A metrology-based approach for measuring the social dimension of cognitive trust in collaborative networks

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10111-018-0483-1[Abstract]: This paper addresses the measurement of the social dimension of cognitive trust in collaborative networks. Trust indicators are typically measured and combined in literature in order to calculate partners’ trustworthiness. When expressing the result of a measurement, some quantitative indication of the quality of the result—the uncertainty of measurement—should be given. However, currently this is not taken into account for the measurement of the social dimension of cognitive trust in collaborative networks. In view of this, an innovative metrology-based approach for the measurement of social cognitive trust indicators in collaborative networks is presented. Thus, a measurement result is always accompanied by its uncertainty of measurement, as well as by information traditionally used to properly interpret the results: the sample size, and the standard deviation of the sample

EDOC2011 PhD Student Symposium Proceedings

Post-proceedings of the EDOC2011 PhD Student Symposium held in Helsinki 26.8.2011.Peer reviewe

Novos olhares para os cenários e práticas da educação digital

“Novos olhares para os cenários e práticas da educação digital” dá continuidade à linha editorial da coleção Educação a Distância e eLearning da Editora Universidade Aberta. Este segundo volume desta linha editorial concretiza a vontade da Universidade Aberta em partilhar, através de recursos abertos, tanto resultados de investigação como reflexões sobre as interconexões das diferentes vertentes científicas, pedagógicas e sociais, que emergem nos cenários de inovação educativa. Este volume, que reúne sete capítulos, mantém o diálogo com todos os interessados nos ambientes de educação digital ao apresentar cenários pedagógicos, teóricos e de inovação que constituem a realidade multidimensional do Ensino a Distância e em Rede e que são objeto de estudos, de experiências e de projetos que decorrem atualmente na Universidade Aberta. O primeiro capítulo, da autoria de Sónia Sousa e Paulo Dias, apresenta resultados de um estudo sobre a confiança, entendida como meio para a valorização da sustentabilidade dos processos de interação entre os membros das redes de atores nos cenários emergentes de educação aberta e em rede. Darlinda Moreira e Filipa Seabra, no capítulo 2, analisam o conceito e as potencialidades da mobilidade virtual no contexto do ensino superior a distância online. O capitulo 3, cujos autores são M. M. Ishida, Ana Paula Martinho, Pedro Pereira, Lúcia Amante e Sandra Caeiro, compara dois modelos pedagógicos de cursos oferecidos na modalidade a distância na área das ciências aplicadas: o da Universidade Aberta e o da Universidade Federal de Santa Catarina. Paula Bacelar-Nicolau, Ulisses M. Azeiteiro e Pedro Pereira, no capítulo 4, relatam resultados de uma década de estudos realizados no âmbito do mestrado em Cidadania Ambiental e Participação da Universidade Aberta e discutem as predisposições atitudinais, as motivações e as barreiras identificadas na adoção do eLearning bem como progressos e iniciativas institucionais implementadas. No Capítulo 5, Susana Henriques, J. António Moreira, Daniela Melaré Vieira Barros e Fátima Goulão explanam a concepção e o modelo de formação seguido no Curso de Formação para a Docência Online, da Universidade Aberta. O sexto capítulo da autoria de Maria de Fátima Goulão e Rebeca Cerezo Menéndez, relata uma experiência realizada na Universidade Aberta e que decorreu previamente ao início das atividades académicas, onde os estudantes que frequentam pela primeira vez a universidade são conduzidos a desenvolver a metacognição como estratégia autorreguladora do processo de aprendizagem. Tiago Carrilho e José António Porfírio, encerram esta publicação com o sétimo capítulo que apresenta o projeto europeu ISOLearn, apoiado no âmbito do Programa ERASMUS+, cujo objetivo principal é contribuir para o acesso e melhoria da qualidade do Ensino Superior para pessoas com deficiência visual ou auditiva, promovendo a sua inclusão.info:eu-repo/semantics/publishedVersio

Trust and Distrust in Adaptive Inter-enterprise Collaboration Management

The success and competitive edge of enterprises has become increasingly dependent on the enterprises’ agility to become members in business networks that support their own business strategies. Therefore, integration solutions with their well-weathered strategic networks are no longer sufficient. Instead, there is need for more open business service ecosystems where previously unknown services and partnerships can be utilized. The ecosystem is to be supported with infrastructure services to solve the evident problems of semantic and pragmatic interoperability and collaboration-governing contract management. Furthermore, the ecosystem must support the creation of trust relationships with previously unknown partners, and reacting to encountered breaches of trust within collaborations. This paper proposes a trust management system where autonomous enterprises make automated, private trust decisions about their membership in each collaboration separately, while taking advantage of globally shared reputation of business peers in earlier collaborations. The trust decisions are adjustable to different and changing business situations

Trust assessment in the context of unrepresentative information

Trust and reputation algorithms are social methods, complementary to security protocols, that guide agents in multi-agent systems (MAS) in identifying trustworthy partners to communicate with. Agents need to interact to complete tasks, which requires delegating to an agent who has the time, resources or information to achieve it. Existing trust and reputation assessment methods can be accurate when they are learning from representative information, however, representative information rarely exists for all agents at all times. Improving trust mechanisms can benefit many open and distributed multi-agent applications. For example, distributing subtasks to trustworthy agents in pervasive computing or choosing who to share safe and high quality files with in a peer-to-peer network. Trust and reputation algorithms use the outcomes from past interaction experiences with agents to assess their behaviour. Stereotype models supplement trust and reputation methods when there is a lack of direct interaction experiences by inferring the target will behave the same as agents who are observably similar. These mechanisms can be effective in MAS where behaviours and agents do not change, or change in a simplistic way, for example, if agents changed their behaviour at the same rate. In real-world networks, agents experience fluctuations in their location, resources, knowledge, availability, time and priorities. Existing work does not account for the resulting dynamic dynamic populations and dynamic agent behaviours. Additionally, trust, reputation and stereotype models encourage repeat interactions with the same subset of agents which increase the uncertainty about the behaviour of the rest of the agent population. In the long term, having a biased view of the population hinders the discovery of new and better interaction partners. The diversity of agents and environments across MAS means that rigid approaches of maintaining and using data keep outdated information in some situations and not enough data in others. A logical improvement is for agents to manage information flexibly and adapt to their situation. In this thesis we present the following contributions. We propose a method to improve partner selection by making agents aware of a lack of diversity in their own knowledge and how to then make alternative behavioural assessments. We present methods for detecting dynamic behaviour in groups of agents, and give agents the statistical tools to decide which data are relevant. We introduce a data-free stereotype method to be used when there are no representative data for a data-driven behaviour assessment. Finally, we consider how agents can summarise agent behaviours to learn and exploit in depth behavioural patterns. The work presented in this thesis is evaluated in a synthetic environment designed to mimic characteristics of real-world networks and are comparable to evaluation environments from prominent trust and stereotype literature. The results show our work improves agents’ average reward from interactions by selecting better partners. We show that the efficacy of our work is most noticeable in environments where agents have sparse data, because it improve agents’ trust assessments under uncertainty