performed to validate the feasibility and effectiveness of the proposed reputation measurement approach. The experimental results depict that the proposed approach can fairly assess service reputations in the presence of various kinds of raters in the system.

Security analysis of smart contract based rating and review systems: the perilous state of blockchain-based recommendation practices

Nowadays, Blockchain-based rating/review systems are gaining popularity as a backbone for recommender systems due to the inherent cryptographically secured decentralised architecture, immutability, user anonymity, and inclusion of smart contracts. However, the existing Blockchain-based rating/review systems address resistance to the standard attacks, i.e. collusion attack, user threatening, and unfair rating. Still, they do not present security analyses of smart contracts that may result in substantial threats to the users of the systems. This manuscript presents an in-depth study of twelve publicly available security analysis tools and standard vulnerabilities in smart contracts and reviews. The experimental setup uses a two-step approach for selecting the security analysis tool. The first step identifies the seven tools their proposers or independent researchers have compared, and the second step proposes a new method for selecting tools based on continuous improvement. Our experimental results show security issues in 51.72% of the analysed smart contracts of four Blockchain-based rating/review systems. 6.67% of vulnerable smart contracts exhibit high-level severity threats that raise an alarming condition for the current state of system developments.

3R model: A post-purchase context-aware reputation model to mitigate unfair ratings in e-commerce

In e-commerce, retailers or sellers are often assessed by customers or buyers based on reputation information to make wise purchasing decisions. Seller reputation becomes an important credential to shadow seller future behaviour. Most existing reputation models directly aggregate the ratings provided by past buyers. However, it is well documented in practical e-commerce systems that buyers’ ratings can be distorted due to collusion, which negatively affects the applicability of these reputation models. To address this challenging problem, we propose the repurchase-and-return reputation (3R) model, which puts buyers’ ratings into context before aggregating them to compute seller reputation. It considers buyer repurchase and product return behaviour after the point in time when the particular rating was provided. Intuitively, repurchases indicate that the buyers are satisfied with the previously purchased products. Thus, their positive ratings should be given more weight. Similarly, product return behaviours indicate that buyers are dissatisfied with their previous purchasing decisions. Thus, their negative ratings should be given more weight. Based on the proposed 3R reputation model, we design a price premium for a transaction considering the post-purchase behaviour of both the buyer and seller in their transactions. The proposed model is proven capable of achieving a pure strategy Nash equilibrium, in which sellers honestly provide products and buyers prefer to return bad products and repurchase good quality products. Experimental evaluation based on extensive simulation demonstrates that our model can accurately evaluate sellers’ honesty and perform well against prevailing unfair rating attacks.

Blockchain-based decentralized reputation system in E-commerce environment

With the rapid development of E-commerce, more and more people are used to shopping online, in which the reputation scores of sellers play an important role in helping consumers purchase satisfactory products. However, in the existing E-commerce environments, the reputation scores of users (including sellers and buyers) are centrally computed and stored on a centralized cloud server, which might make errors or even engage in fraud and forgery. To address this issue, we propose a blockchain-based decentralized reputation system (BC-DRS) in the E-commerce environment, i.e., online shopping. In this system, the product information including product descriptions and comments is stored in the interplanetary file system (IPFS) and the corresponding address is returned, and the returned address and the reputation scores of users are stored on the blockchain. The reputation evaluation is implemented by designing and deploying a smart contract on the blockchain. Different from the traditional centralized reputation systems (CRSs), the proposed BC-DRS can protect the product information and users reputation scores from intentional and unintentional modifications, since it is very hard to change any data stored in the blockchain and IPFS. Also, as the reputation scores of users are computed and updated by all the ratings of their transactions weighted by the practical transaction factors, it is effective to resist the common attacks, i.e., unfair rating and collusion. In addition, this system also contains a monetary incentive mechanism for the evaluation, which is helpful to form a virtuous circle in online shopping. The proposed BC-DRS is simulated on the popular blockchain platform, i.e., Ethereum with Solidity language. The experimental results and analysis demonstrate that the proposed BC-DRS has desirable usability and reliability.

