## 5 The Demonstrator
### MAS Framework and Implementation  
The MAS framework employed in this study is the MESA framework, a modular architecture written in Python. MESA enables the simulation of complex social systems by creating agents for each actor and designing their interactions within a specified environment. Figure 13 shows how each agent role described in Section 5.4 is modelled within the system using different types of agents, including reactive agents, and the mechanisms for implementing communication and coordination among them. The MESA framework encompasses a variety of tools and modules essential for constructing, executing, and analysing MAS models. These include built-in agent behaviours, scheduling mechanisms, and data collection primitives [21]. The MESA web application framework incorporates visualisation tools, such as 2D grid representations of agent instances, time-series graphs related to variables evolution, and tabular data displays while offering real-time interactivity. This interactivity allows users to control simulation parameters and flow (start, pause, and reset), thereby facilitating a thorough analysis and exploration of complex MAS as shown in Figure 12. On the left-hand side, the first two sliders set the probability of the approval of the General Contractor payment and technician asseveration success. The second group of sliders represent the percentage of funds distributed across the five SAL levels. The final slider sets the percentage allocated to the technicians (e.g., engineers, accountants, and construction managers). The central panel displays the grid representation of agent states. Table 3 summarises the agents involved in the two scenarios of the SFCM: the GC agent initially holds funds in their wallet and pays the technician agents for their assessments. Each **SAL** (i.e., Open, ANT, SAL1, SAL2, and EOW) is recorded by the paperwork agent, along with the entire Super  
Figure 12: On the left side, the demonstrator dashboard for setting the parameters is varied by moving each slider, thus modifying the success threshold of each scenario. To the right side is the agent’s status during the simulation. Figure 13: Multi-agent System architecture of the demonstrator illustrates the interaction between the DAOs and MAS to Superbonus 110% simulation. - Variations in local authority processing times;
- The temporary unavailability of key personnel;
- **Scenario 2. **_Low GC approval and low technician association success rates_**: This scenario simulates a less efficient approval process, reflecting potential bottlenecks stemming from contractor-side factors. We set a 25% approval probability per cycle but introduce delays to represent challenges such as the following:
- Contractor-induced rework due to quality issues;
- Delays in submitting required documentation by the contractor;
- Resource constraints on the contractor's end. Figure 14b illustrates the impact of low SAL approval rates. The workflow progresses more slowly, with pronounced delays at various stages. Consequently, the average time to reach EoW increases to 23.9 weeks. The visual representations and data derived from the SCFM provide valuable insights into the operational dynamics of Multi-agent Systems and the efficacy of various parameter configurations. Datasets generated from all simulations are publicly available on the Zenodo [22] platform, whilst the source code resides in the AAAI-DISIM-UnivAQ GitHub repository ([[https://github.com/AAAI-DISIM-UnivAQ/Secure_Fiscal_Credit_Model](https://github.com/AAAI-DISIM-UnivAQ/Secure_Fiscal_Credit_Model)]([https://github.com/AAAI-DISIM-UnivAQ/Secure_Fiscal_Credit_Model](https://github.com/AAAI-DISIM-UnivAQ/Secure_Fiscal_Credit_Model)), accessed on 10 November 2024).