Acceptance of feedbacks in reputation systems: the role of online social interactions

In an online environment, the aim of reputation systems is to let parties rate each other and to help consumers in deciding whether to transact with a given party. In current reputation systems for e-commerce, users have to trust unreliable information sources and anonymous people. As a result, users are not only hesitant to trust online seller but also to reputation systems. Therefore, there is a need to improve current reputation systems by allowing users to make buying decision based on reliable source of information. This paper proposes a new approach of sharing knowledge and experience in reputation systems by utilizing social interactions. This study examines the potentials of integrating social relations information in reputation systems by proposing a model of acceptance of feedbacks in reputation systems

Reconsidering online reputation systems

Social and socioeconomic interactions and transactions often require trust. In digital spaces, the main approach to facilitating trust has effectively been to try to reduce or even remove the need for it through the implementation of reputation systems. These generate metrics based on digital data such as ratings and reviews submitted by users, interaction histories, and so on, that are intended to label individuals as more or less reliable or trustworthy in a particular interaction context. We suggest that conventional approaches to the design of such systems are rooted in a capitalist, competitive paradigm, relying on methodological individualism, and that the reputation technologies themselves thus embody and enact this paradigm in whatever space they operate in. We question whether the politics, ethics and philosophy that contribute to this paradigm align with those of some of the contexts in which reputation systems are now being used, and suggest that alternative approaches to the establishment of trust and reputation in digital spaces need to be considered for alternative contexts

Simple Reputation Systems

This paper develops a model of simple 'reputation systems' that monitor and publish information about the behavior of sellers in a market with search frictions and asymmetric information. The reputations created by these systems influence the equilibrium search patterns of buyers and thus provide for market-based 'punishment' of bad behavior. Our model allows us to determine the effects of the introduction of a reputation system on the behavior and welfare of buyers and sellers in such a market. We show that a simple reputation system that rewards honesty can enhance welfare by allowing good sellers to truthfully signal their type. However, we also show that in some cases the same reputation system is prone to strategic manipulation by sellers who always have low quality products. In this case, we show that an alternative simple reputation system that screens for type can be superior

Network-aware Evaluation Environment for Reputation Systems

Parties of reputation systems rate each other and use ratings to compute reputation scores that drive their interactions. When deciding which reputation model to deploy in a network environment, it is important to find the most suitable model and to determine its right initial configuration. This calls for an engineering approach for describing, implementing and evaluating reputation systems while taking into account specific aspects of both the reputation systems and the networked environment where they will run. We present a software tool (NEVER) for network-aware evaluation of reputation systems and their rapid prototyping through experiments performed according to user-specified parameters. To demonstrate effectiveness of NEVER, we analyse reputation models based on the beta distribution and the maximum likelihood estimation

A Logical Framework for Reputation Systems

Reputation systems are meta systems that record, aggregate and distribute information about the past behaviour of principals in an application. Typically, these applications are large-scale open distributed systems where principals are virtually anonymous, and (a priori) have no knowledge about the trustworthiness of each other. Reputation systems serve two primary purposes: helping principals decide whom to trust, and providing an incentive for principals to well-behave. A logical policy-based framework for reputation systems is presented. In the framework, principals specify policies which state precise requirements on the past behaviour of other principals that must be fulfilled in order for interaction to take place. The framework consists of a formal model of behaviour, based on event structures; a declarative logical language for specifying properties of past behaviour; and efficient dynamic algorithms for checking whether a particular behaviour satisfies a property from the language. It is shown how the framework can be extended in several ways, most notably to encompass parameterized events and quantification over parameters. In an extended application, it is illustrated how the framework can be applied for dynamic history-based access control for safe execution of unknown and untrusted programs

Fairness Emergence in Reputation Systems

Reputation systems have been used to support users in making decisions under uncertainty or risk that is due to the autonomous behavior of others. Research results support the conclusion that reputation systems can protect against exploitation by unfair users, and that they have an impact on the prices and income of users. This observation leads to another question: can reputation systems be used to assure or increase the fairness of resource distribution? This question has a high relevance in social situations where, due to the absence of established authorities or institutions, agents need to rely on mutual trust relations in order to increase fairness of distribution. This question can be formulated as a hypothesis: in reputation (or trust management) systems, fairness should be an emergent property. The notion of fairness can be precisely defined and investigated based on the theory of equity. In this paper, we investigate the Fairness Emergence hypothesis in reputation systems and prove that , under certain conditions, the hypothesis is valid for open and closed systems, even in unstable system states and in the presence of adversaries. Moreover, we investigate the sensitivity of Fairness Emergence and show that an improvement of the reputation system strengthens the emergence of fairness. Our results are confirmed using a trace-driven simulation from a large Internet auction site.Trust, Simulation, Fairness, Equity, Emergence, Reputation System

Evolution of Cooperation when Feedback to Reputation Scores is Voluntary

Reputation systems are used to facilitate interaction between strangers in one-shot social dilemmas, like transactions in e-commerce. The functioning of various reputation systems depend on voluntary feedback derived from the participants in those social dilemmas. In this paper a model is presented under which frequencies of providing feedback to positive and negative experiences in reputation systems explain observed levels of cooperation. The results from simulations show that it is not likely that reputation scores alone will lead to high levels of cooperation.Trust, Reputation, One-Shot Prisoner Dilemma, Voluntary Feedback, Symbols

Specifying and analysing reputation systems with coordination languages

Reputation systems are nowadays widely used to support decision making in networked systems. Parties in such systems rate each other and use shared ratings to compute reputation scores that drive their interactions. The existence of reputation systems with remarkable differences calls for formal approaches to their analysis. We present a verification methodology for reputation systems that is based on the use of the coordination language Klaim and related analysis tools. First, we define a parametric Klaim specification of a reputation system that can be instantiated with different reputation models. Then, we consider stochastic specification obtained by considering actions with random (exponentially distributed) duration. The resulting specification enables quantitative analysis of properties of the considered system. Feasibility and effectiveness of our proposal is demonstrated by reporting on the analysis of two reputation models