Thanasis G. Papaioannou

Effective use of reputation in peer-to-peer environments

Peer-to-peer environments have become popular as a framework for exchange of services. In these environments, certain peers may fail to provide. their services.. Reputation can be a proper means of discovering low-performing peers, without affecting significantly inherent characteristics of peer-to-peer environments, such as anonymity and privacy. However, the accurate calculation of the reputation metrics may not be sufficient to provide the right incentives to peers. In this paper, we show that the straightforward approach for peers to exploit the reputation metrics (i.e. by just selecting as a providing peer the one with the highest reputation) may lead to unexpectedly low efficiency for high-performing peers. We argue and justify experimentally that the calculation of the reputation values has to be complemented by reputation-based policies that define the pairs of peers eligible to interact. We introduce two orthogonal dimensions constituting the reputation based policies: provider selection and contention resolution. We argue and show by means of simulation experiments that both these dimensions have a significant impact to the achieved efficiency of the peers. We also investigate experimentally the achievable efficiency of specific reputation-based policies for the case of short-lived peers of two different fixed-strategy types. Finally, we deal with the efficient computation of the reputation value by means of aggregation of the ratings feedback provided by the peers. We propose that this can be accomplished by aggregating only a small randomly selected subset of this feedback. Simulation experiments indicate that this approach indeed leads to the fast and accurate calculation of the reputation values even if the peer-to-peer population is renewed with a high rate.

Achieving Honest Ratings with Reputation-Based Fines in Electronic Markets

The effectiveness of online feedback mechanisms for rating the performance of providers in electronic markets is vulnerable to the submission of dishonest ratings. In this paper, we deal with how to elicit honest such ratings in a competitive electronic market where each participant can occasionally act both as provider and as client. We assume that each service provision is rated by both parties involved; only upon agreement, this rating is included in the calculation of reputation for the providers performance. We first study as a single-shot game the effectiveness of inducing, upon evidence of lying (i.e. disagreement of the submitted feedback), fixed fines to both transacted parties, yet different ones for the provider and the client. We prove that the submission of honest feedback can be a stable equilibrium for the whole market under certain initial system conditions. Then, we refine our game-model for repeated transactions and calculate proper different reputation-based fines for lying. These fines enable the submission of honest feedback as a stable Nash equilibrium of the repeated game and reduce the social losses due to unfair punishments. Finally, we argue that our model is appropriate for analyzing actual electronic markets, and we investigate the impact of employing our approach to the feedback schemes of such markets.

A mechanism that provides incentives for truthful feedback in peer-to-peer systems

We propose a mechanism for providing the incentives for reporting truthful feedback in a peer-to-peer system for exchanging services (or content). This mechanism is to complement reputation mechanisms that employ ratings’ feedback on the various transactions in order to provide incentives to peers for offering better services to others. Under our approach, each of the transacting peers (rather than just the client) submits a rating on the performance of their mutual transaction. If these are in disagreement, then both transacting peers are punished, since such an occasion is a sign that one of them is lying. The severity of each peer’s punishment is determined by his corresponding non-credibility metric; this is maintained by the mechanism and evolves according to the peer’s record. When under punishment, a peer does not transact with others. We model the punishment effect of the mechanism in a peer-to-peer system as a Markov chain that is experimentally proved to be very accurate. According to this model, the credibility mechanism leads the peer-to-peer system to a desirable steady state isolating liars. Then, we define a procedure for the optimization of the punishment parameters of the mechanism for peer-to-peer systems of various characteristics. We experimentally prove that this optimization procedure is effective and necessary for the successful employment of the mechanism in real peer-to-peer systems. Then, the optimized credibility mechanism is combined with reputation-based policies to provide a complete solution for high performance and truthful rating in peer-to-peer systems. The combined mechanism was experimentally proved to deal very effectively with large fractions of collaborated liar peers that follow static or dynamic rational lying strategies in peer-to-peer systems with dynamically renewed population, while the efficiency loss induced to sincere peers by the presence of liars is diminished. Finally, we describe the potential implementation of the mechanism in real peer-to-peer systems.

Reputation-based estimation of individual performance in collaborative and competitive grids

Hidden information is a critical issue for the successful delivery of services in grid systems. It arises when the agents (hardware and software resources) employed to serve a task belong to multiple administrative domains, thus rendering monitoring of remote resource provision absent or unreliable. Therefore, the grid service broker can often observe only the outcome of the collective effort of groups of agents rather than their individual efforts, which makes it hard to identify cases of free-riding or low-performing agents. In this paper, we first identify cases of hidden information in grid systems and explain why they cannot be handled satisfactorily by the existing accounting systems. Second, we develop and evaluate a reputation-based mechanism enabling the grid service broker to deal effectively with hidden information. Our mechanism maintains a reputation metric for each agent; we propose and evaluate several approaches on how to update this metric based only on the observations of collective outcomes. We also provide recommendation on which such an approach is preferable for a grid service broker in collaborative or competitive environments.

