King's College London

Research portal

Simple or complicated agent-based models? A complicated issue

Research output: Contribution to journalArticle

Zhanli Sun, Iris Lorscheid, James David Anthony Millington

Original languageEnglish
Pages (from-to)56-67
JournalENVIRONMENTAL MODELLING AND SOFTWARE
Volume86
Early online date24 Sep 2016
DOIs
StatePublished - Dec 2016

Documents

King's Authors

Abstract

Agent-based models (ABMs) are increasingly recognized as valuable tools in modelling human-environmental systems, but challenges and critics remain. One pressing challenge in the era of “Big Data” and given the flexibility of representation afforded by ABMs, is identifying the appropriate level of complicatedness in model structure for representing and investigating complex real-world systems. In this paper, we differentiate the concepts of complexity (model behaviour) and complicatedness (model structure), and illustrate the non-linear relationship between them. We then systematically evaluate the trade-offs between simple (often theoretical) models and complicated (often empirically-grounded) models. We propose using pattern-oriented modelling, stepwise approaches, and modular design to guide modellers in reaching an appropriate level of model complicatedness. While ABMs should be constructed as simple as possible but as complicated as necessary to address the predefined research questions, we also warn modellers of the pitfalls and risks of building “mid-level” models mixing stylized and empirical components.

Download statistics

No data available

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454