  Decision makers are often confronted with complex tasks which cannot be
solved by an individual alone, but require collaboration in the form of a
coalition. Previous literature argues that instability, in terms of the
re-organization of a coalition with respect to its members over time, is
detrimental to performance. Other lines of research, such as the dynamic
capabilities framework, challenge this view. Our objective is to understand the
effects of instability on the performance of coalitions which are formed to
solve complex tasks. In order to do so, we adapt the NK-model to the context of
human decision-making in coalitions, and introduce an auction-based mechanism
for autonomous coalition formation and a learning mechanism for human agents.
Preliminary results suggest that re-organizing innovative and well-performing
teams is beneficial, but that this is true only in certain situations.
