Leveraging Trust and Privacy Concerns in Online Social Networks: an Empirical Study

Unprecedented success of Online Social Networks, such as Facebook, has been recently overshadowed by the privacy risks they imply. Weary of privacy concerns and unable to construct their identity in the desired way, users may restrict or even terminate their platform activities. Even though this means a considerable business risk for these platforms, so far there have been no studies on how to enable social network providers to address these problems. This study fills this gap by adopting a fairness perspective to analyze related measures at the disposal of the provider. In a Structural Equation Model with 237 subjects we find that ensuring interactional and procedural justice are two important strategies to support user participation on the platform.

Oxytocin shapes the neural circuitry of trust and trust adaptation in humans.

Trust and betrayal of trust are ubiquitous in human societies. Recent behavioral evidence shows that the neuropeptide oxytocin increases trust among humans, thus offering a unique chance of gaining a deeper understanding of the neural mechanisms underlying trust and the adaptation to breach of trust. We examined the neural circuitry of trusting behavior by combining the intranasal, double-blind, administration of oxytocin with fMRI. We find that subjects in the oxytocin group show no change in their trusting behavior after they learned that their trust had been breached several times while subjects receiving placebo decrease their trust. This difference in trust adaptation is associated with a specific reduction in activation in the amygdala, the midbrain regions, and the dorsal striatum in subjects receiving oxytocin, suggesting that neural systems mediating fear processing (amygdala and midbrain regions) and behavioral adaptations to feedback information (dorsal striatum) modulate oxytocin's effect on trust. These findings may help to develop deeper insights into mental disorders such as social phobia and autism, which are characterized by persistent fear or avoidance of social interactions.

Trust, Competition and Cooperation in Autism Spectrum Disorder

Objective: Impaired social interactions and repetitive behavior are key features of autism spectrum disorder (ASD). In the present study we compared social decision-making in subjects with and without ASD. Subjects performed five social decision-making games in order to assess trust, fairness, cooperation & competition behavior and social value orientation. Methods: 19 adults with autism spectrum disorder and 17 controls, matched for age and education, participated in the study. Each subject performed five social decision-making tasks. In the trust game, subjects could maximize their gain by sharing some of their money with another person. In the punishment game, subjects played two versions of the Dictator’s Dilemma. In the dictator condition they could share an amount of 0-100 points with another person. In the punishment condition, the opponent was able to punish the subject if he/she was not satisfied with the amount of points received. In the cooperation game, subjects played with a small group of 3 people. Each of them could (anonymously) select an amount of 5, 7.5 or 10 Swiss francs. The goal of the game was to achieve a high group minimum. In the competition game, subjects performed a dexterity task. Before performing the task, they were asked whether they wanted to compete (winner takes it all) or cooperation (sharing the joint achieved amount of points) with a randomly selected person. Lastly, subjects performed a social value orientation task where they were playing for themselves and for another person. Results: There was no overall difference between healthy controls an ASD subjects in investment in the trust game. However, healthy controls increased their investment over number of trials whereas ASD subjects did not. A similar pattern was found for the punishment game. Furthermore, ASD subjects revealed a decreased investment in the dictator condition of the punishment game. There were no mean differences in competition behavior and social value orientation. Conclusions: The results provide evidence for differences between ASD subjects and healthy controls in social decision-making. Subjects with ASD showed a more consistent behavior than healthy controls in the trust game and the dictator dilemma. The present findings provide evidence for impaired social learning in ASD.

Debate over Fair Value Accounting: Should we allow Politics to take an active role in setting accounting standards or should we trust that the SEC knows best?

In light of the recent economic downfall, there has been significant media coverage on the topic of fair value accounting. There are many critics of the accounting rule, who place blame on it for the destruction of billions of dollars in capital between financial institutions. Other commentators, however, see the rule as necessary and applaud its ability to bring the turmoil in the economy into the spotlight promptly so that it could be addressed effectively. This paper will begin by conducting a study of fair-value accounting from its inception in previous standards and then follow it through to Statement No. 157. I will then discuss the SEC’s most recent study of FAS157 and their decision as a result of the study.

TRHIOS: Trust and reputation in hierarchical and quality-oriented societies

In this paper we present TRHIOS: a Trust and Reputation system for HIerarchical and quality-Oriented Societies. We focus our work on hierarchical medical organizations. The model estimates the reputation of an individual, RTRHIOS, taking into account information from three trust dimensions: the hierarchy of the system; the source of information; and the quality of the results. Besides the concrete reputation value, it is important to know how reliable that value is; for each of the three dimensions we calculate the reliability of the assessed reputations; and aggregating them, the reliability of the reputation of an individual. The modular approach followed in the definition of the different types of reputations provides the system with a high flexibility that allows adapting the model to the peculiarities of each society.

