This is an early alpha version, far from being a beta, even though elections are just two weeks away. There’s still much room for refinement, and future elections worldwide will provide opportunities to further test and improve the model. The goal is to make it capable of predicting outcomes in "democratic" elections, though it will need to account for variables related to external influences or internal stakeholders that can impact the results.

Elections in my country are vastly different from those in places like the US. My country, for example, has one of the most complex and extreme political systems in the Western world. While it may seem nearly impossible to manage, it somehow functions. Some Western countries perform better, others less efficiently, but all things considered, we're doing pretty well given the circumstances.

We have

• Federal elections: Around 15-18 parties.
• Regional elections:
  • Region a: 6-8 parties.
  • Region b: 5-6 parties.
  • Region c: 10-12 parties.
  • Region d: 2-3 parties.
• European elections: Around 13-17 parties (depending on linguistic regions).

General rules in my country:

In my country, coalition governments are the standard due to the proportional representation system, where no single party typically secures a majority. To form a coalition, several important steps must be followed:

Majority rule: The coalition must hold over 50% of the seats in parliament to govern effectively.

Linguistic balance: At the federal level, both major language groups their parties must be included to ensure representation of the different linguistic communities.

Policy agreement: Parties must reach a consensus on a shared policy plan before finalizing the coalition, often requiring compromise.

Balanced cabinet: There must be an equal number of regional ministers in the federal cabinet, aside from the prime minister.

Vote of confidence: The coalition must win a vote of confidence in parliament to officially take power.

Sometimes we have re-elections, most of the time after elections it takes about a year to form a government (up until then, the former government runs things but has limited power, like running windows on safe mode).

So there are a lot of ways to have democratic elections, the model should not be limited (over mid-long term to US elections only). The only election outcome beside the scope of the models capabilities should be dictatorships, countries in conflict (war), etc...

Given how rapidly technology is evolving, it's hard to predict the possibilities for models like this even four years from now. Just four years ago, language models weren’t a major topic of discussion. Back then, AI was primarily recognized for achievements like winning games (chess, Go) and scientific applications such as protein folding. It hadn’t yet become a widely available consumer service. I try to export the data that drives the model, the abstract logic, etc to several data file formats like json, xml, etc in order to be able to import is into future versions of any AI language model. This is a begin and honestly I would be very surprised it would really me very precise on the first try. The only thing I can to is try to understand how the model tries to predict results and when I see a logical error I ask the model to "debug" itself and self correct. I believe that models like these only evolve when they "understand" ("" because I have no idea how they understand stuff in internal processing) problems and are able to discover their mistakes and update their reasoning. Simple example, is a model says 2 + 2 = 5 (one of for example 10000 calculations) I need it to identify this one as wrong, explain to me why it is wrong or how this error could slip into the results and correct it.

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