# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 1 Introduction
### The Case for Agent-Based Modelling in Community-Based Adaptation to Climate Change  
ABM's capabilities make it the ideal tool for incorporating and handling CBA structures and processes and dynamics. It offers a robust framework for exploring the intricate interplay between an array of actors [33], spanning from individual community members to organisational bodies across various levels from grassroot to local government. The domain of CBA is inherently complex, encompassing many agents, from households to local institutions and ecosystems [34]. ABM shines in its capacity to encapsulate these complexities, allowing for the representation of this diverse cast of characters and their dynamic behaviours within a community. In the realm of CBA, where adaptation strategies are as varied as the communities they support, context specificity is paramount [35]. ABM exhibits the capability to assimilate heterogeneity and context-specific nuances into its models [36]. This empowers researchers to represent the diversities within agent characteristics, including socio-economic conditions, cultural norms, and environmental landscapes. In a study conducted by [PERSON] et al. [37], ABM was applied to investigate the resilience and adaptive capabilities of rural households within the context of climate fluctuations, socio-economic variables, and community-level land use. The findings from this research reveal that the recurrent occurrences of extreme events, such as droughts, have a detrimental impact on the adaptive capacity of these households, ultimately resulting in migration from the region. Similarly, [PERSON] et al. [38] argue for the application of ABM to simulate the effects of adaptation on coastal tourism dynamics. Vulnerability reduction and community resilience building lie at the heart of CBA [39]. ABM provides a robust platform for assessing vulnerabilities at multiple tiers, from individual households to entire communities. It illuminates vulnerabilities within various community segments and ecosystems, thereby assisting policymakers in prioritising adaptation strategies. Furthermore, ABM facilitates scenario testing [40], a pivotal element in policy analysis for CBA. By modelling the repercussions of policy interventions and adaptation strategies, it becomes an invaluable aid in the decision-making process. It enables the dynamic simulation of the impacts of various policy measures, thereby guiding policymakers to opt for the most effective solutions. Engaging local stakeholders in the modelling process is not just advantageous but also indispensable for a successful CBA. ABM embraces and supports participatory modelling, in which community members actively partake in the development and testing of models. This participatory approach emerges as a vital means to ensure that adaptation strategies are not only effective but also socially acceptable and equitable [40]. Nevertheless, ABM, like any compelling tool, comes with its own set of challenges. The calibration and validation of models, data availability, and computational demands can be considerable hurdles. Researchers emphasise that it is imperative to obtain high-quality data and conduct rigor ous testing of ABM to ensure its reliability [41]. Moreover, there is promise in integrating ABM with other modelling approaches, such as system dynamics or econometric models. This represents an anchor point for future research and holds the potential to further enrich the field of CBA studies. ABM can provide one remedy within the domain of CBA studies. Its capacity to disentangle complexity, cater to context-specific nuances, and simulate the dynamics of adaptation strategies show synergies between the two tools.