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.

摘要

基于主体的模型 (ABM) 起源于对复杂性科学的考虑，它规定许多现象可以“自下而上地发展”。明确地，这在 Epstein & Axtell (1996) 的Growing Artificial Societies中表达为从“你能解释一下吗？”的变化。到“你能种植它吗？”。2008 年，Epstein 发表了一篇题为Why Model?在其中，他与要求 ABM 进行预测的人讨论了他的愤怒，指出可能应用它的许多其他目的比预测更值得考虑，包括解释、改进数据收集、测试理论和建议类比。十四年后，关于 ABM 预测能力的争论仍未解决。本期特刊介绍了 ABM 和预测的立场范围，解决了方法论、认识论和实用问题。