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Recent reviews stated that the complex and context-dependent nature of human decisionmaking resulted in ad-hoc representations of human decision in agent-based land use change models (LUCC ABMs) and that these representations are often not explicitly grounded in theory. However, a systematic survey on the characteristics (e.g. uncertainty, adaptation, learning, interactions and heterogeneities of agents) of the representation of human decision in LUCC ABMs is missing. To inform this debate we performed a quantitative review of 134 LUCC ABM papers using a standardised questionnaire with a particular focus on the characteristics and the theoretical foundation of human decision-making. Thereby, we investigated whether implementations of human decision-making in current LUCC ABMs are theory based. Additionally, we assessed to which degree key factors such as learning, interaction or economic, environmental or social influence factors are considered in human decision making sub-models. We show that most human decision sub-models are not explicitly based on a specific theory and if so they are mostly based on economic theories. In contrast, promising psychological theories such as the theory of planned behaviour are the exception. The key factors of human decision sub-models showed a huge diversity and are not strongly related to neither the characteristics of the specific studied systems (e.g. rural vs. urban or its geographic location) nor the applied theoretical paradigm. We finish by presenting approaches for consolidating and enlarging the theoretical basis for modelling human decision-making.

Understanding social-ecological systems (SES) is crucial to supporting the sustainable management of resources. Agent-based modelling is a valuable tool to achieve this because it can represent the behaviour and interactions of organisms, human actors and institutions. Agent-based models (ABMs) have therefore already been widely used to study SES. However, ABMs of SES are by their very nature complex. They are therefore di icult to parameterize and analyse, which can limit their usefulness. It is time to critically reflect upon the current state-of-the-art to evaluate to what degree the potential of agent-based modelling for gaining general insights and supporting specific decision-making has already been utilized. We reviewed achievements and challenges by building upon developments in good modelling practice in the field of ecological modelling with its longer history. As a reference, we used the TRACE framework, which encompasses elements of model development, testing and analysis. We firstly reviewed achievements and challenges with regard to the elements of the TRACE framework addressed in reviews and method papers of social-ecological ABMs. Secondly, in a mini-review, we evaluated whether and to what degree the elements of the TRACE framework were addressed in publications on specific ABMs. We identified substantial gaps with regard to ( ) communicating whether the models represented real systems well enough for their intended purpose and ( ) analysing the models in a systematic and transparent way so that model output is not only observed but also understood. To fill these gaps, a joint e ort of the modelling community is needed to foster the advancement and use of strategies such as participatory approaches, standard protocols for communication, sharing of source code, and tools and strategies for model design and analysis. Throughout our analyses, we provide specific recommendations and references for improving the state-of-the-art. We thereby hope to contribute to the establishment of a new advanced culture of agent-based modelling of SES that will allow us to better develop general theory and practical solutions.

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.

Weise, H. (2014). Standardised and transparent model descriptions for agent-based models: Current status and prospects. ENVIRONMENTAL MODELLING AND SOFTWARE, 55, 156-163. https://doi.org/10.1016/j.envsoft.2014 Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections. General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain •You may freely distribute the URL identifying the publication in the Research Portal Take down policy If you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access to the work immediately and investigate your claim.