Sensitivity analyses need to simulate thousands of different parameter value combinations to gain a better understanding of the modelled systems. Often sensitivity analyses are the central part of such refinements, because they give access to detailed information on the relative importance of model parameters for model outputs (Saltelli et al., 2008). Model analysis is a crucial part of the modelling cycle, not only for understanding model processes but also during model refinement and development (Grimm & Railsback, 2005). With rising complexity of NetLogo models, increasing efforts need to be spent on model analyses and documentation of such analyses. NetLogo is a Java-based modelling environment that features a very comprehensible syntax, which allows for fast prototyping of agent-based models but also offers capabilities to formulate and implement complex agent-based models efficiently (Railsback et al., 2017). Yet, incentives for sharing code of agent-based models and model analyses alongside publications are still lacking (Janssen, 2017).Ī widely used programming language to develop agent-based models in ecological and socio-economic sciences is NetLogo (Abar, Theodoropoulos, Lemarinier, & O'Hare, 2017 Vincenot, 2018 Wilensky, 1999). Access to scripts, runs and results of statistical analyses is a key criterion for reproducible research (Peng, 2011 Sandve, Nekrutenko, Taylor, & Hovig, 2013). However, next-generation agent-based models require reproducability, repeatability and parallelization of model analyses. Due to the release from computing power constraints in recent years, next-generation agent-based models have evolved that are structurally realistic, powerful and detailed enough to address real-world problems (Cabral, Valente, & Hartig, 2017 Grimm & Berger, 2016). Agent-based models incorporate the heterogeneity of entities at the individual level in order to observe patterns emerging on broader scales (Grimm & Railsback, 2005). They are developed and applied across many different research disciplines from natural sciences over archaeology to socio-economic sciences (e.g. The nlrx package is the first framework for documentation and application of reproducible NetLogo simulation model analysis.Īgent-based models are an increasingly popular method for understanding complex systems (DeAngelis & Grimm, 2014).We also present a use case scenario using a NetLogo model, for which we performed a sensitivity analysis and a genetic algorithm optimization. We provide a detailed description of the nlrx package functions and the overall workflow.nlrx enables reproducibility by storing all relevant information and simulation output of experiments in one r object which can conveniently be archived and shared. Output is automatically collected in user-friendly formats and can be post-processed with provided utility functions. Class objects make setting up experiments more convenient and helper functions provide many parameter exploration approaches, such as Latin Hypercube designs, Sobol sensitivity analyses or optimization approaches. We present the r-package nlrx, which overcomes stability and resource allocation issues by running NetLogo simulations via dynamically created XML experiment files.However, this package is not suited for efficient, reproducible research as it has stability and resource allocation issues, is not straightforward to be setup and used on high performance computing clusters and does not provide utilities, such as storing and exchanging metadata, in an easy way. One tool for controlling NetLogo externally is the r-package RN etL ogo. NetLogo is a widely used environment for agent-based model development, but it does not provide sufficient built-in tools for extensive model exploration, such as sensitivity analyses. However, this is at the expense of increased model complexity, which requires more efficient tools for model exploration, analysis and documentation that enable reproducibility, repeatability and parallelization. Due to increasing capacities of computing resources it was possible to improve the level of detail and structural realism of next-generation models in recent years. Agent-based models find wide application in all fields of science where large-scale patterns emerge from properties of individuals.
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