Proposal of a trust and reputation model to provide recommendation in open quality oriented environments

El auge y penetración de las nuevas tecnologías junto con la llamada Web Social están cambiando la forma en la que accedemos a la medicina. Cada vez más pacientes y profesionales de la medicina están creando y consumiendo recursos digitales de contenido clínico a través de Internet, surgiendo el problema de cómo asegurar la fiabilidad de estos recursos. Además, un nuevo concepto está apareciendo, el de pervasive healthcare o sanidad ubicua, motivado por pacientes que demandan un acceso a los servicios sanitarios en todo momento y en todo lugar. Este nuevo escenario lleva aparejado un problema de confianza en los proveedores de servicios sanitarios. Las plataformas de eLearning se están erigiendo como paradigma de esta nueva Medicina 2.0 ya que proveen un servicio abierto a la vez que controlado/supervisado a recursos digitales, y facilitan las interacciones y consultas entre usuarios, suponiendo una buena aproximación para esta sanidad ubicua. En estos entornos los problemas de fiabilidad y confianza pueden ser solventados mediante la implementación de mecanismos de recomendación de recursos y personas de manera confiable. Tradicionalmente las plataformas de eLearning ya cuentan con mecanismos de recomendación, si bien están más enfocados a la recomendación de recursos. Para la recomendación de usuarios es necesario acudir a mecanismos más elaborados como son los sistemas de confianza y reputación (trust and reputation) En ambos casos, tanto la recomendación de recursos como el cálculo de la reputación de los usuarios se realiza teniendo en cuenta criterios principalmente subjetivos como son las opiniones de los usuarios. En esta tesis doctoral proponemos un nuevo modelo de confianza y reputación que combina evaluaciones automáticas de los recursos digitales en una plataforma de eLearning, con las opiniones vertidas por los usuarios como resultado de las interacciones con otros usuarios o después de consumir un recurso. El enfoque seguido presenta la novedad de la combinación de una parte objetiva con otra subjetiva, persiguiendo mitigar el efecto de posibles castigos subjetivos por parte de usuarios malintencionados, a la vez que enriquecer las evaluaciones objetivas con información adicional acerca de la capacidad pedagógica del recurso o de la persona. El resultado son recomendaciones siempre adaptadas a los requisitos de los usuarios, y de la máxima calidad tanto técnica como educativa. Esta nueva aproximación requiere una nueva herramienta para su validación in-silico, al no existir ninguna aplicación que permita la simulación de plataformas de eLearning con mecanismos de recomendación de recursos y personas, donde además los recursos sean evaluados objetivamente. Este trabajo de investigación propone pues una nueva herramienta, basada en el paradigma de programación orientada a agentes inteligentes para el modelado de comportamientos complejos de usuarios en plataformas de eLearning. Además, la herramienta permite también la simulación del funcionamiento de este tipo de entornos dedicados al intercambio de conocimiento. La evaluación del trabajo propuesto en este documento de tesis se ha realizado de manera iterativa a lo largo de diferentes escenarios en los que se ha situado al sistema frente a una amplia gama de comportamientos de usuarios. Se ha comparado el rendimiento del modelo de confianza y reputación propuesto frente a dos modos de recomendación tradicionales: a) utilizando sólo las opiniones subjetivas de los usuarios para el cálculo de la reputación y por extensión la recomendación; y b) teniendo en cuenta sólo la calidad objetiva del recurso sin hacer ningún cálculo de reputación. Los resultados obtenidos nos permiten afirmar que el modelo desarrollado mejora la recomendación ofrecida por las aproximaciones tradicionales, mostrando una mayor flexibilidad y capacidad de adaptación a diferentes situaciones. Además, el modelo propuesto es capaz de asegurar la recomendación de nuevos usuarios entrando al sistema frente a la nula recomendación para estos usuarios presentada por el modo de recomendación predominante en otras plataformas que basan la recomendación sólo en las opiniones de otros usuarios. Por último, el paradigma de agentes inteligentes ha probado su valía a la hora de modelar plataformas virtuales complejas orientadas al intercambio de conocimiento, especialmente a la hora de modelar y simular el comportamiento de los usuarios de estos entornos. La herramienta de simulación desarrollada ha permitido la evaluación del modelo de confianza y reputación propuesto en esta tesis en una amplia gama de situaciones diferentes. ABSTRACT Internet is changing everything, and this revolution is especially present in traditionally offline spaces such as medicine. In recent years health consumers and health service providers are actively creating and consuming Web contents stimulated by the emergence of the Social Web. Reliability stands out as the main concern when accessing the overwhelming amount of information available online. Along with this new way of accessing the medicine, new concepts like ubiquitous or pervasive healthcare are appearing. Trustworthiness assessment is gaining relevance: open health provisioning systems require mechanisms that help evaluating individuals’ reputation in pursuit of introducing safety to these open and dynamic environments. Technical Enhanced Learning (TEL) -commonly known as eLearning- platforms arise as a paradigm of this Medicine 2.0. They provide an open while controlled/supervised access to resources generated and shared by users, enhancing what it is being called informal learning. TEL systems also facilitate direct interactions amongst users for consultation, resulting in a good approach to ubiquitous healthcare. The aforementioned reliability and trustworthiness problems can be faced by the implementation of mechanisms for the trusted recommendation of both resources and healthcare services providers. Traditionally, eLearning platforms already integrate recommendation mechanisms, although this recommendations are basically focused on providing an ordered classifications of resources. For users’ recommendation, the implementation of trust and reputation systems appears as the best solution. Nevertheless, both approaches base the recommendation on the information from the subjective opinions of other users of the platform regarding the resources or the users. In this PhD work a novel approach is presented for the recommendation of both resources and users within open environments focused on knowledge exchange, as it is the case of TEL systems for ubiquitous healthcare. The proposed solution adds the objective evaluation of the resources to the traditional subjective personal opinions to estimate the reputation of the resources and of the users of the system. This combined measure, along with the reliability of that calculation, is used to provide trusted recommendations. The integration of opinions and evaluations, subjective and objective, allows the model to defend itself against misbehaviours. Furthermore, it also allows ‘colouring’ cold evaluation values by providing additional quality information such as the educational capacities of a digital resource in an eLearning system. As a result, the recommendations are always adapted to user requirements, and of the maximum technical and educational quality. To our knowledge, the combination of objective assessments and subjective opinions to provide recommendation has not been considered before in the literature. Therefore, for the evaluation of the trust and reputation model defined in this PhD thesis, a new simulation tool will be developed following the agent-oriented programming paradigm. The multi-agent approach allows an easy modelling of independent and proactive behaviours for the simulation of users of the system, conforming a faithful resemblance of real users of TEL platforms. For the evaluation of the proposed work, an iterative approach have been followed, testing the performance of the trust and reputation model while providing recommendation in a varied range of scenarios. A comparison with two traditional recommendation mechanisms was performed: a) using only users’ past opinions about a resource and/or other users; and b) not using any reputation assessment and providing the recommendation considering directly the objective quality of the resources. The results show that the developed model improves traditional approaches at providing recommendations in Technology Enhanced Learning (TEL) platforms, presenting a higher adaptability to different situations, whereas traditional approaches only have good results under favourable conditions. Furthermore the promotion period mechanism implemented successfully helps new users in the system to be recommended for direct interactions as well as the resources created by them. On the contrary OnlyOpinions fails completely and new users are never recommended, while traditional approaches only work partially. Finally, the agent-oriented programming (AOP) paradigm has proven its validity at modelling users’ behaviours in TEL platforms. Intelligent software agents’ characteristics matched the main requirements of the simulation tool. The proactivity, sociability and adaptability of the developed agents allowed reproducing real users’ actions and attitudes through the diverse situations defined in the evaluation framework. The result were independent users, accessing to different resources and communicating amongst them to fulfil their needs, basing these interactions on the recommendations provided by the reputation engine.

