Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyse the current trends and developments in this area, and to propose a research agenda for trust and reputation systems.

Trust and reputation are central to effective interactions in open multi-agent systems in which agents, that are owned by a variety of stakeholders, can enter and leave the system at any time. This openness means existing trust and reputation models cannot readily be used. To this end, we present FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent's likely performance. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide a trust metric in most circumstances. FIRE is empirically benchmarked and is shown to help agents effectively select appropriate interaction partners.

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular,

Our trust conceptualizations have benefited from discussions with Ellen Berscheid and Larry Cummings of the University of Minnesota. The authors also thank three anonymous reviewers from the Organizational Behavior division of the 1996 meeting of the Academy of Management for their comments on an earlier version of this paper. THE MEANINGS OF TRUST What does the word ‘trust ’ mean? Scholars continue to express concern regarding their collective lack of consensus about trust’s meaning. Conceptual confusion on trust makes comparing one trust study to another problematic. To facilitate cumulative trust research, the authors propose two kinds of trust typologies: (a) a classification system for types of trust, and (b) definitions of six related trust types that form a model. Some of the model’s implications for management are also outlined. 2 THE MEANINGS OF TRUST “...trust is a term with many meanings. ” (Williamson, 1993: 453) “Trust is itself a term for a clustering of perceptions. ” (White, 1992: 174) Scholars and practitioners widely acknowledge trust's importance. Trust makes cooperative endeavors happen (e.g., Arrow, 1974; Deutsch, 1973; Gambetta, 1988). Trust is a key to positive interpersonal relationships in

ABSTRACT: Trust is a vital relationship concept that needs clarification because researchers across disciplines have defined it in so many different ways. A typology of trust types would make it easier to compare and communicate results, and would be especially valuable if the types of trust related to one other. The typology should be interdisciplinary because many disciplines research e-commerce. This paper justifies a parsimonious interdisciplinary typology and relates trust constructs to e-commerce consumer actions, defining both conceptual-level and operational-level trust constructs. Conceptual-level constructs consist of disposition to trust (primarily from psychology), institution-based trust (from sociology), and trusting beliefs and trusting intentions (primarily from social psychology). Each construct is decomposed into measurable subconstructs, and the typology shows how trust constructs relate to already existing Internet relationship constructs. The effects of Web vendor interventions on consumer behaviors are posited to be partially mediated by consumer trusting beliefs and trusting intentions in the e-vendor. KEY WORDS AND PHRASES: Customer relationships, human issues in e-commerce, Internet consumers, trust.

Abstract. In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open systems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent’s trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate. 1

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This research aims to develop a model of trust and reputation that will ensure good interactions amongst software agents in large scale open systems. The following are key drivers for our model: (1) agents may be self-interested and may provide false accounts of experiences with other agents if it is beneficial for them to do so; (2) agents will need to interact with other agents with which they have little or no past experience. Against this background, we have developed TRAVOS (Trust and Reputation model for Agentbased Virtual OrganisationS) which models an agent’s trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents. When there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate.

The article develops a process and control model for the analysis and design of inter-firm relations, in which both opportunism and trust play a role. Its aim is to develop a tool which helps to analyze combinations, balances and imbalances of trust and opportunism, formal and informal forms of governance, and viable sequences of strategies of governance, depending on different conditions. It employs both transaction cost economics and social exchange theory. 1

Cooperation is the fundamental underpinning of multi-agent systems, allowing agents to interact to achieve their goals. Where agents are self-interested, or potentially unreliable, there must be appropriate mechanisms to cope with the uncertainty that arises. In particular, agents must manage the risk associated with interacting with others who have different objectives, or who may fail to fulfil their commitments. Previous work has utilised the notions of motivation and trust in engendering successful cooperation between self-interested agents. Motivations provide a means for representing and reasoning about agents' overall objectives, and trust offers a mechanism for modelling and reasoning about reliability, honesty, veracity and so forth. This paper extends that work to address some of its limitations. In particular, we introduce the concept of a clan: a group of agents who trust each other and have similar objectives. Clan members treat each other favourably when making private decisions about cooperation, in order to gain mutual benefit. We describe mechanisms for agents to form, maintain, and dissolve clans in accordance with their self-interested nature, along with giving details of how clan membership influences individual decision making. Finally, through some simulation experiments we illustrate the effectiveness of clan formation in addressing some of the inherent problems with cooperation among self-interested agents.