  A characteristic feature of complex systems in general is a tight coupling
between their constituent parts. In complex socio-economic systems this kind of
behavior leads to self-organization, which may be both desirable (e.g. social
cooperation) and undesirable (e.g. mass panic, financial "bubbles" or
"crashes"). Abundance of the empirical data as well as general insights into
the trading behavior enables the creation of simple agent-based models
reproducing sophisticated statistical features of the financial markets. In
this contribution we consider a possibility to prevent self-organized extreme
events in artificial financial market setup built upon a simple agent-based
herding model. We show that introduction of agents with predefined
fundamentalist trading behavior helps to significantly reduce the probability
of the extreme price fluctuations events. We also test random trading control
strategy, which was previously found to be promising, and find that its impact
on the market is rather ambiguous. Though some of the results indicate that it
might actually stabilize financial fluctuations.
