论文标题

平行的少数族裔游戏及其在流行病期间的运动优化中应用

Parallel Minority Game and it's application in movement optimization during an epidemic

论文作者

Biswas, Soumyajyoti, Mandal, Amit Kr

论文摘要

我们介绍了少数族裔游戏的版本,其中可用选择的总数为$ D> 2 $,但是代理只有两个可用的选择。因此,对于任何给定选择中的所有代理商,因此,其他选择都在剩余的$ d-1 $选项之间分配。从本质上是一组MG,并并行播放,这使得达到具有最大资源利用率的状态时增加了复杂性。我们表明,在MG中使用的随机策略在这里也很好。我们讨论模型还原为其他已知模型的限制。最后,我们研究了该模型在持续流行期间一个国家内部各州之间人口运动中的应用。我们表明,该国的总受感染人口可能与长期停止区域间运动的完全停止,只要代理人反而遵循上述随机策略,就他们的两种选择之间的运动决策。代理商的目的是保持其两种选择之间的感染状态。我们进一步表明,遵循随机策略的任何两个州之间的动力器一次,而当不考虑旅行期间被感染的风险被视为(或不选择)这种运动选择的可能性。这表明了移动药物遵循随机策略的动机。

We introduce a version of the Minority Game where the total number of available choices is $D>2$, but the agents only have two available choices to switch. For all agents at an instant in any given choice, therefore, the other choice is distributed between the remaining $D-1$ options. This brings in the added complexity in reaching a state with the maximum resource utilization, in the sense that the game is essentially a set of MG that are coupled and played in parallel. We show that a stochastic strategy, used in the MG, works well here too. We discuss the limits in which the model reduces to other known models. Finally, we study an application of the model in the context of population movement between various states within a country during an ongoing epidemic. We show that the total infected population in the country could be as low as that achieved with a complete stoppage of inter-region movements for a prolonged period, provided that the agents instead follow the above mentioned stochastic strategy for their movement decisions between their two choices. The objective for an agent is to stay in the lower infected state between their two choices. We further show that it is the agents moving once between any two states, following the stochastic strategy, who are less likely to be infected than those not having (or not opting for) such a movement choice, when the risk of getting infected during the travel is not considered. This shows the incentive for the moving agents to follow the stochastic strategy.

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