Group decision making (GDM) is a central activity of human interaction, and also an increasingly
important issue in computer science with applications such as multi-agent systems, preference
aggregation in artificial intelligence, e-democracy platforms, and group recommender systems. This
research project is situated in the field of computational social choice (COMSOC), which is
concerned with the study of GDM from a computational point of view. The main focus of this
project lies on sustainable, long-term, algorithm-supported GDM within small or medium-sized
groups that decide about a varying range of topics.
Instead of considering decisions as singular events, we want to take the history of past group
decisions into account. This novel viewpoint allows us to tackle a fundamental issue of GDM: how
to avoid situations where frustrated participants drop out of the decision making process. For
example, if a minority has been repeatedly overruled in the past, it might withdraw from the
decision process. To overcome this problem, we will introduce and analyze continuative voting
rules: these are voting rules that take the history of previous decisions into account and guarantee
fairness across this history.
Furthermore, if a GDM algorithm does not provide incentives to compromise, the decision quality
might deteriorate and, consequently, users may refuse to use a corresponding system. Hence, we
will study measures that foster compromise in GDM and will augment continuative voting rules
with corresponding methods.
Finally, it may be that GDM algorithms require excessively detailed preference information. In such
a case, the effort required to use such a system may outweigh its benefits (such as guarantees on
fairness). We will analyze the information requirements of continuative voting rules and reduce
these by utilizing information learned from previous decisions.
The overall goal of this project is to find GDM algorithms that foster participation and make the
GDM process long-term sustainable. To achieve this goal, we will use a large array of methods
established in computational social choice. The outcome of this project contributes to a future in
which GDM apps and e-democracy systems have "COMSOC inside", and hence make use of the
theoretical and experimental expertise established in this field. The perspectives of the proposed
researchlong-term GDM, compromise, and reduced information requirementsare essential
ingredients for sustaining GDM processes.