Trust and Reputation in Internet Auctions

Exchange between anonymous actors in Internet auctions corresponds to a one-shot prisoner's dilemma-like situation. Therefore, in any given auction the risk is high that seller and buyer will cheat and, as a consequence, that the market will collapse. However, mutual cooperation can be attained by the simple and very efficient institution of a public rating system. By this system, sellers have incentives to invest in reputation in order to enhance future chances of business. Using data from about 200 auctions of mobile phones we empirically explore the effects of the reputation system. In general, the analysis of nonobtrusive data from auctions may help to gain a deeper understanding of basic social processes of exchange, reputation, trust, and cooperation, and of the impact of institutions on the efficiency of markets. In this study we report empirical estimates of effects of reputation on characteristics of transactions such as the probability of a successful deal, the mode of payment, and the selling price (highest bid). In particular, we try to answer the question whether sellers receive a "premium" for reputation. Our results show that buyers are willing to pay higher prices for reputation in order to diminish the risk of exploitation. On the other hand, sellers protect themselves from cheating buyers by the choice of an appropriate payment mode. Therefore, despite the risk of mutual opportunistic behavior, simple institutional settings lead to cooperation, relatively rare events of fraud, and efficient markets.

Annotated record of the detailed examination of Mn deposits from the H.M.S. Sealark Percy Sladen Trust Expedition in the Indian ocean

In 1905, the Percy Sladen Trust Expedition, under the supervision of Stanley Gardiner in H.M.S. 'Sealark' made an extensive cruise in the Indian Ocean. The author received 79 samples from Mr. Gardiner which were thoroughly examined.