基于Agent的建模方法在复杂社会系统中的预测

IF 3 3区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
C. Elsenbroich, J. Gareth Polhill
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引用次数: 0

摘要

基于主体的模型(ABMs)起源于复杂性科学的考虑,它规定许多现象可以“自下而上地生长”。Epstein & Axtell(1996)的《成长中的人工社会》(Growing Artificial Societies)明确表达了这一点,从“你能解释一下吗?”到“你能种吗?”2008年,爱泼斯坦发表了一篇题为《为什么是模型?》在这篇文章中,他谈到了他对那些要求从ABM中做出预测的人的愤怒,并指出,与预测相比,ABM可能应用于许多其他更值得考虑的目的,包括解释、改进数据收集、检验理论和提出类比。14年过去了,关于ABM预测能力的争论仍未得到解决。本期特刊介绍了对ABM和预测的一系列立场,解决了方法论、认识论和实用主义问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agent-based modelling as a method for prediction in complex social systems
ABSTRACT Agent-based models (ABMs) have their origins in considerations of complexity science stipulating that many phenomena can be ‘grown from the bottom up’. Explicitly, this was expressed in Epstein & Axtell’s (1996) Growing Artificial Societies as the change from ‘Can you explain it?’ to ‘Can you grow it?’. In 2008, Epstein published an article entitled Why Model? in which he discussed his exasperation with people asking for predictions from ABM, pointing out that many other purposes to which it might be applied are more worthy of consideration than prediction, including explanation, improving data collection, testing theories and suggesting analogies. Fourteen years later, the debate about the predictive powers of ABM is still unresolved. This special issue presents the range of positions on ABM and prediction, tackling methodological, epistemological and pragmatic issues.
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来源期刊
International Journal of Social Research Methodology
International Journal of Social Research Methodology SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
7.90
自引率
3.00%
发文量
52
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