Crossing the chasm: a ‘tube-map’ for agent-based social simulation of policy scenarios in spatially-distributed systems
Crossing the chasm: a ‘tube-map’ for agent-based social simulation of policy scenarios in spatially-distributed systems
复制标题
跨越鸿沟：空间分布式系统中基于代理的政策场景社会模拟的“管道图”
作者：
J. Gareth Polhill;Jiaqi Ge;M. Hare;K. Matthews;A. Gimona;D. Salt;J. Yeluripati
基于智能体的模型（ABMs）模拟自主智能体/群体的行为和相互作用，以及它们对整个系统的影响，在不假设完美理性或完全知识的情况下解释学习。ABMs是一种越来越受欢迎的研究复杂的、空间分布的社会环境系统的方法，但在那些想要探索这种系统中的场景的人所期望的意义上，它仍然是一种既定的方法。在某种程度上，这是一个意识问题——反弹道导弹仍然很新，许多人还没有听说过它；在某种程度上，这是一个信心问题——如果ABM要成为一种首选方法，它还需要做更多的工作来证明自己。如果反弹道导弹要成为政策或利益相关者接受的主流科学的一部分，本文将确定反弹道导弹的工艺和部署所需要的进展。在过去的十年中，ABM的行为已经从使用系统的抽象表示（支持理论主导的思想实验）过渡到更容易获得的经验表示（提供更多的应用分析）。这加强了ABM产出的潜在用户的看法，即后者是突出和可信的。然而，经验性ABM并不是灵丹妙药，因为它需要更多的计算和数据资源，将应用程序限制在数据存在的领域，以及需要这些数据的合适环境模型。此外，经验ABM仍然面临着验证和用于首先描述系统的本体的严重问题。以Geoffrey a . Moore的《跨越鸿沟》（Crossing the Chasm）为例，我们认为ABM的前进之路在于确定它能最好地展示其优势的利基市场，与合作者合作，证明它能兑现承诺。这使我们确定了需要开展工作的几个领域。
Agent based models (ABMs) simulate actions and interactions of autonomous agents/groups and their effect on systems as a whole, accounting for learning without assuming perfect rationality or complete knowledge. ABMs are an increasingly popular approach to studying complex, spatially distributed socio-environmental systems, but have still to become an established approach in the sense of being one that is expected by those wanting to explore scenarios in such systems. Partly, this is an issue of awareness – ABM is still new enough that many people have not heard of it; partly, it is an issue of confidence – ABM has more to do to prove itself if it is to become a preferred method. This paper will identify advances in the craft and deployment of ABM needed if ABM is to become an accepted part of mainstream science for policy or stakeholders. The conduct of ABM has, over the last decade, seen a transition from using abstracted representations of systems (supporting theory-led thought experiments) to more accessible representations derived empirically (to deliver more applied analysis). This has enhanced the perception of potential users of ABM outputs that the latter are salient and credible. Empirical ABM is not, however, a panacea, as it demands more computing and data resources, limiting applications to domains where data exist along with suitable environmental models where these are required. Further, empirical ABM is still facing serious questions of validation and the ontology used to describe the system in the first place. Using Geoffrey A. Moore’s Crossing the Chasm as a lens, we argue that the way ahead for ABM lies in identifying the niches in which it can best demonstrate its advantages, working with collaborators to demonstrate that it can deliver on its promises. This leads us to identify several areas where work is needed.
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影响因子：
2.5
作者：
Grau, Bernardo Cuenca;Horrocks, Ian;Sattler, Ulrike
通讯作者：
Sattler, Ulrike
DOI：
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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
DOI：
10.1016/j.compenvurbsys.2008.09.004
发表时间：
2008-11-01
影响因子：
6.8
作者：
Crooks, Andrew;Castle, Christian;Batty, Michael
通讯作者：
Batty, Michael
DOI：
10.18564/jasss.3223
发表时间：
2016
期刊：
Journal of Artificial Societies and Social Simulation
影响因子：
--
作者：
Elsenbroich C
通讯作者：
Elsenbroich C