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.

We consider schemes for obtaining truthful reports on a common but hidden signal from large groups of rational, self-interested agents. One example are online feedback mechanisms, where users provide observations about the quality of a product or service so that other users can have an accurate idea of what quality they can expect. However, (i) providing such feedback is costly, and (ii) there are many motivations for providing incorrect feedback. Both problems can be addressed by reward schemes which (i) cover the cost of obtaining and reporting feedback, and (ii) maximize the expected reward of a rational agent who reports truthfully. We address the design of such incentive-compatible rewards for feedback generated in environments with pure adverse selection. Here, the correlation between the true knowledge of an agent and her beliefs regarding the likelihoods of reports of other agents can be exploited to make honest reporting a Nash equilibrium. In this paper we extend existing methods for designing incentive-compatible rewards by also considering collusion. We analyze different scenarios, where, for example, some or all of the agents collude. For each scenario we investigate whether a collusion-resistant, incentive-compatible reward scheme exists, and use automated mechanism design to specify an algorithm for deriving an efficient reward mechanism

Crowdsourcing is widely proposed as a method to solve a large variety of judgment tasks, such as classifying website content, peer grading in online courses, or collecting real-world data. As the data reported by workers cannot be verified, there is a tendency to report random data without actually solving the task. This can be countered by making the reward for an answer depend on its consistency with answers given by other workers, an approach called peer consistency. However, it is obvious that the best strategy in such schemes is for all workers to report the same answer without solving the task. Dasgupta and Ghosh [2013] show that, in some cases, exerting high effort can be encouraged in the highest-paying equilibrium. In this article, we present a general mechanism that implements this idea and is applicable to most crowdsourcing settings. Furthermore, we experimentally test the novel mechanism, and validate its theoretical properties.

Online reputation mechanisms need honest feedback to function effectively. Self interested agents report the truth only when explicit rewards offset the cost of reporting and the potential gains that can be obtained from lying. Side-payment schemes (monetary rewards for submitted feedback) can make truth-telling rational based on the correlation between the reports of different buyers.In this paper we use the idea of automated mechanism design to construct the payments that minimize the budget required by an incentive-compatible reputation mechanism. Such payment schemes are defined by a linear optimization problem that can be solved efficiently in realistic settings. Furthermore, we investigate two directions for further lowering the cost of incentive-compatibility: using several reference reports to construct the side-payments, and filtering out reports that are probably false.

Service-level agreements (SLAs) establish a contract between service providers and clients concerning Quality of Service (QoS) parameters. Without proper penalties, service providers have strong incentives to deviate from the advertised QoS, causing losses to the clients. Reliable QoS monitoring (and proper penalties computed on the basis of delivered QoS) are therefore essential for the trustworthiness of a service-oriented environment. In this paper, we present a novel QoS monitoring mechanism based on quality ratings from the clients. A reputation mechanism collects the ratings and computes the actual quality delivered to the clients. The mechanism provides incentives for the clients to report honestly, and pays special attention to minimizing cost and overhead.