System Dynamics and Intelligent Agent-Based Simulation : Where is the Synergy ?
System Dynamics and Intelligent Agent-Based Simulation : Where is the Synergy ?
复制标题
系统动力学和基于智能代理的仿真：协同作用在哪里？
DOI：
--
复制
发表时间：
--
期刊：
影响因子：
--
通讯作者：
H. Sedehi
中科院分区：
文献类型：
--
作者：
John Pourdehnad;K. Maani;H. Sedehi
教学研究表明，虽然传统的教学方法可用于传授事实信息（“冷知识”），但模拟和视频游戏在教授决策过程（“热知识”）方面更有效。视频游戏创作的进步允许开发多智能体，人工社会模拟器，具有模拟生理，压力，情感和行动决策过程的能力。这种新方法使您能够对组织及其相关业务环境的复杂性有上级理解。这反过来又提供了一个玩游戏的机会，有助于促进更好的决策。系统动力学还允许管理人员明确他们对业务问题的理解并改进它们。这是通过对结构进行建模（例如，关系、政策、激励机制等）。虽然系统动力学承认个人和组织心理模型的关键作用（例如，动机、价值观、规范、偏见等）作为结构的基础或关键影响因素，它没有明确地建模心理模型，也没有考虑决策者的情绪。相反，在基于代理的建模（ABM）中，组织被建模为半自主决策部分（有目的的个人）的系统，称为代理。宏观行为不是模拟的;它来自个体代理人的微观决策。在这项工作中，每个代理单独评估其情况，并根据行动目标的价值层次，对人工制品的偏好和行为标准做出决策。在ABM中，代理人有一个有限的理性，受到压力，时间压力和情感力量。在最简单的层次上，基于代理的模型由代理系统和它们之间的关系组成。基于代理的建模的经验表明，即使是一个简单的基于代理的模型可以表现出复杂的行为模式，并提供有价值的信息动态的真实的世界系统，模仿他们。在本文中，两种不同的模拟方法的学习效果，即，基于代理的建模和系统动力学的概念进行了比较，并讨论了它们之间的潜在协同作用。因此，本文具有理论性和探索性。本文的观察和理论还需要进一步的研究来提供实证证据。
Pedagogical research has demonstrated that while traditional teaching methods can be useful for imparting factual information ("cold knowledge"), simulations and video games are more effective in teaching decision-making processes ("warm knowledge"). Advances in video game creation allow the development of multi-agent, artificial society simulators with capabilities for modeling physiology, stress, emotion, and course-of-action decision-making. This new approach enables superior understanding of the complexity in organizations and their relevant business environments. This in turn provides an opportunity for game-play that helps promote better decision-making. System Dynamics also allows managers to make their understanding of business problems explicit and improve upon them. This occurs by modelling structures (e.g., relationships, policies, incentives, etc) that underlie behaviour of systems. While system dynamics acknowledges the critical role of personal and organizational mental models (e.g., motivations, values, norms, biases, etc.) as the foundation or key influencers of structure, it does not explicitly model mental models, nor does it take into account decision makers ‘mood’. In contrast, in Agent-Based Modelling (ABM), organizations are modelled as a system of semiautonomous decision-making parts (purposeful individuals) called agents). Macro-behaviour is not simulated; it emerges from the micro-decisions of individual agents. In this work, each agent individually assesses its situation and makes decisions based upon value hierarchies of goals for action, preferences for artefacts, and standards for behaviour. In ABM, agents have a bounded rationality that is subject to stress, time pressure, and emotive forces. At the simplest level, an agent-based model consists of a system of agents and the relationships between them. Experience with agent-based modelling shows that even a simple agent-based model can exhibit complex behaviour patterns and provide valuable information about the dynamics of the real world system that emulates them. In this paper the two different simulation approaches to learning effectiveness, i.e., the agent-based modelling and systems dynamics are compared conceptually and the potential synergy between them is discussed. As such this paper is theoretical and exploratory in nature. Further studies are needed to provide empirical evidence to the observations and theories put forward in this paper.
DOI：
--
发表时间：
1999
期刊：
--
影响因子：
--
作者：
Nigel Gi lbert;K. G. Troitzsch
通讯作者：
Nigel Gi lbert;K. G. Troitzsch