Promoting Honesty in Electronic Marketplaces: Combining Trust Modeling and Incentive Mechanism Design

This thesis work is in the area of modeling trust in multi-agent systems, systems of software agents designed to act on behalf of users (buyers and sellers), in applications such as e-commerce. The focus is on developing an approach for buyers to model the trustworthiness of sellers in order to make effective decisions about which sellers to select for business. One challenge is the problem of unfair ratings, which arises when modeling the trust of sellers relies on ratings provided by other buyers (called advisors). Existing approaches for coping with this problem fail in scenarios where the majority of advisors are dishonest, buyers do not have much personal experience with sellers, advisors try to flood the trust modeling system with unfair ratings, and sellers vary their behavior widely. We propose a novel personalized approach for effectively modeling trustworthiness of advisors, allowing a buyer to 1) model the private reputation of an advisor based on their ratings for commonly rated sellers 2) model the public reputation of the advisor based on all ratings for the sellers ever rated by that agent 3) flexibly weight the private and public reputation into one combined measure of the trustworthiness of the advisor. Our approach tracks ratings provided according to their time windows and limits the ratings accepted, in order to cope with advisors flooding the system and to deal with changes in agents' behavior. Experimental evidence demonstrates that our model outperforms other models in detecting dishonest advisors and is able to assist buyers to gain the largest profit when doing business with sellers. Equipped with this richer method for modeling trustworthiness of advisors, we then embed this reasoning into a novel trust-based incentive mechanism to encourage agents to be honest. In this mechanism, buyers select the most trustworthy advisors as their neighbors from which they can ask advice about sellers, forming a social network. In contrast with other researchers, we also have sellers model the reputation of buyers. Sellers will offer better rewards to satisfy buyers that are well respected in the social network, in order to build their own reputation. We provide precise formulae used by sellers when reasoning about immediate and future profit to determine their bidding behavior and the rewards to buyers, and emphasize the importance for buyers to adopt a strategy to limit the number of sellers that are considered for each good to be purchased. We theoretically prove that our mechanism promotes honesty from buyers in reporting seller ratings, and honesty from sellers in delivering products as promised. We also provide a series of experimental results in a simulated dynamic environment where agents may be arriving and departing. This provides a stronger defense of the mechanism as one that is robust to important conditions in the marketplace. Our experiments clearly show the gains in profit enjoyed by both honest sellers and honest buyers when our mechanism is introduced and our proposed strategies are followed. In general, our research will serve to promote honesty amongst buyers and sellers in e-marketplaces. Our particular proposal of allowing sellers to model buyers opens a new direction in trust modeling research. The novel direction of designing an incentive mechanism based on trust modeling and using this mechanism to further help trust modeling by diminishing the problem of unfair ratings will hope to bridge researchers in the areas of trust modeling and mechanism design

Dynamic Credibility Threshold Assignment in Trust and Reputation Mechanisms Using PID Controller

In online shopping buyers do not have enough information about sellers and cannot inspect the products before purchasing them. To help buyers find reliable sellers, online marketplaces deploy Trust and Reputation Management (TRM) systems. These systems aggregate buyers’ feedback about the sellers they have interacted with and about the products they have purchased, to inform users within the marketplace about the sellers and products before making purchases. Thus positive customer feedback has become a valuable asset for each seller in order to attract more business. This naturally creates incentives for cheating, in terms of introducing fake positive feedback. Therefore, an important responsibility of TRM systems is to aid buyers find genuine feedback (reviews) about different sellers. Recent TRM systems achieve this goal by selecting and assigning credible advisers to any new customer/buyer. These advisers are selected among the buyers who have had experience with a number of sellers and have provided feedback for their services and goods. As people differ in their tastes, the buyer feedback that would be most useful should come from advisers with similar tastes and values. In addition, the advisers should be honest, i.e. provide truthful reviews and ratings, and not malicious, i.e. not collude with sellers to favour them or with other buyers to badmouth some sellers. Defining the boundary between dishonest and honest advisers is very important. However, currently, there is no systematic approach for setting the honesty threshold which divides benevolent advisers from the malicious ones. The thesis addresses this problem and proposes a market-adaptive honesty threshold management mechanism. In this mechanism the TRM system forms a feedback system which monitors the current status of the e-marketplace. According to the status of the e-marketplace the feedback system improves the performance utilizing PID controller from the field of control systems. The responsibility of this controller is to set the the suitable value of honesty threshold. The results of experiments, using simulation and real-world dataset show that the market-adaptive honesty threshold allows to optimize the performance of the marketplace with respect to throughput and buyer satisfaction

