# DAO Research Trends: Reflections and Learnings from the First European DAO Workshop (DAWO)
## 3 Developing Tools and Frameworks for DAOs  
The advancement of DAOs relies heavily on the development of tools and frameworks that facilitate effective governance, transparency, and scalability [19]. This Section presents several key areas of research featured at DAWO24 in this area and structures the different directions into a cohesive discussion, highlighting both the progress made and the questions that remain. Central to the success of DAOs is the development of democratic collaboration tools to enhance decision-making and document creation within DAOs, as highlighted by [PERSON] and [PERSON] [34], who explore methods like metric spaces, iterative voting, and coalition formation. These approaches are designed to improve the collaborative capabilities of DAOs, ensuring that diverse perspectives are effectively represented. As DAOs continue to grow, it is critical to identify the most effective collaboration tools that support transparency and inclusivity across large and diverse organizations, while balancing consensus-building with efficiency and scalability. Further, it is also important to explore new voting methods to increase consensus without causing delays or deadlocks. The technical infrastructure that supports the deployment of Ethereum-based decentralized applications (DApps) within DAOs also demands attention. For this, [PERSON] et al. [35] present an open-source framework designed to automate and accelerate the deployment of Ethereum-baseddecentralized applications (DApps), particularly for scenarios requiring customized test environments like DAOs. The framework leverages Docker and bash scripts to create networks of Ethereum and IPFS nodes, simplifying the deployment process to a single command, thereby addressing inefficiencies in current methods. The study concludes that this tool significantly reduces the complexity and time required to set up realistic test environments for DApps. Expanding the framework's adaptability to a broader range of use cases, applying it in resource-constrained environments, and further enhancing its scalability and integration with other decentralized technologies are important directions to pursue. Measuring autonomy within DAOs presents another significant challenge. [PERSON] and [PERSON] [36] have introduced the Autonomy Level Indicator (ALI), a standardized metric designed to provide a clear and consistent measure of autonomy. This advancement aims to help researchers and practitioners evaluate and compare different DAO designs. However, the complexity and inconsistent definitions of autonomy require further investigation into the most critical aspects to measure and how to quantify them effectively. Additionally, it is necessary to explore whether ALI can be standardized across the industry and to understand the impact of different levels of autonomy on governance outcomes. Integrating complexity science into DAO frameworks offers a promising approach to addressing inefficiencies and centralization issues. [PERSON] et al. [37] suggest that complexity science can provide new perspectives on decentralization and emergent properties, potentially leading to more effective and decentralized systems. Future research should explore how principles of collective intelligence and self-organization can be applied to DAO design, and critically analyze the potential benefits and limitations of novel mechanisms like futarchies and idea markets. Understanding how these principles affect DAO governance and comparing their effectiveness to traditional hierarchical models will be critical for advancing the field. Evaluating the distribution of control within DAOs is another crucial aspect of governance. [PERSON] et al. [38] propose a probabilistic framework that incorporates complexity and entropy analyses to refine decentralization metrics. This approach aims to offer a more precise measure of decentralization, but it requires further refinement and validation, particularly for large-scale DAOs.