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A characteristic feature of complex systems in general is a tight couplingbetween their constituent parts. In complex socio-economic systems this kind ofbehavior leads to self-organization, which may be both desirable (e.g. socialcooperation) and undesirable (e.g. mass panic, financial "bubbles" or"crashes"). Abundance of the empirical data as well as general insights intothe trading behavior enables the creation of simple agent-based modelsreproducing sophisticated statistical features of the financial markets. Inthis contribution we consider a possibility to prevent self-organized extremeevents in artificial financial market setup built upon a simple agent-basedherding model. We show that introduction of agents with predefinedfundamentalist trading behavior helps to significantly reduce the probabilityof the extreme price fluctuations events. We also test random trading controlstrategy, which was previously found to be promising, and find that its impacton the market is rather ambiguous. Though some of the results indicate that itmight actually stabilize financial fluctuations.

Herding interactions as an opportunity to prevent extreme events in financial markets