Comparative Approaches for Innovation in Agent-Based Modelling of Landscape Change

Abstract

In this Special Issue on "Agent-Based Modelling and Landscape Change" we aimed to bring together articles that showcase innovative uses of agent-based models (ABMs) for investigating and explaining landscape change and dynamics. The resulting 10 articles demonstrate the diverse range of processes and landscapes that ABMs are currently used to examine, including: land-use decision making in agricultural landscapes; soil erosion in semi-arid environments; forest change in mountainous landscapes; trade in 1st Century BC southern France; social adaptations of herders in northern Mongolia; and malaria epidemiology in ) draw on a range of modelling approaches, but one common theme among several of the papers is the use of comparative approaches. Here, we discuss how comparative approaches offer opportunities for future innovation in modelling landscape change, particularly for addressing the challenge of understanding the role of human activity in the Anthropocene.The issue of comparison in ABMs is not new to the studies in this Special Issue and has been advocated and pursued over many years. Axtell et al. (1996) [11] were among the earliest to investigate the alignment of computational models, or 'docking' as they suggested it might be abbreviated. Docking entailed comparing an ABM to another model (whether ABM or otherwise) of the same system to see if the models could reproduce similar results, thereby enabling critical experimentation and the determination of whether one model was better than another, or if one was a special case of the other (i.e., could be subsumed). Since then, model-to-model analysis has continued (e.g., Hales et al. 2003 [12], Rouchier et al. 2008 [13]), although the rate of comparison has not kept pace with number of ABMs being developed. Robust comparison of models, to the point of trying to 'break' them (i.e., identifying at what point modelled mechanisms are no longer useful for explaining observations), is needed to ensure credible and efficient scientific progress in computational modelling (Thiele and Grimm 2015 [14]). Beyond examining how well different models fit the same set of empirical data, model comparison can aim to reproduce others' models from scratch in new computer code (e.g., Janssen 2009 [15]) or extend analysis including by exploring the sensitivity of model parameters in greater detail (e.g., Miodownik et al. 2010 [16], Seagren 2015). In contrast, articles in this Special Issue examine variations in agent-based representation, from an entire absence of agent representation, through comparing heterogeneous vs. aggregated representation of human activity, to alternative means of parameterizing individual agent behaviour.For example, to investigate the effect of agricultural practices on the formation of deeply incised valley formations in semi-arid Mediterranean landscapes, Barton et al. (2015) [5] 'turned off' the human land-use component of their hybrid ABM-cellular model. By using the same model with humans represented versus not, this approach aims to understand th...

Discussing Landscape Compositional Scenarios Generated with Maximization of Non-Expected Utility Decision Models Based on Weighted Entropies

“…Concerning the object landscape, a recent review based on agent-based modeling of landscape dynamics shows a cornucopia of methods with emphasis on comparative approaches, pursuing the continuation of innovative modeling for understanding landscape change, its causes and consequences for sustainability in the Anthropocene [55].…”

Exploring Futures in Landscape Agronomy: Methodological Issues and Prospects of Combining Scenarios and Spatially Explicit Models

“…They can also be used to identify factors that influence farmers' decisions and practices the most (Olabisi et al 2015). Since ABMs show great potential for modeling agricultural landscapes at local and regional levels, combining them with other types of models to produce innovative representations will become more frequent (Millington and Wainwright 2016).…”