Dynamic behaviours of mix-game model and its application

In this paper a minority game （MG） is modified by adding into it some agents who play a majority game. Such a game is referred to as a mix-game. The highlight of this model is that the two groups of agents in the mix-game have different bounded abilities to deal with historical information and to count their own performance. Through simulations, it is found that the local volatilities change a lot by adding some agents who play the majority game into the MG, and the change of local volatilities greatly depends on different combinations of historical memories of the two groups. Furthermore, the analyses of the underlying mechanisms for this finding are made. The applications of mix-game mode are also given as an example.

System dynamics of behaviour-evolutionary mix-game models

In real financial markets there are two kinds of traders： one is fundamentalist, and the other is a trend-follower. The mix-game model is proposed to mimic such phenomena. In a mix-game model there are two groups of agents： Group 1 plays the majority game and Group 2 plays the minority game. In this paper, we investigate such a case that some traders in real financial markets could change their investment behaviours by assigning the evolutionary abilities to agents： if the winning rates of agents are smaller than a threshold, they will join the other group; and agents will repeat such an evolution at certain time intervals. Through the simulations, we obtain the following findings： （i） the volatilities of systems increase with the increase of the number of agents in Group 1 and the times of behavioural changes of all agents; （ii） the performances of agents in both groups and the stabilities of systems become better if all agents take more time to observe their new investment behaviours; （iii） there are two-phase zones of market and non-market and two-phase zones of evolution and non-evolution; （iv） parameter configurations located within the cross areas between the zones of markets and the zones of evolution are suited for simulating the financial markets.

投资者反馈交易视角下证券流动性多重分形波动的内在机理分析

针对证券流动性存在多重分形波动特征但鲜有成果探究其内在机理的背景下,本文基于投资者普遍采用正反馈和负反馈交易行为的客观事实,从投资者交易行为出发,结合分形市场假说,独辟蹊径地构建了分形耦合少数-多数派博弈(fractal coupling minority-majority game,FCMMG)模型,推演出投资者在正反馈和负反馈交易策略行为间的动态多样化选择驱使着证券流动性的多重分形波动。在此基础上,通过实证发现,仿真流动性具有多重分形波动特征,使用FCMMG解释证券流动性呈现多重分形波动特征具有合理性和有效性。研究结果既为探究证券流动性多重分形波动特征开拓了视角,也为利用证券流动性的多重分形波动进行风险预警奠定了基础。

集市模型:少数者与多数者博弈演化分析

为比较主体不同行为策略对社会经济复杂系统演化的影响,基于酒吧模型提出了多数者博弈的集市模型A和少数者-多数者混合博弈的集市模型B。采用多主体建模方法,引入了主体的预期规则、策略选择、学习与适应等微观机制,模型模拟结果展现了人类社会系统所独有的预期的自我实现和/或自我毁灭现象。文章着重讨论了主体预期、行为策略对稳态参与人数均值的影响,发现主体的行为策略是决定系统演化结果的关键因素。

多数者博弈模型演化分析

为研究经纪人行为策略对社会经济复杂系统的影响,本文在少数者博弈模型的基础上提出多数方获胜的基本多数者博弈模型以及演化多数者博弈模型。采用多主体建模方法,模型中的众多经纪人被赋予有限理性,他们可以选择自己的最优策略或者调整策略概率来竞争有限资源,演化到稳态后也能表现出人类社会系统所独有的现象。在基本少数者博弈中,当模型中的游戏规则变为多数方获胜时,得到的基本多数者博弈模型可以更快演化到稳定状态。并且在历史记忆长度m较小的时候系统资源利用率较高,随着m增大资源利用率逐渐降低,最终与经纪人随机选择得到的结果一致。而演化多数者博弈模型的资源利用率则不受m影响,因此在m较大时,引入演化能提高资源利用率。同样的系统参数,随机初始条件不同演化多数者博弈模型经纪人概率也有可能分布在p=0.5不同侧。同时发现,稳定后每时步平均获胜方人数与经纪人概率分布也有联系,在经纪人概率重置时采用不同的边界条件,得到的经纪人概率分布也不同。进一步分析演化多数者博弈模型系统资源利用率,发现经纪人新旧策略概率的相关程度r越大,概率分布越平坦,系统资源利用率越高。增加奖惩比R,也会影响经纪人概率分布,资源利用率也会提高。