Application of Machine Learning for Online Reputation Systems

Users on the Internet usually require venues to provide better purchasing recommendations. This can be provided by a reputation system that processes ratings to provide recommendations. The rating aggregation process is a main part of reputation systems to produce global opinions about the product quality. Naive methods that are frequently used do not consider consumer profiles in their calculations and cannot discover unfair ratings and trends emerging in new ratings. Other sophisticated rating aggregation methods that use a weighted average technique focus on one or a few aspects of consumers’ profile data. This paper proposes a new reputation system using machine learning to predict reliability of consumers from their profile. In particular, we construct a new consumer profile dataset by extracting a set of factors that have a great impact on consumer reliability, which serve as an input to machine learning algorithms. The predicted weight is then integrated with a weighted average method to compute product reputation score. The proposed model has been evaluated over three MovieLens benchmarking datasets, using 10-folds cross validation. Furthermore, the performance of the proposed model has been compared to previous published rating aggregation models. The obtained results were promising which suggest that the proposed approach could be a potential solution for reputation systems. The results of the comparison demonstrated the accuracy of our models. Finally, the proposed approach can be integrated with online recommendation systems to provide better purchasing recommendations and facilitate user experience on online shopping markets.

FFED: a novel strategy based on fast entropy to detect attacks against trust computing in cloud

Trust management systems give way to trustworthy interactions in cloud computing. However, malicious cloud users can intentionally provide unfair ratings to benefit or reduce a cloud provider’s reputation. This paper proposes a novel detection strategy to supervise cloud users’ feedback and detect unfair rating attacks for cloud environments based on the Fast Entropy metric and named Feedback Fast Entropy-based Detection strategy (FFED). The provided detection system monitors users’ ratings in successive short periods, detects the unfair rating attacks rapidly in real-time using fast entropy algorithm and permits the scale effectively. Through the performed experimental tests, we prove the good performances of the introduced system and its advantages.

Unfair Pricing and Standard Essential Patents

Technical standards that are agreed within a Standard Development Organization (SDO) often cover several ‘essential’ patents for the implementation of a standard (i.e., Standard Essential Patents, SEPs). In order to allow for the standard implementation, the SEP holder commits to license its patents to any potential licensee on the basis of Fair and Reasonable and Non-Discriminatory (FRAND) conditions. In view of the recent ruling of the UK Supreme Court in Unwired Planet and the judgement of the German Bundesgerichtshof in Sisvel v. Haier, the paper assumes that the FRAND commitment implies a ‘range’ rather than a ‘single’ royalty rate. On the other hand, a royalty rate ‘beyond the outer boundary of the range’ should be considered ‘unfair’, and thus incompatible with the FRAND commitment. Besides representing a breach of the FRAND commitment, an ‘unfair’ royalty rate might also be considered an abuse of a dominant position by the SEP holder, in breach of Art. 102(a) TFEU. This paper analyses whether, and under what circumstances, Art. 102(a) TFEU can be relied upon by a competition authority in Europe to sanction a case where an ‘unfair’ royalty rate has been set by the SEP holder. To this regard, the paper provides a detailed analysis of the EU Court of Justice’s jurisprudence on Art. 102(a) TFEU. In particular, the latter jurisprudence is relied as a ‘yardstick’ to assess ‘when’ competition policy should sanction a request of unfair royalty rate by the SEP holder, ‘how’ a competition agency should assess the case and, eventually, ‘what’ remedies the competition authority might adopt. Economists have elaborated a number of ‘filters’ to define ‘when’ EU competition policy should sanction unfair pricing cases. In particular, antitrust intervention would be justified only in markets that are characterized by high and stable entry barriers, in which a firm enjoys a super-dominant position. Due to the phenomenon of over-declaration, not every SEP is indeed ‘essential’; the market power of the SEP holder thus requires a case-by-case analysis of the ‘essentiality’ of every SEP. A number of authors have also argued that excessive pricing cases should not be sanctioned in industries characterized by dynamic efficiencies. The paper argues that innovation considerations could be considered as efficiency defences in the context of antitrust investigations, rather than in excluding a priori competition policy enforcement in this field. The paper argues that a competition agency should rely on the case law of the Court of Justice of the European Union (CJEU) on Art. 102(a) TFEU to analyse a case of unfair royalty rate. In particular, United Brands cost/price test is not suitable for assessing an unfair royalty rate requested by the SEP holder, since it is de facto impossible to determine the ‘costs of production’ of individual SEPs. On the other hand, in accordance with the CJEU case law, the competition agency might rely on a number of benchmark methods with which to assess the alleged unfairness of the rate. In particular, the agency should verify its findings under multiple benchmark tests, in order to minimize the risk of false negative errors. Finally, the SEP holder could argue that the requested royalty rate is justified by its past R&D investments. In terms of remedies, the paper argues that a competition agency could require the SEP holder to license its ‘essential’ patent; such behavioral remedy is well established in the practice of the European Commission. In light of the recent Broadcom interim decision, if the competition authority was confident about its preliminary findings of unfair pricing, the agency might require the SEP holder to license its ‘essential’ patents via an interim decision; the scope, duration and exact obligations of such a duty would later be refined in the final commitment decision.

