Third-Party Effects

Most theories about effects of social embeddedness on trust define mechanisms that assume someone’s decision to trust is based on the reputation of the person to be trusted or on other available information. However, there is little empirical evidence about how subjects use the information that is available to them. In this chapter, we derive hypotheses about the effects of reputation and other information on trust from a range of theories and we devise an experiment that allows for testing these hypotheses simultaneously. We focus on the following mechanisms: learning, imitation, social comparison, and control. The results show that actors learn particularly from their own past experiences. Considering third-party information, imitation seems to be especially important

Third-Party Effects

Most theories about effects of social embeddedness on trust define mechanisms that assume someone’s decision to trust is based on the reputation of the person to be trusted or on other available information. However, there is little empirical evidence about how subjects use the information that is available to them. In this chapter, we derive hypotheses about the effects of reputation and other information on trust from a range of theories and we devise an experiment that allows for testing these hypotheses simultaneously. We focus on the following mechanisms: learning, imitation, social comparison, and control. The results show that actors learn particularly from their own past experiences. Considering third-party information, imitation seems to be especially important

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Competition fosters trust

We study the effects of reputation and competition in a stylized market for experience goods. If interaction is anonymous, such markets perform poorly: sellers are not trustworthy, and buyers do not trust sellers. If sellers are identifiable and can, hence, build a reputation, efficiency quadruples but is still at only a third of the first best. Adding more information by granting buyers access to all sellers’ complete history has, somewhat surprisingly, no effect. On the other hand, we find that competition, coupled with some minimal information, eliminates the trust problem almost completely

Dynamics of Trust Reciprocation in Heterogenous MMOG Networks

Understanding the dynamics of reciprocation is of great interest in sociology and computational social science. The recent growth of Massively Multi-player Online Games (MMOGs) has provided unprecedented access to large-scale data which enables us to study such complex human behavior in a more systematic manner. In this paper, we consider three different networks in the EverQuest2 game: chat, trade, and trust. The chat network has the highest level of reciprocation (33%) because there are essentially no barriers to it. The trade network has a lower rate of reciprocation (27%) because it has the obvious barrier of requiring more goods or money for exchange; morever, there is no clear benefit to returning a trade link except in terms of social connections. The trust network has the lowest reciprocation (14%) because this equates to sharing certain within-game assets such as weapons, and so there is a high barrier for such connections because they require faith in the players that are granted such high access. In general, we observe that reciprocation rate is inversely related to the barrier level in these networks. We also note that reciprocation has connections across the heterogeneous networks. Our experiments indicate that players make use of the medium-barrier reciprocations to strengthen a relationship. We hypothesize that lower-barrier interactions are an important component to predicting higher-barrier ones. We verify our hypothesis using predictive models for trust reciprocations using features from trade interactions. Using the number of trades (both before and after the initial trust link) boosts our ability to predict if the trust will be reciprocated up to 11% with respect to the AUC

Economic Growth, Innovation, Cultural Diversity. What Are We All Talking About? A Critical Survey of the State-of-the-art

This report constitutes the first deliverable of the project ENGIME – Economic Growth and Innovation in Multicultural Environments, financed by the European Commission – FP5 – Key Action: Improving socio-economic knowledge base. Contract HPSE-CT2001-50007Multiculturalism, Diversity, Economic Growth