# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 3 Results  
Each tool was developed with distinctive characteristics and purposes. Table 5 provides the specific technical details of the selected ABM tools. The comparative analysis of various ABM tools reveals their distinctive strengths and characteristics. AOR Simulation stands out with high modelling strength and a moderate development effort, making it particularly effective in representing complex systems. \"Modelling strength\" is defined as an ABM tool's ability to simulate complex systems and interactions within CBA scenarios. It considers factors like flexibility in agent definitions, detailed behaviour specifications, model validation, sensitivity analysis, and scalability. Tools with medium to high modelling strength can manage complex agent behaviours and interactions and provide robust mechanisms for model validation and sensitivity analysis. Ascape, utilising Java, demonstrates a balanced profile with medium-scale modelling strength and a moderate development effort, suitable for scenarios of moderate complexity. Envision, employing MS Visual C++, excels in scenarios of medium scale with a focus on reactive agents, offering a good compromise between modelling strength and development effort. Medium scale refers to the complexity of the scenarios being modelled rather than specifically to a time or space scale. In this context it describes models that are capable of handling scenarios with moderate levels of complexity, such as those involving multiple interacting agents, diverse socio-economic factors, and environmental data. This definition includes a balance between the number of variables, computational demands, and the ability to represent interactions adequately without reaching the extreme ends of either simplistic or highly complex models. GAMA, developed with the YourKit Java Profiler, is proficient in modelling scenarios of medium scale, particularly emphasising reactive agents. JAS, with Java as its language, stands out for its simplicity, providing easy modelling for medium-scale complexity. LSD, developed in C++, has high modelling strength, making it suitable for representing intricate systems with a balanced development effort. An \"intricate system\" is here defined as a complex and dynamic system composed of multiple interacting agents, where the overall system behaviour emerges from the collective interactions of its parts. This definition aligns with the established scientific understanding of intricate systems, particularly in the context of ABM and CBA. NetLogo, utilising Scala, is well-suited for medium-scale scenarios, offering a simple development effort. Repast HPC, excelling in extremely complex scenarios, demonstrates extreme-scale modelling strength with a moderate development effort in C++. SeSAm, with Java, combines high modelling strength with a simple development effort, making it well-suited for intricate modelling. UrbanSim, utilising Opus, offers a balanced profile for medium-scale complexity. A summary of the comparative assessment of suitability of the tools for CBA is presented in Table 5. In the following sections, each of the tools is explored in depth and discussed in terms of its suitability. For instance, when evaluating NetLogo, the authors assessed its capacity to integrate socio-economic factors by simulating interactions between agents representing different households with varying income levels, which directly influenced their adaptation decisions. This showcases how the tool's features were practically assessed.