# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 1 Introduction
### Agent-Based Modelling and Community-Based Adaptation to Climate Change in the Literature  
While no explicit ABM of CBA exists in the literature so far, a number of studies do resonate with the core issues. This short review section synthesises and critically evaluates 18 studies to assess the role of ABM in advancing the goals of CBA. An initial Scopus search using the key phrase \"agent based modelling community based adaptation to climate change\" resulted in 28 hits from between 2008 and 2024, of which 18 were identified as relevant due to their focus on bottom-up adaptation modelling. A significant number of studies focus on modelling socio-ecological systems to address climate adaptation challenges. Simulating the interactions between stakeholders, ecosystems, and external stressors, these studies showcase ABM's utility in evaluating policy interventions and community responses. For instance, models examining sea-level rise or wildfire risk highlight the importance of understanding local socio-economic dynamics and environmental feedback [38; 42; 43]. However, some studies could benefit from incorporating cultural dimensions into stakeholder behaviour to enhance their relevance for diverse communities. The localised applications of ABM further demonstrate their capacity to evaluate adaptation strategies under specific climatic and socio-economic scenarios. For example, models integrating real estate markets with private adaptation decisions provide innovative approaches to understanding potential maladaptation [44]. Additionally, research on rural Ethiopian households reveals how climate extremes influence migration and resilience, offering critical insights for supporting vulnerable populations [37; 45; 46]. Despite their contributions, these studies often face challenges in scaling their findings to other contexts, as they rely heavily on localised data and narrowly defined scenarios. Several studies emphasise the integration of Indigenous and community knowledge systems within ABM, underscoring the importance of culturally sensitive approaches to adaptation. Participatory modelling frameworks have enabled Indigenous communities to explore adaptation scenarios while embedding traditional knowledge systems into decision-making processes [47; 48; 49]. These efforts demonstrate the value of co-creating models with local stakeholders to enhance the relevance and applicability of findings. However, such models often face difficulties in validating their results and scaling their methodologies beyond specific contexts, limiting their broader applicability. Policy and governance implications are central to many of the reviewed studies, which use ABM to examine decision-making frameworks for climate adaptation. For example, models exploring governance challenges in coastal communities highlight the disconnect between beneficial adaptation policies and existing regulatory structures [50; 51]. Other research demonstrates the potential of linking multiple models to enhance decision-making at different scales [52; 53]. While these studies underscore the role of ABM in evaluating governance options, they often lack actionable policy recommendations and robust mechanisms for engaging policymakers. Behavioural dynamics within adaptation processes are another focal point in the literature. Studies examining household decision-making and social networks highlight how adaptive behaviours evolve within communities. High social cohesion, while beneficial in promoting initial adaptation, can sometimes hinder flexibility when conditions change, leading to maladaptive outcomes [54]. Other research illustrates how adaptation strategies such as climate-smart agriculture improve food security for households with access to resources and strong social networks, though challenges persist for more marginalised groups [55; 56]. A smaller subset of studies focuses on integrating ABM with other modelling frameworks to broaden their applicability.