Simple or complicated agent-based models? A complicated issue

Abstract

Agent-based models (ABMs) are increasingly recognized as valuable tools in modelling humanenvironmental 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.

Agent-based modeling of environment-migration linkages: a review

“…this is not "migration out of the system") . A1.14 Diagram drafted for the ABM described by Walsh et al (2013) and 2016) Table A1.14 Standardized protocol for the ABM described by Walsh et al (2013) and 2016) General Reference ( . A1.15 Diagram drafted for the ABM described by ; agents do not make explicit return decisions, but migrate from region to region and can thereby visit a region again at some point in the future…”

Conclusion: Perspectives on Urban Theories

“…This main proposal is based on general principles for modeling and simulation in the social sciences introduced by Banos (2013), which develops general guidelines to extract knowledge from simulation models. These relate to and draw on widely established practices in diverse disciplines using modeling and simulation, such as ecology (Grimm & Railsback, 2012), computational social science (Epstein, 2006), and general methodological contributions on agent-based modeling for example (Sun et al, 2016). These include in particular that (i) models have different objectives and functions; (ii) they thus must be shared in an open way for their benchmarking and comparison; (iii) models must be reused and coupled; (iv) behavior of models must be known in a precise way with extensive sensitivity analyses.…”

Crossing the chasm: a ‘tube-map’ for agent-based social simulation of policy scenarios in spatially-distributed systems

“…In policy circles, complexity is in any case a hard sell at best, if not an outright deterrent, especially if it raises issues of model output uncertainty (ibid.). Indeed, the academic community are not always especially keen on complexity either [27]. However, managing risks entails being clear about uncertainty and other limits of what is known, rather than ignoring it for hypocognitive [28] reasons that are often clothed in pragmatism.…”

“…Sun et al [27] draw on Loehle's [114] concept of the Medawar zone to argue that, in comparison with conceptual ABMs, empirical ABMs have a relatively high level of complicatedness at which optimum model utility occurs. McDowall and Geels's [115] response to Holtz et al [116] article on modelling transitions draws on Andersson et al [117] to characterize social-ecological systems as both complex (bottom-up, self-organized) and complicated (in structure), and hence only amenable to narrative analysis.…”