AmalgaCloud: Social Network Adaptation for Human and Computational Agent Team Formation

Many complex problems can be solved through an eective organization of human experts and software agents connected by a social network where each node contributes the unique skill set needed to enable a higher order problem solving capability of the group. Recent work in crowdsourcing applications based on enterprise social networks (e.g. PeopleCloud) has shown that the group problem solving approach can be extended to enterprise and potentially Internet-wide scales. However, systems operating at such scales assume that candidate group participants make decisions about which groups to join based on limited connectivity and local information. This paper focuses on the relationship between network adaptation for candidate group participants and performance of problem solving groups. We demonstrate that systems that expect to form groups by engaging participants equipped with diverse skill sets require more sophisticated network adaptation strategies than what can be expected based on previous research. To address this need, we evaluate a set of network adaptation algorithms for crowdsourcing and present some empirical results from a simulation based study.