  Many social sciences such as psychology and economics try to learn the
behaviour of complex agents such as humans, organisations and countries. The
current statistical methods used for learning this behaviour try to infer
generally valid behaviour, but can only learn from one type of study at a time.
Furthermore, only data from carefully designed studies can be used, as the
phenomenon of interest has to be isolated and confounding factors accounted
for. These restrictions limit the robustness and accuracy of insights that can
be gained from social/economic systems. Here we present the online platform
TreeOfKnowledge which implements a new methodology specifically designed for
learning complex behaviours from complex systems: agent-based behaviour
learning. With agent-based behaviour learning it is possible to gain more
accurate and robust insights as it does not have the restriction of
conventional statistics. It learns agent behaviour from many heterogenous
datasets and can learn from these datasets even if the phenomenon of interest
is not directly observed, but appears deep within complex systems. This new
methodology shows how the internet and advances in computational power allow
for more accurate and powerful mathematical models.
