4  Conclusion

While this analysis provides a deep dive into the XIX Legislature, it is more of a structural overview. The current dataset allows us to measure volume and alignment, we can see the broad patterns, the shape of the coalitions, and how votes tend to cluster.

But to really understand the finer dynamics of the Italian parliament, we’d need to go deeper with the integration of more detailed and granular data.

Future directions to focus on are:

• Internal cohesion and defectors (Franchi Tiratori): political drama often happen within the same party. With access to individual roll-call votes (who voted for what), we could measure Party Discipline to identify which groups are most likely to internal conflicts and votes against their own coalition.
• Party switching(Cambio di Casacca): we assigned one party per deputy based on election results. We could track changes in the Deputies’ affiliation over time and reveal their political loyalty, and whether the old tradition of trasformismo (where politicians drift towards the one in power) still exists.
• Conflict Analysis: applying Natural Language Processing (NLP) to the text of amendments and parliamentary debates would allow us to see which topics spark the most conflict.
• Government vs. Parliament Efficiency: how many bills proposed by MPs actually become law compared to those initiated by the Government? Analyzing the “failure rate” of private member bills versus government decrees would tell us a lot about the real balance of power.

In conclusion, while the numbers confirm the stability and discipline of the current majority Party, the true health of a democracy is measured not only by the efficiency of its voting machine, but also by the quality of its debate and the independence of its representatives. Expanding this dataset is the first step toward that deeper understanding.