Stable Invitations

We consider the situation in which an organizer is trying to convene an event, and needs to choose a subset of agents to be invited. Agents have preferences over how many attendees should be at the event and possibly also who the attendees should be. This induces a stability requirement: All invited agents should prefer attending to not attending, and all the other agents should not regret being not invited. The organizer's objective is to find the invitation of maximum size subject to the stability requirement. We investigate the computational complexity of finding the maximum stable invitation when all agents are truthful, as well as the mechanism design problem when agents may strategically misreport their preferences.Comment: To appear in COMSOC 201

How people make friends in social networking sites - A microscopic perspective

We study the detailed growth of a social networking site with full temporal information by examining the creation process of each friendship relation that can collectively lead to the macroscopic properties of the network. We first study the reciprocal behavior of users, and find that link requests are quickly responded to and that the distribution of reciprocation intervals decays in an exponential form. The degrees of inviters/accepters are slightly negatively correlative with reciprocation time. In addition, the temporal feature of the online community shows that the distributions of intervals of user behaviors, such as sending or accepting link requests, follow a power law with a universal exponent, and peaks emerge for intervals of an integral day. We finally study the preferential selection and linking phenomena of the social networking site and find that, for the former, a linear preference holds for preferential sending and reception, and for the latter, a linear preference also holds for preferential acceptance, creation, and attachment. Based on the linearly preferential linking, we put forward an analyzable network model which can reproduce the degree distribution of the network. The research framework presented in the paper could provide a potential insight into how the micro-motives of users lead to the global structure of online social networks.Comment: 10 pages, 12 figures, 2 table

TINA as a virtual market place for telecommunication and information services: the VITAL experiment

The VITAL (Validation of Integrated Telecommunication Architectures for the Long-Term) project has defined, implemented and demonstrated an open distributed telecommunication architecture (ODTA) for deploying, managing and using a set of heterogeneous multimedia, multi-party, and mobility services. The architecture was based on the latest specifications released by TINA-C. The architecture was challenged in a set of trials by means of a heterogeneous set of applications. Some of the applications were developed within the project from scratch, while some others focused on integrating commercially available applications. The applications were selected in such a way as to assure full coverage of the architecture implementation and reflect a realistic use of it. The VITAL experience of refining and implementing TINA specifications and challenging the resulting platform by a heterogeneous set of services has proven the openness, flexibility and reusability of TINA. This paper describes the VITAL approach when choosing the different services and how they challenge and interact with the architecture, focusing especially on the service architecture and the Ret reference point definitions. The VITAL adjustments and enhancements to the TINA architecture are described. This paper contributes to proving that the TINA-based VITAL ODTA allows for easy and cost-effective development and deployment of advanced end-user and operator services, and can indeed act as the basis for a virtual market place for telecommunications service

Network formation by reinforcement learning: the long and medium run

We investigate a simple stochastic model of social network formation by the process of reinforcement learning with discounting of the past. In the limit, for any value of the discounting parameter, small, stable cliques are formed. However, the time it takes to reach the limiting state in which cliques have formed is very sensitive to the discounting parameter. Depending on this value, the limiting result may or may not be a good predictor for realistic observation times.Comment: 14 page

Modeling the formation of R\&D alliances: An agent-based model with empirical validation

We develop an agent-based model to reproduce the size distribution of R\&D alliances of firms. Agents are uniformly selected to initiate an alliance and to invite collaboration partners. These decide about acceptance based on an individual threshold that is compared with the utility expected from joining the current alliance. The benefit of alliances results from the fitness of the agents involved. Fitness is obtained from an empirical distribution of agent's activities. The cost of an alliance reflects its coordination effort. Two free parameters $a_{c}$ and $a_{l}$ scale the costs and the individual threshold. If initiators receive $R$ rejections of invitations, the alliance formation stops and another initiator is selected. The three free parameters $(a_{c},a_{l},R)$ are calibrated against a large scale data set of about 15,000 firms engaging in about 15,000 R\&D alliances over 26 years. For the validation of the model we compare the empirical size distribution with the theoretical one, using confidence bands, to find a very good agreement. As an asset of our agent-based model, we provide an analytical solution that allows to reduce the simulation effort considerably. The analytical solution applies to general forms of the utility of alliances. Hence, the model can be extended to other cases of alliance formation. While no information about the initiators of an alliance is available, our results indicate that mostly firms with high fitness are able to attract newcomers and to establish larger alliances

Guest editorial: Special issue on matching under preferences

No abstract available