CE‐DIFF: An Approach to Identifying and Coping with Irregular Ratings in Collaborative Decision Making

ABSTRACTA key to collaborative decision making is to aggregate individual evaluations into a group decision. One of its fundamental challenges lies in the difficulty in identifying and dealing with irregular or unfair ratings and reducing their impact on group decisions. Little research has attempted to identify irregular ratings in a collaborative assessment task, let alone develop effective approaches to reduce their negative impact on the final group judgment. In this article, based on the synergy theory, we propose a novel consensus‐based collaborative evaluation (CE) method called Collaborative Evaluation based on rating DIFFerence (CE‐DIFF) for identifying irregular ratings and mitigating their impact on collaborative decisions. CE‐DIFF determines and assigns different weights automatically to individual evaluators or ratings based on the level of consistency of one's ratings with the group assessment outcome through continuous iterations. We conducted two empirical experiments to evaluate the proposed method. The results show that CE‐DIFF has higher accuracy in dealing with irregular ratings than existing CE methods, such as arithmetic mean and trimmed mean. In addition, the effectiveness of CE‐DIFF is independent of group size. This study provides a new and more effective method for collaborative assessment, as well as novel theoretical insights and practical implications on how to improve collaborative assessment.

A Fast-Effective Algorithm on A Concise Representation of Top Rated Utility Mining Datasets

The average of customer ratings on a product, which we call a reputation, is one of the key factors in online purchasing decisions. There is, however, no guarantee of the trustworthiness of a reputation since it can be manipulated rather easily. In this paper, we define false reputation as the problem of a reputation being manipulated by unfair ratings and design a general framework that provides trustworthy reputations. For this purpose, we propose Trust-reputation, an algorithm that iteratively adjusts a reputation based on the confidence of customer ratings. We also show the effectiveness of Trust-reputation through extensive experiments in comparisons to state-of-the-art approaches.

The fair decision-making of children and adolescents with high-functioning autism spectrum disorder from the perspective of dual-process theories

BackgroundFairness has received much attention in our society. At present, the findings regarding fair decision-making in high-functioning autism spectrum disorder (HF-ASD) are inconsistent. Previous studies have shown that the fair decision-making of typically developing children is influenced by theory of mind (ToM) and executive functioning (EF). As those with HF-ASD have defects in both domains, this study aims to explore the differences in fair decision-making between children and adolescents with HF-ASD and those with typical development (TD).MethodsWe used a simple ultimatum game (UG) to explore 31 children and adolescents with HF-ASD and 38 children and adolescents with TD. T tests and chi-square tests were used to compare group differences, and Pearson correlation analysis and stepwise regression analysis were used to analyse the mechanisms influencing the two groups’ unfair acceptance rates.ResultsThe results show that children with HF-ASD are more likely to accept unfair offers, but for adolescents, the difference is not significant. Regression analysis showed that the interaction between the behavior regulation index (BRI) and age could negatively predict the unfair acceptance rate of children and adolescents with HF-ASD. Working memory and ToM can negatively predict the unfair acceptance rate of those with TD.ConclusionThis study concluded that the development of fair decision-making by children and adolescents with HF-ASD falls far behind that of those with TD. Intuition processes play a dominant role in the fair decision-making processes of children and adolescents with HF-ASD, and we believe that comorbidity, age, experience and emotional management are important factors influencing the fair decision-making of individuals with HF-ASD.

A Randomized Reputation System in the Presence of Unfair Ratings

With the increasing popularity of online shopping markets, a significant number of consumers rely on these venues to meet their demands while choosing different products based on the ratings provided by others. Simultaneously, consumers feel confident in expressing their opinions through ratings. As a result, millions of ratings are generated on the web for different products, services, and dealers. Nonetheless, a noticeable number of users post unfair feedback. Recent studies have shown that reputation escalation is emerging as a new service, by which dealers pay to receive good feedback and escalate their ratings in online shopping markets. Therefore, finding robust and reliable ways to distinguish between fake and trustworthy ratings from users is a crucial task for every online shopping market. Moreover, with the dramatic increase in the number of ratings provided by consumers, scalability has arisen as another significant issue in the existing methods of reputation systems. To tackle these issues, we propose a randomized algorithm that calculates the reputation based on a random sample of the ratings. Since the randomly selected sample has a logarithmic size, it guarantees feasible scalability for large-scale online review systems. In addition, the randomness nature of the algorithm makes it robust against unfair ratings. We provide a thorough theoretical analysis of the proposed algorithm and validate its effectiveness through extensive empirical evaluation using real-world and synthetically generated datasets. Our experimental results show that the proposed method provides a high accuracy while running much faster than the existing iterative filtering approaches.