Optimizing an incentives' mechanism for truthful feedback in virtual communities

We analyze a mechanism that provides strong incentives for the submission of truthful feedback in virtual communities where services are exchanged on a peer-to-peer basis. Lying peers are punished with a severity that is exponential to their frequency of lying. We had first introduced and evaluated experimentally the mechanism in [1]. In this paper, we develop a Markov-chain model of the mechanism. Based on this, we prove that, when the mechanism is employed, the system evolves to a beneficial steady-state operation even in the case of a dynamically renewed population. Furthermore, we develop a procedure for the efficient selection of the parameters of the mechanism for any peer:| to-peer system; this procedure is based on ergodic arguments. Simulation experiments reveal that the procedure is indeed accurate, as well as effective regarding the incentives provided to participants for submitting truthful feedback.

Reputation-Based Estimation of Individual Performance in Grids

Hidden information is a critical issue for the successful delivery of SLAs in grid systems. It arises when the agents (hardware and software resources) employed to serve a task belong to multiple administrative domains, thus rendering monitoring of remote resource provision absent or unreliable. Therefore, the grid service broker can often observe only the outcome of the collective effort of groups of agents rather than their individual efforts, which makes it hard to identify cases of free-riding or low- performing agents. In this paper, we first identify cases of hidden information in grid systems and explain why they cannot be handled satisfactorily by the existing accounting systems. Second, we develop and evaluate a reputation-based mechanism enabling the grid service broker to deal effectively with hidden information. Our mechanism maintains a reputation metric for each agent; we propose and evaluate several approaches on how to update this metric based only on the observations of collective outcomes.

Enforcing Truthful-Rating Equilibria in Electronic Marketplaces

Reputation-based mechanisms and policies are vulnerable to the submission of untruthful ratings. In this paper, we define and analyze a game-theoretic model that captures the dynamics and the rational incentives in a competitive e-marketplace in which providers and clients exchange roles. We also study how we can enforce equilibria where ratings are submitted truthfully. We employ a mechanism prescribing that each service provision is rated by both the provider and the client, while this rating is included in the calculation of reputation only in case of agreement. First, we analyze the case where fixed monetary penalties are induced to both raters in case of disagreement. We prove that, under certain assumptions on the initial conditions, the system is led to a stable equilibrium where all participants report truthfully their ratings. We also investigate the introduction of non-fixed penalties to provide the right incentives for truthful reporting. We derive lower bounds on such penalties that depend on the participant’s reputation values. Thus, by employing a punishment that is tailored properly for each participant, this approach can limit the unavoidable social welfare losses due to the penalties for disagreement.

IoT-enabled gamification for energy conservation in public buildings

Public buildings are a particularly challenging category of buildings to address for energy conservation due to lack of occupant motives and lack of individual accountability among others. In this paper, we describe an IoT-enabled gamification approach to change energy-consumption behaviors and reduce energy waste in public buildings. This will be assisted by the energy-consumption disaggregation at the device and at the individual employee levels through the design of an IoT infrastructure consisting of multi-channel smart meters, NFC tags, BLE beacons and the user smartphones. Based on our approach, employees will be motivated to improve their energy-consumption behaviors by means of peer-pressure based on a team competition and by means of direct (non-monetary) rewards.

Optimal design of serious games for demand side management

Serious games are a promising approach for demand-side management that aims to higher user engagement and active participation. In this paper1, we introduce the problem of optimal serious-game design for achieving specific energy-consumption reduction goals. We consider a serious game, where a game designer entity presents publicly to all consumers a list of top-K consumers and a list of bottom-M consumers according to their respective energy-consumption reduction at peak hours. The driving forces of this game are the user discomfort due to demand load shifting, the user desire for social approval and the user sensitivity to social outcasting. According to their private values to these parameters, users compete to enter the top-K list and be recognized for their achievement, or to avoid ending up in the bottom-M list and become pinpointed for not being energy-friendly. We formulate the problems of the game designer as an operational-cost minimization one for the utility company and that of each consumer as a utility-maximization one. The game-design problem is to decide on K, M and on the feedback provided to the consumers, while the consumer-side problem amounts to selecting the behavioral change to energy consumption that maximizes the expected user utility. By a series of simulations, we show how the choices of K, M affect the energy consumption reduction for different types of customers.

Modeling and Optimization of the Smart Grid Ecosystem

The aim of the smart electric energy grid is to improve efficiency, flexibility, and stability of the electric energy generation and distribution system, with the ultimate goal being the added value of energy-related services to the end-consumer and to facilitate energy generation and prudent consumption toward energy efficiency. New technologies, such as networks and sensors, are combined with consumer behaviour to create a complex eco-system in which many factors interact. Modeling and Optimization of the Smart Grid Ecosystem gives some structure to the complex ecosystem and surveys key research problems that have shaped the area. The emphasis is on the presentation of the control and optimization methodology used in approaching each of these problems. This methodology spans convex and linear optimization theory, game theory, and stochastic optimization. Modeling and Optimization of the Smart Grid Ecosystem serves as a reference for researchers wishing to understand the fundamental principles and research problems underpinning the smart grid ecosystem, and the main mathematical tools used to model and analyze such systems.