Abstract:
The relevance of this study stems from the need to develop effective tools for managing
innovative investment processes in a highly uncertain market environment. Traditional research approaches,
such as econometric modeling or system dynamics, often encounter difficulties in describing the adaptive
behavior of agents and unpredictable collective effects. Therefore, there is a need for tools that allow for more
realistic simulation of the behavior of investment market participants in all its complexity.
Aim. The study is to develop and test a multi-agent model to evaluate the effectiveness of various
innovative investment scenarios and identify optimal strategies for market participants.
Methods. This paper uses simulation and multi-agent modeling as the primary research methods.
Results. This article presents a multi-agent simulation model of an innovative investment system for
analyzing interactions between investment market participants. Simulation experiments demonstrate that
the developed model is able to replicate the dynamics of innovation system development, evaluate the
effectiveness of various investment strategies, predict market participant behavior, and determine optimal
parameters for interactions between agents.
Conclusions. Future studies propose expanding the model to include a more detailed classification of
investors and projects, integration with real data, and additional learning and collective investment
mechanisms. The developed model can serve as a basis for creating practical decision-making tools for innovative
investment and contribute to improving the efficiency of investment activities.
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