A scalable content-addressable network
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Robust incentive techniques for peer-to-peer networks
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Building small worlds in unstructured P2P networks using a multiagent Bayesian inference mechanism
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Social network semantics for agent communication
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Engaging the dynamics of trust in computational trust and reputation systems
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Comparison of Topologies in Peer-to-Peer Data Sharing Networks
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Towards Efficient Equilibria of Combinations of Network-Formation and Interaction Strategies
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Exploration and Exploitation in Adaptive Trust-Based Decision Making in Dynamic Environments
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Cooperative games with overlapping coalitions
Journal of Artificial Intelligence Research
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Social search platforms like Aardvark or Yahoo Answers have attracted a lot of attention lately. In principle, participants have two strategic dimensions in social search systems: (1) Interaction selection, i.e., forwarding/processing incoming requests (or not), and (2) contact selection, i.e., adding or dropping contacts. In systems with these strategic dimensions, it is unclear whether nodes cooperate, and if they form efficient network structures. To shed light on this fundamental question, we have conducted a study to investigate human behavior in interaction selection and to investigate the ability of humans to form efficient networks. In order to limit the degree of problem understanding necessary by the study participants, we have introduced the problem as an online game. 193 subjects joined the study that was online for 67 days. One result is that subjects choose contacts strategically and that they use strategies that lead to cooperative and almost efficient systems. Surprisingly, subjects tend to overestimate the value of cooperative contacts and keep cooperative but costly contacts. This observation is important: Assisting agents that help subjects to avoid this behavior might yield more efficiency.