Совершенствование тарифной политики пригородных пассажирских компаний

The problem of the construction of a new system of rate-making in the suburban passenger transportation is described. The analyses of the present day rate systems is conducted. It determined such disadvantages as the absence of the systems flexibility, the record of the competitiveness with other means of transport and social fair pricing. Methods of the rate-making control of the suburban railway transport is researched basing on the multi-level optimization from the view point of its implementation to practical use of the suburban passenger companies. In spite of the correct rate-making conception this method forms socially unfair rates, lets passengers save on the payment of transportation due to «sectoring» the tickets between a rail line station. This can raise the unprofitability of the suburban companies. Moreover, the method of the competitiveness with alternative means of transport is produced, mostly, for the ground, but not on separated passenger correspondences. On the analyses of theory and practical work of the suburban companies the project of the calculating method of fair rates in the suburban lines is offered, which allows to work out a rate matrix for people, depending on their social and economical position in municipal structures of the regional service, the transportation expenses of a suburban company and a probable amount of a regional grant. The rate matrix creation is based on the principals of providing the social fairness. It is reached due to the use of kilometer-by-kilometer station-by-station rate-making type. A suburban company works within competitiveness, therefore the flexible rate policy contains an element of the market pricing to attract an extra passenger flow to the suburban trains. An algorithm of the rate matrix calculation is offered. It takes into account restrictions of the passengers’ solvent demands and the amount of grants with the competitive ability-related rates.

Information Theoretical Analysis of Unfair Rating Attacks Under Subjectivity

Ratings provided by advisors can help an advisee to make decisions, e.g., which seller to select in e-commerce. Unfair rating attacks—where dishonest ratings are provided to mislead the advisee—impact the accuracy of decision making. Current literature focuses on specific classes of unfair rating attacks, which does not provide a complete picture of the attacks. We provide the first formal study that addresses all attack behavior that is possible within a given system. We propose a probabilistic modeling of rating behavior, and apply information theory to quantitatively measure the impact of attacks. In particular, we can identify the attack with the worst impact. In the simple case, honest advisors report the truth straightforwardly, and attackers rate strategically. In real systems, the truth (or an advisor’s view on it) may be subjective, making even honest ratings inaccurate. Although there exist methods to deal with subjective ratings, whether subjectivity influences the effect of unfair rating attacks was an open question. We discover that subjectivity decreases the robustness against attacks.

Building Sentiment Analysis Model and Compute Reputation Scores in E-Commerce Environment Using Machine Learning Techniques

Online reputation systems are a novel and active part of e-commerce environments such as eBay, Amazon, etc. These corporations use reputation reporting systems for trust evaluation by measuring the overall feedback ratings given by buyers, which enables them to compute the reputation score of their products. Such evaluation and computation processes are closely related to sentiment analysis and opinion mining. These techniques incorporate new features into traditional tasks, like polarity detection for positive or negative reviews. The “all excellent reputation” problem is common in the e-commerce domain. Another problem is that sellers can write unfair reviews to endorse or reject any targeted product since a higher reputation leads to higher profits. Therefore, the purpose of the present work is to use a statistical technique for excluding unfair ratings and to illustrate its effectiveness through simulations. Also, the authors have calculated reputation scores from users' feedback based on a sentiment analysis model (SAM). Experimental results demonstrate the effectiveness of the approach.

