An experimental approach to find a suitable simulation method in business economics

IF 0.6 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Roman Řperka
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引用次数: 0

Abstract

The main goal of this paper is to compare the results of an agent-based and Monte Carlo simulation experiments in business process negotiation between sellers and customers of a simple trading commodity. The motivation of the presented research is to find suitable method for predicting key performance indicators of a business company. The intention is to develop a software module in the future which might help the management of business companies to support their decisions. Microeconomic demand functions were used as a core element in the negotiation. Specifically, Marshallian demand function and CobbDouglas utility functions is introduced. The paper firstly presents some of the principles of agent-based and Monte Carlo simulation techniques, and demand function theory. Secondly, we present a conceptual model of a business company in terms of a simulation framework. Thirdly, a formalization of demand functions and their implementation in a seller-to-customer negotiation is introduced. Lastly, we discuss some of the simulation results in one year of selling commodities. The results obtained show that agent-based method is more suitable than Monte Carlo in the presented domain, and the demand functions could be used to predict the trading results of a company in some metrics.
在商业经济学中寻找合适的模拟方法的实验方法
本文的主要目的是比较基于agent和蒙特卡罗模拟实验在简单交易商品的卖方和客户之间的业务流程谈判中的结果。本研究的动机是寻找合适的方法来预测企业的关键绩效指标。其目的是在未来开发一个软件模块,它可以帮助商业公司的管理层支持他们的决策。微观经济需求函数被用作谈判的核心要素。具体地说,引入了马歇尔需求函数和柯布道格拉斯效用函数。本文首先介绍了基于智能体和蒙特卡罗仿真技术的一些原理,以及需求函数理论。其次,我们根据仿真框架提出了一个商业公司的概念模型。第三,介绍了需求函数的形式化及其在卖方对客户谈判中的实现。最后,我们讨论了一年内销售商品的一些模拟结果。结果表明,基于智能体的方法比蒙特卡罗方法更适合于该领域,需求函数可以用于预测公司在某些指标上的交易结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.10
自引率
0.00%
发文量
22
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