基于Swarm的股权拍卖机制设计与仿真研究

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.

Research on mechanism design and simulation of equity auction based on Swarm

Based on multi-agent modeling and simulation method, this paper uses particle swarm optimization (PSO) swarm intelligence algorithm to simulate the learning mechanism and evolution rule of outside investors' valuations under the condition of information interaction, and establishes an equity auction model of venture capital exit on the basis of mechanism design. Simulation analysis of equity auction model on Swarm platform shows that the above-mentioned equity auction mechanism can significantly improve the revenue of venture capitalist, and help venture capitalist to predict the change of outside investors' valuations and auction participation. Simulations of equity auction model's parameters find that venture capitalist can further improve their income by introducing more outside investors to participate in the auction; even outside investors overemphasize the importance of one single learning ability, the relatively satisfactory equity auction results also can be achieved eventually. The study of this article can provide policy references for venture capitalist.