## 5 Discussion
The bridging of the digital divide hinges significantly on addressing regulatory barriers, economic incentives, and public policy variations. These elements are critical because they shape the model's capacity to account for factors that impact digital inclusion effectively. While predictive models can identify correlations between broadband access, infrastructure availability, and digital inclusion, regulatory barriers often introduce complexities that such models may not fully capture. For instance, restrictive regulatory frameworks may limit the deployment of telecommunications infrastructure in underserved areas, stalling efforts to expand broadband access even where technical capabilities exist. This creates a lag in digital inclusion that goes beyond what the model predicts, necessitating more nuanced inputs related to regulatory impact. Economic incentives, or the lack thereof, also play a pivotal role in digital access disparities. Areas with low profitability for service providers, often rural or low-income regions, may see limited infrastructure investment despite high demand for connectivity. The model may indicate a potential market for broadband in such areas, yet without robust economic incentives, providers may hesitate to commit resources. This introduces a gap between the model's projections and real-world outcomes, highlighting the need for policies that encourage investment in less lucrative regions through subsidies or tax breaks. Public policy variations across regions lead to inconsistencies in digital inclusion outcomes. Some regions may have policies aimed at incentivizing broadband deployment and digital literacy, while others may lack such initiatives. This variability means that a single predictive model, without accounting for these policy differences, may overlook critical influences on digital inclusion. Thus, while the model is a powerful tool for identifying broad patterns, its capacity for policy innovation could be constrained unless it incorporates regulatory, economic, and policy factors comprehensively. The current predictive models' exclusion of temporal dynamics in technology adoption and evolving infrastructure, like the transition from DSL (Digital Subscriber Line) to fiber optic networks, limits their effectiveness in providing comprehensive insights for future planning. As technology adoption follows patterns influenced by economic factors, regulatory changes, and consumer preferences, ignoring the temporal dimension creates a static model that may fail to reflect real-world shifts in digital access over time. The transition from DSL to fiber optic networks, for example, represents more than a mere technological upgrade--it reshapes digital access capabilities due to fiber optics' higher bandwidth and reliability. This shift is gradual and regionally variable, meaning that areas adopting fiber optic early experience different economic and social impacts compared to those still dependent on DSL or other older technologies. Without integrating these temporal aspects, models overlook how such transitions affect regional digital inclusion differently over time. Additionally, technology adoption is not instantaneous; it often follows an S-curve, where adoption accelerates once a critical mass of users is reached and may plateau in late stages. Failure to account for this progression could lead to models that underestimate the pace of adoption in emerging technologies or overestimate infrastructure demand for outdated technologies. Consequently, policymakers relying on these models may misallocate resources, investing in infrastructure that fails to align with the actual technology trajectory. Therefore, by incorporating temporal dynamics, these predictive models could better anticipate future needs, enabling more precise policy decisions and infrastructure investments that align with evolving technology landscapes. The influence of digital inclusion in Mexico is profound, spanning economic, educational, and social domains.