  We discuss how minimal financial market models can be constructed by bridging
the gap between two existing, but incomplete, market models: a model in which a
population of virtual traders make decisions based on common global information
but lack local information from their social network, and a model in which the
traders form a dynamically evolving social network but lack any decision-making
based on global information. We show that a suitable combination of these two
models -- in particular, a population of virtual traders with access to both
global and local information -- produces results for the price return
distribution which are closer to the reported stylized facts. We believe that
this type of model can be applied across a wide range of systems in which
collective human activity is observed.
