Computerized agents versus human agents in finding core coalition in glove games

IF 1.3 4区 工程技术 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Gayane Grigoryan, Sheida Etemadidavan, Andrew J. Collins
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引用次数: 2

Abstract

One of the challenges for agent-based modeling is being able to incorporate human behavior. Human behavior is a multifaceted phenomenon, with strategic coalition formation being one form. A hybrid agent-based modeling approach, called ABMSCORE, has been derived to emulate strategic group formation. In this paper, we describe a simulation experiment to compare the ABMSCORE with actual human behavior. The comparison criterion is the respective rates of finding an ideal coalition. In our experimental design, we go to great lengths to ensure the similarity of the scenarios in the two trial types: trials with computerized agents only and trials involving human participants when one of the computerized agents is replaced by an actual human. We did this to limit the number of possible extraneous variables introduced into the experimental system. The scenario considered is the glove game, a standard cooperative game that has been previously used in human experiments. Our results indicate that the ABMSCORE model produces similar rates of finding the ideal coalition as the human players; however, there are some limitations. This research provides evidence for using the ABMSCORE modeling approach to model human strategic coalition formation in agent-based models.
手套博弈中计算机智能体与人类智能体寻找核心联盟的比较
基于代理的建模面临的挑战之一是如何整合人类行为。人类行为是一个多方面的现象,战略联盟的形成是一种形式。一种名为ABMSCORE的基于智能体的混合建模方法被用来模拟战略群体的形成。在本文中,我们描述了一个模拟实验来比较ABMSCORE与实际人类行为。比较标准是各自找到理想联盟的比率。在我们的实验设计中,我们竭尽全力确保两种试验类型中场景的相似性:仅使用计算机化代理的试验和涉及人类参与者的试验,其中一个计算机化代理被真人取代。我们这样做是为了限制可能引入实验系统的外来变量的数量。我们所考虑的场景是手套游戏,这是一种标准的合作游戏,之前曾用于人类实验。我们的研究结果表明,ABMSCORE模型产生了与人类玩家相似的理想联盟寻找率;然而,也有一些限制。本研究为在基于主体的模型中使用ABMSCORE建模方法来模拟人类战略联盟的形成提供了证据。
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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来源期刊
CiteScore
3.50
自引率
31.20%
发文量
60
审稿时长
3 months
期刊介绍: SIMULATION is a peer-reviewed journal, which covers subjects including the modelling and simulation of: computer networking and communications, high performance computers, real-time systems, mobile and intelligent agents, simulation software, and language design, system engineering and design, aerospace, traffic systems, microelectronics, robotics, mechatronics, and air traffic and chemistry, physics, biology, medicine, biomedicine, sociology, and cognition.
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