Simple or complicated agent-based models? A complicated issue
Authored by Zhanli Sun, Stefano Balbi, Birgit Mueller, Juergen Groeneveld, Henning Nolzen, Jule Schulze, Nicolas R Magliocca, Iris Lorscheid, James D Millington, Steffen Lauf, Carsten M Buchmann
Date Published: 2016
DOI: 10.1016/j.envsoft.2016.09.006
Sponsors:
European Union
German Federal Ministry of Education and Research (BMBF)
United States National Science Foundation (NSF)
National Socio-Environmental Synthesis Center
Platforms:
NetLogo
Model Documentation:
ODD
Mathematical description
Model Code URLs:
Model code not found
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. (C) 2016 Elsevier Ltd. All
rights reserved.
Tags
Simulation
Complexity
multiagent systems
ecology
land-use dynamics
Institutional change
Challenges
Human decisions
Odd protocol
Socioecological systems