reputation systems
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AbstractIndirect reciprocity is a key mechanism that promotes cooperation in social dilemmas by means of reputation. Although it has been a common practice to represent reputations by binary values, either ‘good’ or ‘bad’, such a dichotomy is a crude approximation considering the complexity of reality. In this work, we studied norms with three different reputations, i.e., ‘good’, ‘neutral’, and ‘bad’. Through massive supercomputing for handling more than thirty billion possibilities, we fully identified which norms achieve cooperation and possess evolutionary stability against behavioural mutants. By systematically categorizing all these norms according to their behaviours, we found similarities and dissimilarities to their binary-reputation counterpart, the leading eight. We obtained four rules that should be satisfied by the successful norms, and the behaviour of the leading eight can be understood as a special case of these rules. A couple of norms that show counter-intuitive behaviours are also presented. We believe the findings are also useful for designing successful norms with more general reputation systems.

Because of the great characteristics of Wireless Sensor Networks like easier to use and less cost of deployment, they have attracted the researchers to conduct the investigations and received the importance in various civilian and military applications. A number of security attacks have been involved due to the lack of centralized management in these networks. The packet drop attack is one of the attacks and it has a compromised node which drops the malicious packets. In WSNs, different techniques have been implemented to identify the packet drop attack but none of them provides the feasibility to stop or isolate their occurrence in the future. In recent times, the reputation systems provide the way to identify the trustworthy nodes for data forwarding. But the lack of data classification in the reputation systems affects the false positive rate. In this paper, a novel CONFIDENT SCORE based BAYESIAN FILTER NODE MONITORING AGENT (CFS-BFNMA) mechanism is introduced to identify & avoid the packet drop nodes and also to monitor the node behaviours to improve the false positive rate. The final CFS of a node is estimated based on the node past and threshold CFS values. The node monitoring agents (BFNMA) constantly monitors the forwarding behaviour of the nodes and assigns CFS based on the successful forwards. The NMA saves the copy of the data packets in their buffers before forwarding to the neighbour nodes to compare them. Also, this BFNMA analyses the traffic pattern of every round of transmission to improve the false positive rate. By comparing with other conventional security algorithms, the proposed mechanism has been improved the network security & false positive rate drastically based on the simulation results.

<p>Since the earliest human communities, reputation has been used by people to decide whether they should trust and interact with someone else. Traditionally, reputation was established through a person’s standing, word of mouth and their associations. However, with the increasingly widespread use of the Internet, this situation has changed. In particular, all of the normal cues that help to build reputation are missing. Even the concept of identity is blurred by the common usage of pseudonyms.  In answer to this problem, many websites on the Internet have developed reputation systems that allow members to leave feedback about the performance of others in the execution of their duties. This accumulation of feedback about any individual can be used to characterise and predict their future behaviour in that context, allowing others to decide if they want to interact with that individual. Unfortunately, the information in each instance is limited to the narrow context of the website in which it was generated.  Not only is the reputation information constrained in context, it also limits the potential scope of what can be determined about an individual. The information that could be collected about entities includes social, demographic and reputation-based information. These are collectively called recommendation information in this thesis. Collecting this recommendation information from multiple sources and contexts should provide a wider view by which an entity can be evaluated than reputation alone could produce. The combination of these multiple sources of recommendation information can be naturally extended in the development of novel applications in areas such as access control and web service composition.  The GRAFT framework developed in this thesis encapsulates a paradigm shift in the way that reputation information is handled. It directly supports the collection and distribution goals by building a global distributed recommendation system that can be used to collect and make available recommendation information about both people and electronic services. This system can be used as both a drop-in replacement for existing systems, or it can be used to drive the consumption of recommendation information in novel new systems.  Recommendation information can be collected from both traditional reputation sources such as Amazon and eBay, and non-traditional reputation sources such as social networks, providing flexibility in what can be collected and subsequently utilised by consumers. The derivation of reputation information from non-reputation sources including demographic and social information, and the subsequent ability to use this recommendation information in the description and evaluation of policies is unique to GRAFT.  The major contributions of this thesis in the areas of reputation and reputation systems include the development of a reputation terminology, generalised models of reputation and reputation context, an extensive survey and taxonomy of reputation systems and a classification of existing reputation systems based on the taxonomy. This thesis also contributes an architecture for GRAFT, a prototype implementation of GRAFT showing its usefulness, and an evaluation that includes the results of a large number of simulation experiments showing how the architecture scales and handles both malicious peers and churn.</p>

<p>Since the earliest human communities, reputation has been used by people to decide whether they should trust and interact with someone else. Traditionally, reputation was established through a person’s standing, word of mouth and their associations. However, with the increasingly widespread use of the Internet, this situation has changed. In particular, all of the normal cues that help to build reputation are missing. Even the concept of identity is blurred by the common usage of pseudonyms.  In answer to this problem, many websites on the Internet have developed reputation systems that allow members to leave feedback about the performance of others in the execution of their duties. This accumulation of feedback about any individual can be used to characterise and predict their future behaviour in that context, allowing others to decide if they want to interact with that individual. Unfortunately, the information in each instance is limited to the narrow context of the website in which it was generated.  Not only is the reputation information constrained in context, it also limits the potential scope of what can be determined about an individual. The information that could be collected about entities includes social, demographic and reputation-based information. These are collectively called recommendation information in this thesis. Collecting this recommendation information from multiple sources and contexts should provide a wider view by which an entity can be evaluated than reputation alone could produce. The combination of these multiple sources of recommendation information can be naturally extended in the development of novel applications in areas such as access control and web service composition.  The GRAFT framework developed in this thesis encapsulates a paradigm shift in the way that reputation information is handled. It directly supports the collection and distribution goals by building a global distributed recommendation system that can be used to collect and make available recommendation information about both people and electronic services. This system can be used as both a drop-in replacement for existing systems, or it can be used to drive the consumption of recommendation information in novel new systems.  Recommendation information can be collected from both traditional reputation sources such as Amazon and eBay, and non-traditional reputation sources such as social networks, providing flexibility in what can be collected and subsequently utilised by consumers. The derivation of reputation information from non-reputation sources including demographic and social information, and the subsequent ability to use this recommendation information in the description and evaluation of policies is unique to GRAFT.  The major contributions of this thesis in the areas of reputation and reputation systems include the development of a reputation terminology, generalised models of reputation and reputation context, an extensive survey and taxonomy of reputation systems and a classification of existing reputation systems based on the taxonomy. This thesis also contributes an architecture for GRAFT, a prototype implementation of GRAFT showing its usefulness, and an evaluation that includes the results of a large number of simulation experiments showing how the architecture scales and handles both malicious peers and churn.</p>

