Jochen Haller

Bio: Jochen Haller is an academic researcher. The author has contributed to research in topics: Decision support system & Reputation system. The author has an hindex of 2, co-authored 2 publications receiving 16 citations.

A Bayesian Reputation System for Virtual Organizations

Abstract: Virtual Organizations (VOs) are an emerging business model in today’s Internet economy. Increased specialization and focusing on an organization’s core competencies requires such novel models to address business opportunities. In a VO, a set of sovereign, geographically dispersed organizations temporarily pool their resources to jointly address a business opportunity. The decision making process determining which potential partners are invited to join the VO is crucial with respect to entire VO’s success. The possibility of a VO partner performing badly during the VO’s operational phase or announcing bankruptcy endangers the investment taken in integrating their processes and infrastructure for the purpose of the VO. A reputation system can provide additional decision support besides the a priori knowledge from quotations and bidding to avoid events such as VO partner replacement by helping to choose reliable partners in the first place. To achieve this, reputation, an objective trust measure, is optimally aggregated from multiple independent trust sources that inherently characterize an organization’s reliability. To allow for the desired predictions of an organization’s future performance, a stochastic modeling approach is chosen. The paper will present a taxonomy of TIs for VO environments, a stochastic model to maintain and aggregate trust sources, so called Trust Indicators, and the inclusion of other subjective measures such as feedback.

A Bayesian Reputation System for Virtual Organizations

Abstract: Virtual Organizations (VOs) are an emerging business model in todayA¢â‚¬â„¢s Internet economy. Increased specialization and focusing on an organizationA¢â‚¬â„¢s core competencies requires such novel models to address business opportunities. In a VO, a set of sovereign, geographically dispersed organizations temporarily pool their resources to jointly address a business opportunity. A VO follows a phased lifecycle where speed is an essential requirement, especially in the initial identification and formation phases that deal with potential VO partner identification and selection. For instance a business opportunity in the form of a government issued tender in the collaborative engineering application domain to upgrade a passenger plane needs to be swiftly answered within a defined deadline. VO management tasks such as VO formation, partner selection and dismissal are duties of the VO manager role, in collaborative engineering typically performed by a systemA¢â‚¬â„¢s integrator. The VO managerA¢â‚¬â„¢s final decision making which potential partners are invited to join the VO is crucial with respect to entire VOA¢â‚¬â„¢s success. The possibility of a VO partner performing badly during the VOA¢â‚¬â„¢s operational phase or announcing bankruptcy endangers the investment taken in integrating their processes and infrastructure for the purpose of the VO. A reputation system can provide additional decision support besides the a priori knowledge from quotations and bidding to avoid events such as VO partner replacement by helping to choose reliable partners in the first place. To achieve this, reputation, an objective trust measure, should be aggregated from multiple independent trust sources, inherently characterizing an organizationA¢â‚¬â„¢s reliability, so-called Trust Indicators (TIs). Such TIs can provide measurable trust values about an organization from heterogeneous domains, e.g. timely delivery of service, organizational stability or environmental risks due to building locations. To allow for the desired predictions of an organizationA¢â‚¬â„¢s future performance, a stochastic modeling approach is chosen. The talk will present: 1) a taxonomy of TIs for VO environments 2) a stochastic model to maintain TIs and their aggregation using Bayes networks 3) the inclusion of other subjective measures such as feedback

An approach to assess collaboration readiness

Abstract: The level of readiness of an organisation to join a collaborative process depends on ‘hard’ factors such as competency fitness or technological compatibility, but also on several other factors of a ‘soft’ nature such as an organisation's character, willingness to collaborate, or affectivity/empathy relationships. Considering these aspects, a modelling approach to assess how prepared an enterprise is to join a collaborative network is proposed. The approach is based on the notion of ‘character’ of the organisation, addressing behavioural aspects regarding collaboration. The distinction between collaboration readiness and preparedness is established. In order to deal with the incompleteness of information and uncertainties associated to the readiness assessment process, it is proposed to use belief networks, which allow performing inference concerning behavioural characteristics of organisations. The approach is then extended in order to handle decision making under situations characterised by uncertainty. ...

Computing Confidence Values: Does Trust Dynamics Matter?

Abstract: Computational Trust and Reputation (CTR) systems are platforms capable of collecting trust information about candidate partners and of computing confidence scores for each one of these partners. These systems start to be viewed as vital elements in environments of electronic institutions, as they support fundamental decision making processes, such as the selection of business partners and the automatic and adaptive creation of contractual terms and associated enforcement methodologies. In this article, we propose a model for the aggregation of trust evidences that computes confidence scores taking into account dynamic properties of trust. We compare our model with a traditional statistical model that uses weighted means to compute trust, and show experimental results that show that in certain scenarios the consideration of the trust dynamics allows for a better estimation of confidence scores.

Negotiation, Auctions, and Market Engineering

Abstract: This book contains a selection of papers presented at the International Seminar "Negotiation and Market Engineering", held at Dagstuhl Castle, Germany, in November 2006. The 17 revised full papers presented in this volume were carefully selected and reviewed after the seminar. The papers deal with the complexity of negotiations, auctions, and markets as economic, social, and IT systems. The authors give a broad overview on the major issues to be addressed and the methodologies used to approach them, covering highly interdisciplinary research from computer science, economics, business administration, and mathematics.

Engaging the dynamics of trust in computational trust and reputation systems

Abstract: Computational Trust and Reputation (CTR) systems are essential in electronic commerce to encourage interactions and suppress deceptive behaviours. This paper focus on comparing two different kinds of approaches to evaluate the trustworthiness of suppliers. One is based on calculating the weighted mean of past results. The second one applies basic properties of the dynamics of trust. Different scenarios are investigated, including a more problematic one that results from introducing newcomers during the simulation. Experimental results presented in this paper prove the benefits of engaging properties of the dynamics of trust in CRT systems, as it noticeably improves the process of business partners' selection and increases the utility.

Privacy Preserving Reputation Systems for Decentralized Environments

Abstract: It has been observed that users in a reputation system often hesitate in providing negative feedback due to the fear of retaliation. A solution to this issue is privacy preserving reputation systems, which compute reputation such that the individual feedback of any user is not revealed. In this thesis, we present privacy preserving reputation protocols, that are decentralized, do not require specialized platforms nor trusted third parties, protect privacy under a range of adversarial models (semi-honest, non-disruptive malicious, disruptive malicious), and are more efficient than comparable protocols (the most expensive protocol requires O(n) + O(log N) messages, where n and N are the number of feedback providers and the total number of users respectively). The techniques that we utilize include trust awareness, data perturbation, secret sharing, secure multi-party computation, additive homomorphic cryptosystems, and zero-knowledge proofs. We also address some issues related to trust recommendation and propagation. In particular, we present a solution to the problem of subjectivity in trust recommendation. Experimental results indicate the effectiveness of the proposed strategies.