Electronic Commerce Research and Applications
A simulation testbed for analyzing trust and reputation mechanisms in unreliable online markets
Graphical abstract
The consumer decision/action model in Euphemus.
Introduction
Open distributed environments have successfully been modeled as multi-agent systems comprising agents that interact with each other under specific protocols and service-level agreements. Most of these systems take the availability of information for granted and just focus on the decision-making strategies of agents. In real-life environments, though, it is practically impossible for agents to have perfect information about the environment, properties and possible strategies or interests of others. Thus, agents have to make decisions under uncertainty.
One way to tackle uncertainty in open distributed systems is through the definition of a trust and reputation scheme. Trust and reputation (TR) models may guide an agent in deciding with whom to (prefer to) interact. In fact, trust and reputation have been recognized as key issues in autonomic, peer-to-peer and grid computing, as well as in service-oriented architectures and e-commerce applications.
In online markets, with millions of nearly-anonymous agents buying and selling a plethora of goods, self-interested selling agents may act maliciously by not delivering products with the same quality as promised. Thus, trust is a critical issue and it is important for buying agents to reason about the trustworthiness of sellers and determine with which sellers to interact. The higher the value of the products being transacted, the higher the importance of trust for the successful engagement of buyers and sellers.
Trust in online markets seems to be more important than in physical ones (Bakos and Bailey, 1997), since neither seller identity nor product characteristics can be evaluated during the transaction. For this reason, users usually request reliable reports on past performance and truthful statements of future guarantees, and are more likely to participate in web transactions and relationships if they receive strong assurances that they are engaging in a trusting relationship.
As commerce in high-value items becomes increasingly profitable on the Internet, online merchants and auctioneers face enormous challenges in overcoming the trust problem and creating attractive trading environments. In this context, trust and reputation systems provide a foundation for security, stability, and efficiency in the online environment because of their ability to stimulate quality and to sanction poor quality. Trust and reputation scores are assumed to represent and predict future quality and behaviour and thereby to provide valuable decision support for relying parties.
Current work aspires to investigate issues related to trust and reputation in open online markets. Towards this aim we have developed Euphemus, a multivariate agent-based platform for simulating online markets, where agents offer a specific service that others may consume. Setting up a set of example scenarios, we have studied the impact of various sources of information and evaluation criteria in decision making, as well as the effect of altering agents’ behavior for various trust and reputation models.
The paper is organized as follows: Section 2 discusses the state-of-the-art on the available trust and reputation models and testbeds. Section 3 discusses the basic trust and reputation concepts that Euphemus builts upon, the models that have been developed through Euphemus, the architecture and the data flow in the developed framework. Finally, Section 4 discusses the categories of the experiments conducted, while Section 5 summarizes work performed, probes on future extensions and concludes the paper.
Section snippets
Trust and reputation models
The terms of trust and reputation have been used in the literature in various ways, but there is no commonly accepted definition. Josang et al. (2007) divide trust in reliability trust and decision trust, where reliability trust focuses on dependence on the trusted party, as seen by the trusting party (Gambetta, 1990), while decision trust is the extent to which one party is willing to depend on something or somebody in a given situation with a feeling of relative security (McKnight and
Euphemus modeling, architecture and data flow
In order to study issues related to trust and reputation in online markets, we have developed a framework where agents interact taking under consideration the trustworthiness of others. In this section we discuss the main building blocks of the framework, its architecture and the flow of data.
Experiments
In this section we demonstrate the capabilities of the Euphemus framework and perform indicative benchmarking between different TR models against an example market scenario. Focus is given on the methodology to follow in order to define a hypothesis and assess the performance of the model, as well as on the way that different TR models can be implemented and run through Euphemus. In this context, we have performed three sets of experiments: a) we have developed the FIRE model (Huynh et al., 2006
Conclusions and future work
Within the context of our work we have developed a multivariate adaptive testbed, Euphemus, for the analysis of multi-objective trust and reputation mechanisms in agent-based online markets. In Euphemus agents are split in two groups, either providing or consuming a service. The Euphemus TR model draws primitive from the FIRE model, but also provides the ability to easily implement other TR models. It has been developed on AMP, in order to allow for easily reconfigurability, fast adaptation and
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