<p>Reputation is an opinion held by others about a particular person, group, organisation, or resource. As a tool, reputation can be used to forecast the reliability of others based on their previous actions, moreover, in some domains it can even be used to estimate trustworthiness. Due to the large scale of virtual communities it is impossible to maintain a meaningful relationship with every member. Reputation systems are designed explicitly to manufacture trust within a virtual community by recording and sharing information regarding past interactions. Reputation systems are becoming increasingly popular and widespread, with the information generated varying considerably between domains. Currently, no formal method to exchange reputation information exists. However, the OpenRep framework, currently under development, is designed to federate reputation information, enabling the transparent exchange of information between reputation systems. This thesis presents a reputation description and interpretation system, designed as a foundation for the OpenRep framework. The description and interpretation system focuses on enabling the consistent and reliable expression and interpretation of reputation information across heterogeneous reputation systems. The description and interpretation system includes a strongly typed language, a verification system to validate usage of the language, and a XML based exchange protocol. In addition to these contributions, three case studies are presented as a means of generating requirements for the description and interpretation system, and evaluating the use of the proposed system in a federated reputation environment. The case studies include an electronic auction, virtual community and social network based relationship management service.</p>

<p>Reputation is an opinion held by others about a particular person, group, organisation, or resource. As a tool, reputation can be used to forecast the reliability of others based on their previous actions, moreover, in some domains it can even be used to estimate trustworthiness. Due to the large scale of virtual communities it is impossible to maintain a meaningful relationship with every member. Reputation systems are designed explicitly to manufacture trust within a virtual community by recording and sharing information regarding past interactions. Reputation systems are becoming increasingly popular and widespread, with the information generated varying considerably between domains. Currently, no formal method to exchange reputation information exists. However, the OpenRep framework, currently under development, is designed to federate reputation information, enabling the transparent exchange of information between reputation systems. This thesis presents a reputation description and interpretation system, designed as a foundation for the OpenRep framework. The description and interpretation system focuses on enabling the consistent and reliable expression and interpretation of reputation information across heterogeneous reputation systems. The description and interpretation system includes a strongly typed language, a verification system to validate usage of the language, and a XML based exchange protocol. In addition to these contributions, three case studies are presented as a means of generating requirements for the description and interpretation system, and evaluating the use of the proposed system in a federated reputation environment. The case studies include an electronic auction, virtual community and social network based relationship management service.</p>

Reputation systems are used by nearly every digital marketplace, but designs vary and the effects of these designs are not well understood. We use a large-scale experiment on Airbnb to study the causal effects of one particular design choice—the timing with which feedback by one user about another is revealed on the platform. Feedback was hidden until both parties submitted a review in the treatment group and was revealed immediately after submission in the control group. The treatment stimulated more reviewing in total. This is due to users’ curiosity about what their counterparty wrote and/or the desire to have feedback visible to other users. We also show that the treatment reduced retaliation and reciprocation in feedback and led to lower ratings as a result. The effects of the policy on feedback did not translate into reduced adverse selection on the platform.

Large-scale non-kin cooperation is a unique ingredient of human success. This type of cooperation is challenging to explain in a world of self-interested individuals. There is overwhelming empirical evidence from different disciplines that reputation and gossip promote cooperation in humans in different contexts. Despite decades of research, important details of reputation systems are still unclear. Our goal with this theme issue is to promote an interdisciplinary approach that allows us to explore and understand the evolution and maintenance of reputation systems with a special emphasis on gossip and honest signalling. The theme issue is organized around four main questions: What are the necessary conditions for reputation-based systems? What is the content and context of reputation systems? How can reputations promote cooperation? And, what is the role of gossip in maintaining reputation systems and thus cooperation? This article is part of the theme issue ‘The language of cooperation: reputation and honest signalling’.

Reputation is a fundamental feature of human sociality as it sustains cooperative relationships among unrelated individuals. Research from various disciplines provides insights on how individuals form impressions of others, condition their behaviours based on the reputation of their interacting partners and spread or learn such reputations. However, past research has often neglected the socio-ecological conditions that can shape reputation systems and their effect on cooperation. Here, we outline how social environments, cultural values and institutions come to play a crucial role in how people navigate reputation systems. Moreover, we illustrate how these socio-ecological dimensions affect the interdependence underlying social interactions (e.g. potential recipients of reputational benefits, degree of dependence) and the extent to which reputation systems promote cooperation. To do so, we review the interdisciplinary literature that illustrates how reputation systems are shaped by the variation of prominent ecological features. Finally, we discuss the implications of a socio-ecological approach to the study of reputation and outline potential avenues for future research. This article is part of the theme issue ‘The language of cooperation: reputation and honest signalling’.