Rational Trust Modeling

Trust models are widely used in various computer science disciplines. The main purpose of a trust model is to continuously measure trustworthiness of a set of entities based on their behaviors. In this article, the novel notion of "rational trust modeling" is introduced by bridging trust management and game theory. Note that trust models/reputation systems have been used in game theory (e.g., repeated games) for a long time, however, game theory has not been utilized in the process of trust model construction; this is where the novelty of our approach comes from. In our proposed setting, the designer of a trust model assumes that the players who intend to utilize the model are rational/selfish, i.e., they decide to become trustworthy or untrustworthy based on the utility that they can gain. In other words, the players are incentivized (or penalized) by the model itself to act properly. The problem of trust management can be then approached by game theoretical analyses and solution concepts such as Nash equilibrium. Although rationality might be built-in in some existing trust models, we intend to formalize the notion of rational trust modeling from the designer's perspective. This approach will result in two fascinating outcomes. First of all, the designer of a trust model can incentivise trustworthiness in the first place by incorporating proper parameters into the trust function, which can be later utilized among selfish players in strategic trust-based interactions (e.g., e-commerce scenarios). Furthermore, using a rational trust model, we can prevent many well-known attacks on trust models. These two prominent properties also help us to predict behavior of the players in subsequent steps by game theoretical analyses

An adaptive trust based service quality monitoring mechanism for cloud computing

Cloud computing is the newest paradigm in distributed computing that delivers computing resources over the Internet as services. Due to the attractiveness of cloud computing, the market is currently flooded with many service providers. This has necessitated the customers to identify the right one meeting their requirements in terms of service quality. The existing monitoring of service quality has been limited only to quantification in cloud computing. On the other hand, the continuous improvement and distribution of service quality scores have been implemented in other distributed computing paradigms but not specifically for cloud computing. This research investigates the methods and proposes mechanisms for quantifying and ranking the service quality of service providers. The solution proposed in this thesis consists of three mechanisms, namely service quality modeling mechanism, adaptive trust computing mechanism and trust distribution mechanism for cloud computing. The Design Research Methodology (DRM) has been modified by adding phases, means and methods, and probable outcomes. This modified DRM is used throughout this study. The mechanisms were developed and tested gradually until the expected outcome has been achieved. A comprehensive set of experiments were carried out in a simulated environment to validate their effectiveness. The evaluation has been carried out by comparing their performance against the combined trust model and QoS trust model for cloud computing along with the adapted fuzzy theory based trust computing mechanism and super-agent based trust distribution mechanism, which were developed for other distributed systems. The results show that the mechanisms are faster and more stable than the existing solutions in terms of reaching the final trust scores on all three parameters tested. The results presented in this thesis are significant in terms of making cloud computing acceptable to users in verifying the performance of the service providers before making the selection

Trust Transfer in the Sharing Economy - A Survey-Based Approach

The sharing economy is experiencing explosive growth around the globe in which trust plays a crucial role and builds the foundation of the services. With the rise of the sharing economy and the increasing numbers of cross-contextual users, this research aims at the lack of trust transference possibilities across the Peer-to-Peer applications and has the goal to find out whether and how trust can be transferred between the platforms, so that new users do not have to create their reputation from scratch every time they join a new platform. First, this research provides an in-depth literature review of trust transfer theories. Secondly, a conceptual research model for the role of the imported trust in the context of the sharing economy is outlined and analysed by proposing and evaluating a questionnaire using structural equation modeling. Throughout the study, a three-dimensional scale of trust, i.e. ability, benevolence and integrity, is validated in the context of the sharing economy. The experimental study shows that both the overall and subdimensional trust in the provider is directly affected by the overall trust in the platform, the perceived reputation as well as the perceived social presence. The study also provides empirical evidence for the existence of trust transferability. The findings show that in addition to the immanent ratings, imported ratings also significantly affect the perceived reputation of the provider positively. Finally, this paper discusses further details of the trust transfer processes and broadens implications for future research. The sharing economy is experiencing explosive growth around the globe in which trust plays a crucial role and builds the foundation of the services. With the rise of the sharing economy and the increasing numbers of cross-contextual users, this research aims at the lack of trust transference possibilities across the Peer-to-Peer applications and has the goal to find out whether and how trust can be transferred between the platforms, so that new users do not have to create their reputation from scratch every time they join a new platform. First, this research provides an in-depth literature review of trust transfer theories. Secondly, a conceptual research model for the role of the imported trust in the context of the sharing economy is outlined and analysed by proposing and evaluating a questionnaire using structural equation modeling. Throughout the study, a three-dimensional scale of trust, i.e. ability, benevolence and integrity, is validated in the context of the sharing economy. The experimental study shows that both the overall and subdimensional trust in the provider is directly affected by the overall trust in the platform, the perceived reputation as well as the perceived social presence. The study also provides empirical evidence for the existence of trust transferability. The findings show that in addition to the immanent ratings, imported ratings also significantly affect the perceived reputation of the provider positively. Finally, this paper discusses further details of the trust transfer processes and broadens implications for future research.  Keywords: Sharing Economy, Trust, Trust Transfer, Reputation, Peer-to-pee

