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基于多主体的建模仿真方法,运用particle swarm optimization(PSO)群体智能算法模拟信息交互条件下外部投资者估价变化的学习机制和演化规律,在机制设计的基础上,建立了实现风险投资退出的股权拍卖模型.在Swarm平台上对股权拍卖模型的仿真分析表明,所设计的股权拍卖机制能够显著地提高风险投资家的收益,并能帮助风险投资家预测外部投资者的估价和拍卖参与度的变化.对股权拍卖模型的参数仿真发现,风险投资家可以通过引入更多的外部投资者参与股权拍卖来进一步提高自己的收益;即便外部投资者过度强化单一学习能力,最终也可以得到相对理想的股权拍卖结果.本文的研究可以为风险投资家的策略选择提供参考依据.
Based on multi-agent modeling and simulation method, particle swarm optimization (PSO) swarm intelligence algorithm is used to simulate the learning mechanism and evolution rule of external investors’ valuation changes under the condition of information interaction. On the basis of mechanism design, Equity auction model.The simulation analysis of the equity auction model on the Swarm platform shows that the designed equity auction mechanism can significantly increase the return of venture capitalists and can help venture capitalists to predict the value of external investors’ valuation and auction participation Change.The simulation of the parameters of the equity auction model found that venture capitalists can further increase their profits by introducing more external investors to participate in the equity auction. Even if the external investors over-emphasize the single learning ability, they can eventually get relatively ideal Equity auction results.The research in this paper can provide a reference for the venture capitalist’s strategy selection.