ARM: Toward Adaptive and Robust Model for Reputation Aggregation

In dynamic, open, and service-oriented computing environments, e.g., e-commerce and crowdsourcing, service consumers must choose one of the services or items to complete their tasks. Due to the scale and dynamic characteristics of these environments, service consumers may have little or no experience with the available services. To this end, reputation systems are proposed and have played a crucial role in the success of online service-oriented transactions. In this paper, we study the current reputation systems used in commercial environments. In these rating-based reputation systems, we found they are not only resilient to the changes (time lag) but also vulnerable to unfair ratings. To address the problems in parallel, we propose an adaptive reputation model (ARM). ARM can dynamically adjust its model parameters to adapt the latest changes in a service. To tackle time lag, the proposed model generalizes the fixed sliding window, used in current commercial platforms, into a dynamic sliding window mechanism. Thus, the model can completely mitigate the influence of obsolete ratings. To detect unfair ratings, our model implements a statistical strategy based on hypothesis testing after transforming the ratings in the linear window into residuals. Experiments not only validate the effectiveness of the proposed model but also show that it outperforms the existing reputation system by 45% on average based on five test cases. The results also show that the proposed model can asymptotically converge to the underlying reputation value as ratings begin to accumulate. Note to Practitioners —The reputation models adopted by current commercial platforms, such as Amazon, eBay, and Taobao, not only suffer heavily from unfair rating but also resilient to the changes in services. To address the problems simultaneously, we design and implement a hybrid model that continuously monitors received ratings and aggregates the reputation value in a self-adaptive way. Our model first fits received fair ratings using the Bayesian linear regression approach and captures the distribution of fair ratings; it then filters out unfair ratings leveraging hypothesis testing. Finally, to sensitively respond the dynamic service changes, the dynamic sliding window algorithm in our model shifts the rating collection window into a new one and discards outdated ratings, reputation value is aggregated in the new window to mitigate the influence of obsolete ratings. Extensive experiments are conducted on widely used scenarios to demonstrate the efficiency and the effectiveness of our proposed model.

An unfair tax and an unfair rate

The sector has long called for an end to the business rates imposed on nurseries that are hampering investment and threatening sustainability. The tax on space is meant for factories, not education sites.

A Provenance-Aware Multi-dimensional Reputation System for Online Rating Systems

Online rating systems are widely accepted as means for quality assessment on the web and users increasingly rely on these systems when deciding to purchase an item online. This makes such rating systems frequent targets of attempted manipulation by posting unfair rating scores. Therefore, providing useful, realistic rating scores as well as detecting unfair behavior are both of very high importance. Existing solutions are mostly majority based, also employing temporal analysis and clustering techniques. However, they are still vulnerable to unfair ratings. They also ignore distances between options, the provenance of information, and different dimensions of cast rating scores while computing aggregate rating scores and trustworthiness of users. In this article, we propose a robust iterative algorithm which leverages information in the profile of users and provenance of information, and which takes into account the distance between options to provide both more robust and informative rating scores for items and trustworthiness of users. We also prove convergence of iterative ranking algorithms under very general assumptions, which are satisfied by the algorithm proposed in this article. We have implemented and tested our rating method using both simulated data as well as four real-world datasets from various applications of reputation systems. The experimental results demonstrate that our model provides realistic rating scores even in the presence of a massive amount of unfair ratings and outperforms the well-known ranking algorithms.

GSLDA: LDA-based group spamming detection in product reviews

Online product reviews are becoming increasingly important due to their guidance function in people’s purchase decisions. As being highly subjective, online reviews are subject to opinion spamming, i.e., fraudsters write fake reviews or give unfair ratings to promote or demote target products. Although there have been much efforts in this field, the problem is still left open due to the difficulties in gathering ground-truth data. As more and more people are using Internet in everyday life, group review spamming, which involves a group of fraudsters writing hype-reviews (promote) or defaming-reviews (demote) for one or more target products, becomes the main form of review spamming. In this paper, we propose a LDA-based computing framework, namely GSLDA, for group spamming detection in product review data. As a completely unsupervised approach, GSLDA works in two phases. It first adapts LDA (Latent Dirichlet Allocation) to the product review context in order to bound the closely related group spammers into a small-sized reviewer cluster, and then it extracts high suspicious reviewer groups from each LDA-clusters. Experiments on three real-world datasets show that GSLDA can detect high quality spammer groups, outperforming many state-of-the-art baselines in terms of accuracy.

A Modified Bayesian Trustworthiness Evaluation Method to Mitigate the Effect of Unfair Ratings

The choice of trustworthy interaction partners is one of the key factors for successful transactions in online communities. To choose the most trustworthy sellers to interact with, buyers rely on trust and reputation models. Therefore, online systems must be able to accurately assess peers’ trustworthiness. The Beta distribution function provides a sound mathematical basis for combining feedback and deriving users’ trustworthiness. But the Beta reputation system suffers from many forms of cheating behavior such as the proliferation of unfair positive ratings, leading a poor service provider to build a good reputation, and the proliferation of unfair negative feedback, leading a good service provider to end up with a bad reputation. In this paper, we propose a new and coherent method for computing users’ trustworthiness by combining the Beta trustworthiness expectation function with the credibility function. This novel combination mechanism mitigates the impact of unfair ratings. In comparison with Bayesian trust model, we quantitatively show that our approach provides significantly more accurate estimation of peers’ trustworthiness through feedback gathered from multiple sources. Furthermore, we propose an extension of Bayesian trustworthiness expectation function by introducing the initial trust propensity to allow assessing individuals’ initial trust.