Asymptotically idempotent aggregation operators for trust management in multi-agent systems

The study of trust management in multi-agent system, especially distributed, has grown over the last years. Trust is a complex subject that has no general consensus in literature, but has emerged the importance of reasoning about it computationally. Reputation systems takes into consideration the history of an entity’s actions/behavior in order to compute trust, collecting and aggregating ratings from members in a community. In this scenario the aggregation problem becomes fundamental, in particular depending on the environment. In this paper we describe a technique based on a class of asymptotically idempotent aggregation operators, suitable particulary for distributed anonymous environments

Credit Risk Evaluation in Peer-to-peer Lending With Linguistic Data Transformation and Supervised Learning

The widespread availability of various peer-to-peer lending solutions is rapidly changing the landscape of ï¬nancial services. Beside the natural advantages over traditional services,a relevant problem in the domain is to correctly assess the risk associated with borrowers. In contrast to traditional ï¬nancial services industries, in peer-to-peer lending the unsecured nature of loans as well as the relative novelty of the platforms make the assessment of risk a difï¬cult problem. In this article we propose to use traditional machine learning methods enhanced with fuzzy set theory based transformation of data to improve the quality of identifying loans with high likelihood of default. We assess the proposed approach on a real-life dataset from one of the largest peer-to-peer platforms in Europe. The results demonstrate that (i) traditional classiï¬cation algorithms show good performance in classifying borrowers, and (ii) their performance can be improved using linguistic data transformatio

A review on trust propagation and opinion dynamics in social networks and group decision making frameworks

On-line platforms foster the communication capabilities of the Internet to develop large- scale influence networks in which the quality of the interactions can be evaluated based on trust and reputation. So far, this technology is well known for building trust and harness- ing cooperation in on-line marketplaces, such as Amazon (www.amazon.com) and eBay (www.ebay.es). However, these mechanisms are poised to have a broader impact on a wide range of scenarios, from large scale decision making procedures, such as the ones implied in e-democracy, to trust based recommendations on e-health context or influence and per- formance assessment in e-marketing and e-learning systems. This contribution surveys the progress in understanding the new possibilities and challenges that trust and reputation systems pose. To do so, it discusses trust, reputation and influence which are important measures in networked based communication mechanisms to support the worthiness of information, products, services opinions and recommendations. The existent mechanisms to estimate and propagate trust and reputation, in distributed networked scenarios, and how these measures can be integrated in decision making to reach consensus among the agents are analysed. Furthermore, it also provides an overview of the relevant work in opinion dynamics and influence assessment, as part of social networks. Finally, it identi- fies challenges and research opportunities on how the so called trust based network can be leveraged as an influence measure to foster decision making processes and recommen- dation mechanisms in complex social networks scenarios with uncertain knowledge, like the mentioned in e-health and e-marketing frameworks.The authors acknowledge the financial support from the EU project H2020-MSCA-IF-2016-DeciTrustNET-746398, FEDER funds provided in the National Spanish project TIN2016-75850-P , and the support of the RUDN University Program 5-100 (Russian Federation)