# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 2 Methodology Adopted
### Tool Assessment Criteria
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**CBA Requirement** & **Assessment** & **Theoretical Justification** \\ \hline \multirow{6}{*}{Adaptability to Community Dynamics} & Assess the tool’s dynamic community interaction modelling and agent learning capabilities. Evaluate the agent’s integration of cognitive factors like updating strategies, household ingenuity, and proximity effects. Check the tool’s support for memory updating, discarding outdated information, and enhancing adaptability in response to environmental changes. & Dynamic interactions play a crucial role in community-based systems, as highlighted in numerous studies [14]. Models that overlook these interactions may oversimplify community dynamics and miss critical aspects. In climate change adaptation, the learning and adaptive capacity of actors are paramount for preparing communities and societies for the adverse impacts of climate change [58; 59]. & Socio-economic factors play a pivotal role in community-based adaptation [57], and models lacking the integration of these variables might overlook key determinants of successful adaptation. Additionally, demographic factors significantly impact vulnerability and resilience in communities facing environmental changes [60], necessitating their consideration in models to ensure realistic representations. \\ \hline \multirow{6}{*}{Participatory Modelling Support} & Evaluate the tool’s support for participatory modelling with community involvement and collaborative decision-making, focusing on features like stakeholder engagement, user-friendly interfaces, and interactive scenario planing. Look for functionalities such as participatory workshops, stakeholder consultations, and visualisation tools that enable non-experts to contribute to model development and explore alternative adaptation strategies together. & Participatory modelling enhances the legitimacy and effectiveness of models by incorporating local knowledge and perspectives [61], fostering a more accurate representation of community realities through the involvement of community members. Additionally, collaborative decision-making is crucial for developing adaptive strategies [62], highlighting the need for models to facilitate scenario planning and empower stakeholders in making informed choices for community well-being. \\ \hline \multirow{6}{*}{Handling Diverse Data Types} & Assess the tool’s data handling for CBA modelling in ABM. Evaluate support for diverse data types (climate, geographical, socio-economic, including interoperability, spatial handling, transformation, preprocessing, and database capabilities. Examine compatibility with standard formats/protocols. Evaluate the ability to integrate various data sources (socio-economic, environmental, demographic) by assessing ingestion, processing, and harmonisation across formats/platforms. Consider functionalities, import/export, geospatial formats, transformation tools, database connectivity, and exchange protocol compatibility. & The multidimensional nature of community-based challenges necessitates diverse data types for accurate modelling [63], highlighting the importance of models capable of handling varied data to represent the complexity of the community environment. Integration with diverse data sources aligns with the principles of data-driven decision-making in CBA [64], with models benefiting from the incorporation of climate, geographical, and socio-economic datasets. \\ \hline \multirow{6}{*}{Scalability to Different Community Sizes} & Evaluate the tool’s capability to scale models for varying community sizes, considering factors such as computational demands, resource allocation efficiency, the scalability of algorithms and data structures, parallel computing capabilities, optimisation techniques, performance monitoring features, parameter tuning support, sensitivity analysis, and flexibility in adjusting model resolution and granularity to meet specific modelling objectives and computational constraints. & Addressing scalability challenges is crucial for ensuring the robustness of models in varying community contexts [19; 65]. Scalable models are essential to accommodate diverse community sizes, ensuring applicability to both small and large communities and enabling adaptation to the size and complexity of the community being simulated [66; 67]. \\ \hline \multirow{6}{*}{Feedback Mechanisms and Monitoring} & Evaluate the tool’s ability to incorporate feedback loops for modelling the impact of adaptation measures over time and to support the continuous monitoring and evaluation of strategies. This includes assessing its capability to model dynamic interactions and feedback processes, track and analyse outcomes, and provide features such as data logging, performance dashboards, visualisation tools, and support for scenario analysis to assess strategy robustness and resilience. & Feedback loops are central to understanding the long-term impacts of adaptation measures [68], as models without feedback mechanisms may overlook delayed or indirect effects. Additionally, continuous monitoring and evaluation are essential for adaptive management [69], emphasising the need for models to support the ongoing assessment of implemented strategies for community well-being. \\ \hline \hline \end{tabular}
\end{table}