Peer-to-peer (P2P) online communities are commonly perceived as an environment offering both opportunities and threats. One way to

Traditional centralised approaches to security are difficult to apply to large, distributed marketplaces in which software agents operate. Developing a notion of trust that is based on the reputation of agents can provide a softer notion of security that is sufficient for many multi-agent applications. In this paper, we address the issue of incentivecompatibility (i.e. how to make it optimal for agents to share reputation information truthfully), by introducing a sidepayment scheme, organised through a set of broker agents, that makes it rational for software agents to truthfully share the reputation information they have acquired in their past experience. We also show how to use a cryptographic mechanism to protect the integrity of reputation information and to achieve a tight bounding between the identity and reputation of an agent. 1.

in online auction settings with noisy monitoring of quality and adverse selection. The mechanism combines the ability of electronic markets to solicit feedback from buyers with the more traditional ability to levy listing fees from sellers. Each period the mechanism charges a listing fee contingent on a seller's announced expected quality. It subsequently pays the seller a reward contingent on both his announced quality and the rating posted for that seller by that period's winning bidder. I show that, in the presence of a continuum of seller types with di#erent cost functions, imperfect private monitoring of a seller's e#ort level and a simple "binary" feedback mechanism that asks buyers to rate a transaction as "good" or "bad", it is possible to derive a schedule of fees and rewards that induces all seller types to produce at their respective first-best quality levels and to truthfully announce their intended quality levels to buyers. The mechanism maximizes average social welfare for the entire community and is robust to a number of contingencies of particular concern in online environments, such as easy name changes and the existence of inept sellers. On the other hand, the mechanism distorts the resulting payo#s of transferring part of the payo#s of more e#cient sellers to less e#cient sellers. The magnitude of this distortion is proportional to the amount of noise associated with observing and reporting the quality of a good.

Online reputation mechanisms are emerging as a promising alternative to more established mechanisms for promoting trust and cooperative behavior, such as legally enforceable contracts. As information technology

This paper offers a systematic exploration of reputation mechanism design in trading environments with opportunistic sellers, imperfect monitoring of a seller's actions and two possible seller e#ort levels, one of which has no value to buyers. The objective of reputation mechanisms in such settings is to induce sellers to exert high e#ort as often as possible. I study the impact of various mechanism parameters (such as the granularity of solicited feedback, the format of the public reputation profile, the policy regarding missing feedback, and the rules for admitting new sellers) on the resulting market e#ciency. I find that the maximum e#ciency that is attainable through reputation mechanisms is bounded away from the hypothetical first-best case where sellers could credibly pre-commit to full cooperation by a factor that is related to the probability that cooperating sellers may receive "unfair" bad ratings. Furthermore, the maximum e#ciency is independent of the length of past history summarized in a seller's public reputation profile. I apply my framework to a model of eBay's feedback mechanism and conclude that eBay's simple mechanism is capable of inducing the maximum theoretical e#ciency independently of the number of recent ratings that are being summarized in a seller's profile. I also derive optimal policies for dealing with missing feedback and easy online identity changes.

Abstract. Reputation mechanisms offer an efficient way of building the necessary level of trust in electronic markets. In the absence of independent verification authorities that can reveal the true outcome of a transaction, market designers have to ensure that it is in the best interest of the trading agents to report the behavior in transactions truthfully. As opposed to side-payment schemes that correlate a present report with future reports submitted about the same agent, we present a mechanism we have called “CONFESS”, that discovers (in equilibrium) the true outcome of a transaction by analyzing the two reports coming from the agents involved in the exchange. For two long-run rational agents, we show that it is possible to design such a mechanism that makes cooperation a stable equilibrium. 1

Reputation mechanisms offer an efficient way of building the necessary level of trust in electronic markets. Feedback about an agent’s past behavior can be aggregated into a measure of reputation, and used by other agents for taking trust decisions. Unfortunately, true feedback cannot be automatically assumed. In the absence of Trusted Third Parties, the mechanism has to make it rational for agents to truthfully share reputation information. In this paper we describe two mechanisms that can be used in decentralized environments for eliciting true feedback. The mechanisms are accompanied by examples inspired by real scenarios.

Reputation mechanisms o#er an e#cient way of building the necessary level of trust in electronic markets. In the absence of independent verification authorities that can reveal the true outcome of a transaction, market designers have to insure that it is in the best interest of the trading agents to report the behavior in transactions truthfully. As opposed to side-payment schemes that correlate a present report with future reports submitted about the same agent, we present a mechanism that discovers (in equilibrium) the true outcome of a transaction by analyzing the two reports coming from the agents involved in the exchange. For two long-run rational agents, we show that it is possible to design such a mechanism that makes cooperation a stable equilibrium.

Reputation mechanisms provide a promising alternative to the traditional security methods for preventing malicious behavior in online transactions. However, obtaining correct reputation information is not trivial. In the absence of objective authorities (or trusted third parties) which can oversee every transaction, mechanism designers have to ensure that it is rational for the participating parties to report the truth. In this paper we describe a complete reputation mechanism for the online hotel booking industry that is efficient (i.e. the equilibrium behavior is cooperative) and incentive compatible. Our mechanism discovers the true outcome of an interaction by analyzing the two reports coming from the agents involved in the interaction. Based on side payments, such a mechanism makes it profitable for longrun agents to commit to always report the truth. 1.

A reputation management system (RMS) is a system that helps users in an online community compute reputation of others. One important issue in this kind of systems is how to eliminate effect of unfair ratings. In this paper we make a survey of how people have solved this problem so far. Beside briefly explaining the solutions, we discuss about pros and cons of every solution and the future research direction on this issue.