Using Identity Premium for Honesty Enforcement and Whitewashing Prevention

One fundamental issue with existing reputation systems, particularly those implemented in open and decentralized environments, is whitewashing attacks by opportunistic participants. If identities are cheap, it is beneficial for a rational provider to simply defect when selling services to its clients, leave the system to avoid punishment and then rejoin with a new identity. Current work usually assumes the existence of an effective identity management scheme to avoid the problem, without proposing concrete solutions to directly prevent this unwanted behavior. This article presents and analyzes an incentive mechanism to effectively motivate honesty of rationally opportunistic providers in the aforementioned scenario, by eliminating incentives of providers to change their identities. The main idea is to give each provider an identity premium, with which the provider may sell services at higher prices depending on the duration of its presence in the system. Our price-based incentive mechanism, implemented with the use of a reputation-based provider selection protocol and a reverse auction scheme, is shown to significantly reduce the impact of malicious and strategic ratings, while still allowing explicit competition among the providers. It is proven that if the temporary cheating gain by a provider is bounded and small and given a trust model with a reasonable low error bound in identifying malicious ratings, our approach can effectively eliminate irrationally malicious providers and enforce honest behavior of rationally opportunistic ones, even when cheap identities are available. We suggest an identity premium function that helps such honesty to be sustained given a certain cost of identities and analyze incentives of participants in accepting the proposed premium. Related implementation issues in different application scenarios are also discussed

The historical origins of corruption in the developing world: a comparative analysis of East Asia

A new approach has emerged in the literature on corruption in the developing world that breaks with the assumption that corruption is driven by individualistic self-interest and, instead, conceptualizes corruption as an informal system of norms and practices. While this emerging neo-institutionalist approach has done much to further our understanding of corruption in the developing world, one key question has received relatively little attention: how do we explain differences in the institutionalization of corruption between developing countries? The paper here addresses this question through a systematic comparison of seven developing and newly industrialized countries in East Asia. The argument that emerges through this analysis is that historical sequencing mattered: countries in which the "political marketplace" had gone through a process of concentration before universal suffrage was introduced are now marked by less harmful types of corruption than countries where mass voting rights where rolled out in a context of fragmented political marketplaces. The paper concludes by demonstrating that this argument can be generalized to the developing world as a whole

A Reputation-based Framework for Honest Provenance Reporting

Given the distributed, heterogenous, and dynamic nature of service-based IoT systems, capturing circumstances data underlying service provisions becomes increasingly important for understanding process flow and tracing how outputs came about, thus enabling clients to make more informed decisions regarding future interaction partners. Whilst service providers are the main source of such circumstances data, they may often be reluctant to release it, e.g. due to the cost and effort required, or to protect their interests. In response, this paper introduces a reputation-based framework, guided by intelligent software agents, to support the sharing of truthful circumstances information by providers. In this framework, assessor agents, acting on behalf of clients, rank and select service providers according to reputation, while provider agents, acting on behalf of service providers, learn from the environment and adjust provider’s circumstances provision policies in the direction that increases provider profit with respect to perceived reputation. The novelty of the reputation assessment model adopted by assessor agents lies in affecting provider reputation scores by whether or not they reveal truthful circumstances data underlying their service provisions, in addition to other factors commonly adopted by existing reputation schemes. The effectiveness of the proposed framework is demonstrated through an agent-based simulation including robustness against a number of attacks, with a comparative performance analysis against FIRE as a baseline reputation model

A reputation-based framework for honest provenance reporting

Given the distributed, heterogenous, and dynamic nature of service-based IoT systems, capturing circumstances data underlying service provisions becomes increasingly important for understanding process flow and tracing how outputs came about, thus enabling clients to make more informed decisions regarding future interaction partners. Whilst service providers are the main source of such circumstances data, they may often be reluctant to release it, e.g. due to the cost and effort required, or to protect their interests. In response, this paper introduces a reputation-based framework, guided by intelligent software agents, to support the sharing of truthful circumstances information by providers. In this framework, assessor agents, acting on behalf of clients, rank and select service providers according to reputation, while provider agents, acting on behalf of service providers, learn from the environment and adjust provider’s circumstances provision policies in the direction that increases provider profit with respect to perceived reputation. The novelty of the reputation assessment model adopted by assessor agents lies in affecting provider reputation scores by whether or not they reveal truthful circumstances data underlying their service provisions, in addition to other factors commonly adopted by existing reputation schemes. The effectiveness of the proposed framework is demonstrated through an agent-based simulation including robustness against a number of attacks, with a comparative performance analysis against FIRE as a baseline reputation model